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pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json create mode 100644 pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json create mode 100644 pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json diff --git a/pydata-virginia-2025/category.json b/pydata-virginia-2025/category.json new file mode 100644 index 000000000..e20285b00 --- /dev/null +++ b/pydata-virginia-2025/category.json @@ -0,0 +1,3 @@ +{ + "title": "PyData Virginia 2025" +} diff --git a/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json b/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json new file mode 100644 index 000000000..50547d8ec --- /dev/null +++ b/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nSciPy is a powerful library for scientific and technical computing in Python. The primary objectives of this presentation are to explore the core concepts of Responsible AI and to demonstrate these concepts with SciPy.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 3152, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/W6fTFSgyhMg/maxresdefault.jpg", + "title": "Andrea Hobby - Responsible AI with SciPy | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=W6fTFSgyhMg" + } + ] +} diff --git a/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json b/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json new file mode 100644 index 000000000..09352fb39 --- /dev/null +++ b/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nMulti-armed bandits are a reinforcement learning tool often used in environments where the cost or rewards of different choices are unknown or where those functions may change over time. The good news is that as far as implementation goes, bandits are surprisingly easy to implement; however, in practice, the difficulty comes from defining a reward function that best targets your specific use case. In this talk, we will discuss how to use bandit algorithms effectively, taking note of practical strategies for experimental design and deployment of bandits in your applications.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1819, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/jP978VKBl-w/maxresdefault.jpg", + "title": "Benjamin Bengfort - Practical Multi Armed Bandits | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=jP978VKBl-w" + } + ] +} diff --git a/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json b/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json new file mode 100644 index 000000000..13d229f24 --- /dev/null +++ b/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nWhen Bayesian modeling scales up to large datasets, traditional MCMC methods can become impractical due to their computational demands. Variational Inference (VI) offers a scalable alternative, trading exactness for speed while retaining the essence of Bayesian inference.\n\nIn this tutorial, we\u2019ll explore how to implement and compare VI techniques in PyMC, including the Adaptive Divergence Variational Inference (ADVI) and the cutting-edge Pathfinder algorithm.\n\nStarting with simple models like linear regression, we\u2019ll gradually introduce more complex, real-world applications, comparing the performance of VI against Markov Chain Monte Carlo (MCMC) to understand the trade-offs in speed and accuracy.\n\nThis tutorial will arm participants with practical tools to deploy VI in their workflows and help answer pressing questions, like \"What do I do when MCMC is too slow?\", or \"How does VI compare to MCMC in terms of approximation quality?\".\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 5357, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/XECLmgnS6Ng/maxresdefault.jpg", + "title": "Chris Fonnesbeck - A Beginner's Guide to Variational Inference | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=XECLmgnS6Ng" + } + ] +} diff --git a/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json b/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json new file mode 100644 index 000000000..8d69d9ae7 --- /dev/null +++ b/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nGeospatial data can unlock valuable insights. OpenStreetMap includes electric power and telecommunication infrastructure geospatial data, and it is already \u201copen\u201d. This presentation will demonstrate how to use Python to \u201cunlock the insights\u201d available in OSM power and telecommunications geospatial data.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1522, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/kmRyFmMThVo/maxresdefault.jpg", + "title": "Cory Eicher- Using Python to Unlock Insights from OpenStreetMap Data at Scale | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=kmRyFmMThVo" + } + ] +} diff --git a/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json b/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json new file mode 100644 index 000000000..bc0c6da9d --- /dev/null +++ b/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json @@ -0,0 +1,51 @@ +{ + "description": "www.pydata.org\n\nTired of waiting for massive datasets to load on your local machine? In this beginner-friendly tutorial, we\u2019ll explore how to scale your data analysis skills from pandas to PySpark using a real-world anime dataset. We\u2019ll walk through the basics of distributed computing, discuss why Spark was created, and demonstrate the benefits of working with PySpark for big data tasks\u2014including reading, cleaning, and transforming millions of records with ease. By the end of this workshop, you\u2019ll understand how PySpark harnesses cluster computing to handle large-scale data and you\u2019ll be comfortable applying these techniques to your own projects.\n\nParticipant Requirements:\n- A laptop (any OS) with an internet connection\n- A Google account (to access Colab notebooks and slides)\n- Familiarity with Python and pandas\n\nHere's the link to the Google Colab to follow along \ud83d\udc47\ud83c\udffe\nhttps://colab.research.google.com/drive/1fi0cTQ1NIE5kDEH0ynp2sqDuVeiBJJWU?usp=sharing\n\nHere are the slides \ud83d\udc47\ud83c\udffe\nhttps://drive.google.com/file/d/11JIih1VzLxTJ9O6PeGzqD_e8vumTZQmw/view?usp=sharing\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 4399, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://colab.research.google.com/drive/1fi0cTQ1NIE5kDEH0ynp2sqDuVeiBJJWU?usp=sharing", + "url": "https://colab.research.google.com/drive/1fi0cTQ1NIE5kDEH0ynp2sqDuVeiBJJWU?usp=sharing" + }, + { + "label": "https://drive.google.com/file/d/11JIih1VzLxTJ9O6PeGzqD_e8vumTZQmw/view?usp=sharing", + "url": "https://drive.google.com/file/d/11JIih1VzLxTJ9O6PeGzqD_e8vumTZQmw/view?usp=sharing" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/McbJMdcKp5c/maxresdefault.jpg", + "title": "Cynthia Ukawu - From Pandas to PySpark | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=McbJMdcKp5c" + } + ] +} diff --git a/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json new file mode 100644 index 000000000..41265ee74 --- /dev/null +++ b/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nThe team behind DVC has spent years tackling data versioning challenges. With the rise of AI, we\u2019ve seen new complexities emerge - especially with multimodal datasets like images, video, audio, and text. This talk shows why multimodal data versioning is different and how Pydantic provides a powerful way to structure and integrate metadata.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1659, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/yNBoJSKl49U/maxresdefault.jpg", + "title": "Dmitry Petrov - Versioning Multimodal Data: Metadata & Beyond | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=yNBoJSKl49U" + } + ] +} diff --git a/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json new file mode 100644 index 000000000..e3c19ef5e --- /dev/null +++ b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nHealth disparities remain a critical challenge in public health, demanding innovative approaches to uncover inequities and drive actionable change. This webinar will demonstrate how Python can serve as a powerful tool for creating data visualizations that illustrate the unequal burden of HIV across different populations. Participants will learn how Python\u2019s popular libraries, such as Matplotlib, Seaborn, and Plotly, can transform complex datasets into accessible, impactful visuals.\nUsing an HIV dataset containing demographic, geographic, and clinical variables, this session will guide attendees through a series of practical examples. From creating heatmaps and geospatial maps to analyzing temporal trends, the webinar emphasizes how to identify and communicate key social determinants related to race, gender, socioeconomic status, and access to care. Through hands-on demonstrations, attendees will see how Python\u2019s capabilities streamline data analysis and visualization workflows.\nKey takeaways from the session include identifying regions and communities in Texas, disproportionately affected by HIV, uncovering intersectional factors influencing health outcomes, and leveraging visual tools to inform policy and resource allocation. Special attention will be given to designing visuals that resonate with non-technical audiences, ensuring findings are actionable for public health professionals and policymakers.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 4007, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/-BA2eXBoDoc/maxresdefault.jpg", + "title": "Dr. Kimberly Deas-Data Viz in Pyton as a Tool to Study HIV Health Disparities | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=-BA2eXBoDoc" + } + ] +} diff --git a/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json new file mode 100644 index 000000000..b16577432 --- /dev/null +++ b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nWhere do landlords engage in more eviction actions? What characteristics of renters or landlords increase the practice of serial filing? There is widespread interest in using administrative data -- information collected by government and agencies in the implementation of public programs -- to evaluate systems and promote most just outcomes. Working with the Civil Court Data Initiative of Legal Services Corporation, we use data collected from civil court records in Virginia to analyze the behavior of landlords. Expanding on our Virginia Evictors Catalog, we use data on court evictions to build additional data tools to support the work of legal and housing advocates and model key eviction outcomes to contribute to our understanding of landlord behavior.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1755, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/eE0D79trL2c/maxresdefault.jpg", + "title": "Dr. Michele Claibourn & Samantha Toet - Exploring Eviction Trends in Virginia | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=eE0D79trL2c" + } + ] +} diff --git a/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json b/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json new file mode 100644 index 000000000..811eeb5aa --- /dev/null +++ b/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nWhen every day counted during the COVID-19 pandemic, data science became an essential catalyst in accelerating the path to widespread vaccination. This talk delves into the data-driven strategies that enabled the U.S. government\u2019s vaccine trials to move faster, cutting crucial weeks\u20146 to 8, by our estimates\u2014off the timeline to deployment. Through sophisticated geospatial modeling, we identified and swiftly mobilized trial recruitment efforts in emerging hot zones, ensuring that each candidate pool was both numerically sufficient and demographically representative. Attendees will discover how advanced analytics, predictive modeling, and interdisciplinary collaboration converged to target the right communities at the right time, ultimately expediting vaccine availability. This behind-the-scenes look at rapid-response data science highlights not just the technical innovations, but the decisive cultural and operational shifts that turned real-time insights into life-saving action.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1733, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/zXKdjBv1SGc/maxresdefault.jpg", + "title": "Greg Michaelson- How data science shortened the COVID-19 pandemic by 2 months | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=zXKdjBv1SGc" + } + ] +} diff --git a/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json b/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json new file mode 100644 index 000000000..6e080ff5c --- /dev/null +++ b/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nThis workshop will provide a comprehensive introduction to Large Language Models (LLMs), covering their capabilities, structure, and practical applications. Participants will learn prompt engineering techniques, retrieval-augmented generation (RAG), agentic AI design, fine-tuning strategies, and model evaluation methods. The session will conclude with a discussion on the future of AI-powered reasoning machines.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 5940, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/jmwLzX_ltbQ/maxresdefault.jpg", + "title": "John Berryman - Mastering LLMs: From Prompt Engineering to Agentic AI | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=jmwLzX_ltbQ" + } + ] +} diff --git a/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json b/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json new file mode 100644 index 000000000..7ed516ea9 --- /dev/null +++ b/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nAs businesses strive to become AI-first, the pivotal role of AI practitioners extends beyond technical implementation to encompass strategic stewardship. This transition necessitates a profound understanding of organizational goals, data governance, and ethical considerations. By aligning AI initiatives with business objectives, fostering cross-functional collaboration, and addressing challenges such as data privacy and employee adaptation, AI professionals can drive effective transformation. This keynote explores the essential competencies and approaches required for AI practitioners to lead their organizations successfully into an AI-centric future.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 3481, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/jOgUY9Rcd80/maxresdefault.jpg", + "title": "KEYNOTE: Rajkumar Venkatesan- Building AI-First Organizations & Opening Notes | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=jOgUY9Rcd80" + } + ] +} diff --git a/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json b/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json new file mode 100644 index 000000000..d9b1e288e --- /dev/null +++ b/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nTraditional PDF extraction tools often struggle with complex layouts, tables, and images, Docling (an opensource Python library developed at IBM) excels at extracting structured information from these elements, enabling the creation of richer, more accurate vector databases. This hands-on tutorial will guide participants through building a Retrieval Augmented Generation (RAG) system using Docling, an open-source document processing library.\n\nParticipants will learn how to harness Docling's advanced capabilities to build superior RAG systems that can understand and retrieve information from complex document elements that traditional tools might miss. Participants will learn how to handle complex documents, extract structured information, and create an efficient vector database for semantic search. The session will cover best practices for document parsing, chunking strategies, and integration with popular LLM frameworks.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 5414, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/41pxp4-pRmI/maxresdefault.jpg", + "title": "Krishna Rekapalli - Unlock Information from Tables, Images and Complex Docs | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=41pxp4-pRmI" + } + ] +} diff --git a/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json new file mode 100644 index 000000000..6b1a503c4 --- /dev/null +++ b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nWhen \u201cAI Agent\u201d became the buzz word, have you ever wondered: what exactly is an AI agent? What is the multi-agent system? And how can you use the power of AI agents in your day-to-day data science workflow? In this hands-on tutorial, I will introduce AI agents and demonstrate how to design, build, and manage a multi-agent system for your data science workflows. Participants will learn how to break down complex tasks, assign AI agents to collaborate effectively, and ensure accuracy and reliability in their outputs. We will also discuss the trade-offs, limitations, and best practices for incorporating AI agents into data science projects.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 5337, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/s5dx_4y6Iy8/maxresdefault.jpg", + "title": "Krishnan, Liu, Puri, & Rojas - Build Your Own Data Science AI Agents | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=s5dx_4y6Iy8" + } + ] +} diff --git a/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json b/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json new file mode 100644 index 000000000..d02fb5ff6 --- /dev/null +++ b/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nDiscover how S&P Global built an enterprise-grade evaluation framework that transformed our GenAI deployment process. Through automated monitoring, expert validation, & continuous testing, we\u2019ve streamlined the document integration step of our RAG tools, while ensuring our AI tools maintain consistent quality and reliability.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1799, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/DJr1FSDCpGo/maxresdefault.jpg", + "title": "MacKenzye Leroy - Building a Robust Evaluation Framework for GenAI Productivity Tools", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=DJr1FSDCpGo" + } + ] +} diff --git a/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json b/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json new file mode 100644 index 000000000..58ac8edd5 --- /dev/null +++ b/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nTutorial on building an image segmentation and classification pipeline for binary or multiclass classification using the popular packages scikit-learn, scikit-image and PyTorch\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 4500, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/xLxBCPmNDaI/maxresdefault.jpg", + "title": "Matt Litz - Tutorial on Image Classification using Scikit-Image, Scikit-learn, and PyTorch", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=xLxBCPmNDaI" + } + ] +} diff --git a/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json b/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json new file mode 100644 index 000000000..50230ce1c --- /dev/null +++ b/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nIn the mining industry's pursuit of zero harm, distinguishing real safety improvements from random variation is crucial yet challenging. This talk demonstrates how classical changepoint analysis and Bayesian methods provide safety teams at Asarco LLC with rigorous tools to objectively evaluate progress towards our zero-harm goal. Using near miss reporting and lost time metrics, we will show how these statistical approaches help identify meaningful trends while avoiding misleading conclusions from natural variation. While the focus is on mining, these methods are applicable to other safety-critical and data-limited scenarios. No prior experience with changepoint analysis is required.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1672, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/B0eGMlYQkiw/maxresdefault.jpg", + "title": "Mauricio Mathey - Using Changepoint and Bayesian Analysis to Drive Safety Improvements in Mining", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=B0eGMlYQkiw" + } + ] +} diff --git a/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json b/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json new file mode 100644 index 000000000..69ac48f30 --- /dev/null +++ b/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nText-to-Image models, like CLIP, have brought us into a new frontier of visual search. Whether it's searching by circling a section of a photo or powering image generators like Dalle-E the gap between pixels and tokens has never been smaller. This talk discusses how we are improving search and empowering designers with these models at Eezy, a stock art marketplace.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1549, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/Xzd25tbKFLA/maxresdefault.jpg", + "title": "Nathan Day - Maximizing Multimodal: Exploring the search frontier of text-to-image models", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Xzd25tbKFLA" + } + ] +} diff --git a/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json b/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json new file mode 100644 index 000000000..e54f53108 --- /dev/null +++ b/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json @@ -0,0 +1,51 @@ +{ + "description": "www.pydata.org\n\nIn this introductory hands-on tutorial, participants will learn how to accelerate their data workflows with RAPIDS (https://rapids.ai/), an open-source suite of libraries designed to leverage the power of NVIDIA (https://www.nvidia.com/) GPUs for end-to-end data pipelines. Using familiar PyData APIs like cuDF (GPU-accelerated pandas) and cuML (GPU-accelerated machine learning), attendees will explore how to seamlessly integrate these tools into their existing workflows with minimal code changes, achieving significant speedups in tasks such as data processing and model training.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 5729, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://rapids.ai/", + "url": "https://rapids.ai/" + }, + { + "label": "https://www.nvidia.com/", + "url": "https://www.nvidia.com/" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/IJ8rjVD4-yE/maxresdefault.jpg", + "title": "Naty Clementi & Mike McCarty - RAPIDS: GPU-Accelerated Data Science for PyData Users", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=IJ8rjVD4-yE" + } + ] +} diff --git a/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json b/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json new file mode 100644 index 000000000..b8ec2fe4f --- /dev/null +++ b/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nGraphs are a fundamental form of storing data. This is because everything is connected! Hence, Graphs are very useful for modeling and solving a wide variety of real-world problems.\n\nWhile NetworkX is amazing for getting started with Graphs, the library encounters bottlenecks in performance at scale.\n\nIs there a solution out there for users who want more performance from NX and also Open-Source developers who want to implement fast algorithms? Yes! Thanks to the magic of dispatching.\n\nNetworkX now supports dispatching to various backends, including the GPU accelerated cuGraph library by Nvidia RAPIDS.\n\nAttend this talk to learn about how you can use nx-cugraph \u2013 the cuGraph-powered backend for NetworkX \u2013 and how it unlocks exciting new possibilities for you to solve real-world graph analytics problems.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1916, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/TUT4GLDo2pU/maxresdefault.jpg", + "title": "Ralph Liu - Zero Code Change GPU-Powered Graph Analytics with NetworkX and cuGraph", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=TUT4GLDo2pU" + } + ] +} diff --git a/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json b/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json new file mode 100644 index 000000000..410e088eb --- /dev/null +++ b/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json @@ -0,0 +1,47 @@ +{ + "description": "www.pydata.org\n\nAgents have become one of the most talked-about topics in the AI community, but much of the discussion focuses on its potential impact rather than practical implementation. This hands-on workshop will guide data scientists and engineers through building a complete workflow using LangGraph, and will show how to define custom tools, implement vector retrieval, leverage semantic caching, incorporate allow/block list routing, and structure model output for downstream consumption. In order to participate, attendees will need to have Python (3.11 or later), docker, an OpenAI api key, and the starter code for the project cloned.\n\nStarter code: https://github.com/redis-developer/agents-redis-lang-graph-workshop\n\nNote: participants can test their environment setup ahead of time by following the Readme and running python test_setup.py before the workshop.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 5195, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + }, + { + "label": "https://github.com/redis-developer/agents-redis-lang-graph-workshop", + "url": "https://github.com/redis-developer/agents-redis-lang-graph-workshop" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/kCpZ22_XluM/maxresdefault.jpg", + "title": "Robert Shelton - Blazing the AI Trail: Using LangGraph to Conquer the Oregon Trail", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=kCpZ22_XluM" + } + ] +} diff --git a/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json b/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json new file mode 100644 index 000000000..088029d46 --- /dev/null +++ b/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nWe will review Wikipedia, introduce Wikidata, then demonstrate queries to access wiki content\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 4404, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/0eGNxqvW89M/maxresdefault.jpg", + "title": "Robin Isadora Brown & Lane Rasberry - Introduction to Wikidata | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=0eGNxqvW89M" + } + ] +} diff --git a/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json b/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json new file mode 100644 index 000000000..b495f155f --- /dev/null +++ b/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nIn the era of big data, multi-modal data from multiple sources or modalities has become increasingly prevalent in various fields such as healthcare. The National COVID Cohort Collaborative (N3C) provides researchers with abundant clinical data in different forms by aggregating and harmonizing Electronic Health Records (EHR) data across different clinical organizations in the United States, making it convenient for researchers to analyze COVID-related topics and build models with large multimodal data. Bayesian risk analysis has advantages in handling the complexities and heterogeneities of multi-modal healthcare data, specifically in cohort studies when researchers try to answer questions of interest in public health or medicine field regarding COVID and Long COVID.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1878, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/Ul7ndUBtx2Y/maxresdefault.jpg", + "title": "Sihang Jiang - Bayesian Risk Analysis For Large Multi-Modal Data | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Ul7ndUBtx2Y" + } + ] +} diff --git a/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json b/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json new file mode 100644 index 000000000..8915ec611 --- /dev/null +++ b/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nIn our project, we took MRI-derived brain data and reinterpreted it through an aesthetic lens. Using multidimensional scaling (MDS) to distill complex patterns in cortical anatomy, we transformed these insights into physical 3D-printed brain models. Each sculpture serves as a tangible narrative, celebrating both the subtle and striking differences between male and female brains, whether neurotypical or affected by ASD.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1592, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/4riqJcrDVcw/maxresdefault.jpg", + "title": "Siwen Liao - Celebrating Neurodiversity Through Aesthetic Data Visualization | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=4riqJcrDVcw" + } + ] +} diff --git a/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json b/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json new file mode 100644 index 000000000..d52e2ed16 --- /dev/null +++ b/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 1841, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/dwILgDsbmto/maxresdefault.jpg", + "title": "Suhas Pai - Making the most of test-time compute in LLMs", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=dwILgDsbmto" + } + ] +} diff --git a/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json b/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json new file mode 100644 index 000000000..89a2e3152 --- /dev/null +++ b/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nLearn how to wrangle data in Python with DuckDB, a fast, open source, in-process analytical SQL database!\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2066, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/PMEHdnrYyaE/maxresdefault.jpg", + "title": "Will Angel - Data Wrangling with DuckDB | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=PMEHdnrYyaE" + } + ] +} diff --git a/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json b/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json new file mode 100644 index 000000000..cc3aa363b --- /dev/null +++ b/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json @@ -0,0 +1,43 @@ +{ + "description": "www.pydata.org\n\nData system interoperability remains a significant challenge in open source ecosystems, with high costs in development time and resources when moving data across complex infrastructures. The Apache Arrow project offers a standardized solution to reduce these integration challenges.\n\nWill Ayd (Apache Arrow Committer and pandas maintainer) and Matt Topol (Apache Arrow PMC Member and author of \"In Memory Analytics with Apache Arrow\") will discuss how Apache Arrow is changing the data landscape. A brief overview of Arrow standards will be provided, while also reviewing real world implementations of where the Arrow specification has driven down the cost of data interoperability.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "duration": 2099, + "language": "eng", + "recorded": "2025-04-18", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/virginia2025" + }, + { + "label": "https://github.com/numfocus/YouTubeVideoTimestamps", + "url": "https://github.com/numfocus/YouTubeVideoTimestamps" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/G4eXX_-S5nM/maxresdefault.jpg", + "title": "Will Ayd & Matt Topol - Practical Applications of Apache Arrow | PyData Virginia 2025", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=G4eXX_-S5nM" + } + ] +} From e70c7d09840e41d4db39f2a262fd75ebea171f47 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ezequiel=20Leonardo=20Casta=C3=B1o?= <14986783+ELC@users.noreply.github.com> Date: Sat, 28 Jun 2025 14:52:05 -0300 Subject: [PATCH 2/4] Remove tags --- ...ible-ai-with-scipy-pydata-virginia-2025.json | 17 +---------------- ...ulti-armed-bandits-pydata-virginia-2025.json | 17 +---------------- ...iational-inference-pydata-virginia-2025.json | 17 +---------------- ...tmap-data-at-scale-pydata-virginia-2025.json | 17 +---------------- ...-pandas-to-pyspark-pydata-virginia-2025.json | 17 +---------------- ...ta-metadata-beyond-pydata-virginia-2025.json | 17 +---------------- ...health-disparities-pydata-virginia-2025.json | 17 +---------------- ...trends-in-virginia-pydata-virginia-2025.json | 17 +---------------- ...ndemic-by-2-months-pydata-virginia-2025.json | 17 +---------------- ...ring-to-agentic-ai-pydata-virginia-2025.json | 17 +---------------- ...ions-opening-notes-pydata-virginia-2025.json | 17 +---------------- ...s-and-complex-docs-pydata-virginia-2025.json | 17 +---------------- ...-science-ai-agents-pydata-virginia-2025.json | 17 +---------------- ...-framework-for-genai-productivity-tools.json | 17 +---------------- ...g-scikit-image-scikit-learn-and-pytorch.json | 17 +---------------- ...-to-drive-safety-improvements-in-mining.json | 17 +---------------- ...search-frontier-of-text-to-image-models.json | 17 +---------------- ...celerated-data-science-for-pydata-users.json | 17 +---------------- ...aph-analytics-with-networkx-and-cugraph.json | 17 +---------------- ...g-langgraph-to-conquer-the-oregon-trail.json | 17 +---------------- ...uction-to-wikidata-pydata-virginia-2025.json | 17 +---------------- ...e-multi-modal-data-pydata-virginia-2025.json | 17 +---------------- ...data-visualization-pydata-virginia-2025.json | 17 +---------------- ...g-the-most-of-test-time-compute-in-llms.json | 17 +---------------- ...ngling-with-duckdb-pydata-virginia-2025.json | 17 +---------------- ...ns-of-apache-arrow-pydata-virginia-2025.json | 17 +---------------- 26 files changed, 26 insertions(+), 416 deletions(-) diff --git a/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json b/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json index 50547d8ec..46bd46a3f 100644 --- a/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/W6fTFSgyhMg/maxresdefault.jpg", "title": "Andrea Hobby - Responsible AI with SciPy | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json b/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json index 09352fb39..5450deb2f 100644 --- a/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/jP978VKBl-w/maxresdefault.jpg", "title": "Benjamin Bengfort - Practical Multi Armed Bandits | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json b/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json index 13d229f24..14fafa13a 100644 --- a/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/XECLmgnS6Ng/maxresdefault.jpg", "title": "Chris Fonnesbeck - A Beginner's Guide to Variational Inference | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json b/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json index 8d69d9ae7..d5f79372f 100644 --- a/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/kmRyFmMThVo/maxresdefault.jpg", "title": "Cory Eicher- Using Python to Unlock Insights from OpenStreetMap Data at Scale | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json b/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json index bc0c6da9d..8953197f8 100644 --- a/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json @@ -24,22 +24,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/McbJMdcKp5c/maxresdefault.jpg", "title": "Cynthia Ukawu - From Pandas to PySpark | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json index 41265ee74..0c8494b1a 100644 --- a/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/yNBoJSKl49U/maxresdefault.jpg", "title": "Dmitry Petrov - Versioning Multimodal Data: Metadata & Beyond | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json index e3c19ef5e..6a0902ee4 100644 --- a/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/-BA2eXBoDoc/maxresdefault.jpg", "title": "Dr. Kimberly Deas-Data Viz in Pyton as a Tool to Study HIV Health Disparities | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json index b16577432..db4e054a8 100644 --- a/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/eE0D79trL2c/maxresdefault.jpg", "title": "Dr. Michele Claibourn & Samantha Toet - Exploring Eviction Trends in Virginia | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json b/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json index 811eeb5aa..5a287085e 100644 --- a/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/zXKdjBv1SGc/maxresdefault.jpg", "title": "Greg Michaelson- How data science shortened the COVID-19 pandemic by 2 months | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json b/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json index 6e080ff5c..037ec8ffd 100644 --- a/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/jmwLzX_ltbQ/maxresdefault.jpg", "title": "John Berryman - Mastering LLMs: From Prompt Engineering to Agentic AI | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json b/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json index 7ed516ea9..77d1d278f 100644 --- a/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/jOgUY9Rcd80/maxresdefault.jpg", "title": "KEYNOTE: Rajkumar Venkatesan- Building AI-First Organizations & Opening Notes | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json b/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json index d9b1e288e..0637155f7 100644 --- a/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/41pxp4-pRmI/maxresdefault.jpg", "title": "Krishna Rekapalli - Unlock Information from Tables, Images and Complex Docs | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json index 6b1a503c4..c5f87747a 100644 --- a/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/s5dx_4y6Iy8/maxresdefault.jpg", "title": "Krishnan, Liu, Puri, & Rojas - Build Your Own Data Science AI Agents | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json b/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json index d02fb5ff6..fc7df7211 100644 --- a/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json +++ b/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/DJr1FSDCpGo/maxresdefault.jpg", "title": "MacKenzye Leroy - Building a Robust Evaluation Framework for GenAI Productivity Tools", "videos": [ diff --git a/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json b/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json index 58ac8edd5..1e9025f28 100644 --- a/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json +++ b/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/xLxBCPmNDaI/maxresdefault.jpg", "title": "Matt Litz - Tutorial on Image Classification using Scikit-Image, Scikit-learn, and PyTorch", "videos": [ diff --git a/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json b/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json index 50230ce1c..e4c427a4d 100644 --- a/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json +++ b/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/B0eGMlYQkiw/maxresdefault.jpg", "title": "Mauricio Mathey - Using Changepoint and Bayesian Analysis to Drive Safety Improvements in Mining", "videos": [ diff --git a/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json b/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json index 69ac48f30..0d65ee055 100644 --- a/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json +++ b/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/Xzd25tbKFLA/maxresdefault.jpg", "title": "Nathan Day - Maximizing Multimodal: Exploring the search frontier of text-to-image models", "videos": [ diff --git a/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json b/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json index e54f53108..509ba1b5d 100644 --- a/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json +++ b/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json @@ -24,22 +24,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/IJ8rjVD4-yE/maxresdefault.jpg", "title": "Naty Clementi & Mike McCarty - RAPIDS: GPU-Accelerated Data Science for PyData Users", "videos": [ diff --git a/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json b/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json index b8ec2fe4f..61d9f8706 100644 --- a/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json +++ b/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/TUT4GLDo2pU/maxresdefault.jpg", "title": "Ralph Liu - Zero Code Change GPU-Powered Graph Analytics with NetworkX and cuGraph", "videos": [ diff --git a/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json b/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json index 410e088eb..38c1848aa 100644 --- a/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json +++ b/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json @@ -20,22 +20,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/kCpZ22_XluM/maxresdefault.jpg", "title": "Robert Shelton - Blazing the AI Trail: Using LangGraph to Conquer the Oregon Trail", "videos": [ diff --git a/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json b/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json index 088029d46..0593be384 100644 --- a/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/0eGNxqvW89M/maxresdefault.jpg", "title": "Robin Isadora Brown & Lane Rasberry - Introduction to Wikidata | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json b/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json index b495f155f..9a266ab48 100644 --- a/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/Ul7ndUBtx2Y/maxresdefault.jpg", "title": "Sihang Jiang - Bayesian Risk Analysis For Large Multi-Modal Data | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json b/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json index 8915ec611..9271d490e 100644 --- a/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/4riqJcrDVcw/maxresdefault.jpg", "title": "Siwen Liao - Celebrating Neurodiversity Through Aesthetic Data Visualization | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json b/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json index d52e2ed16..44682a3f4 100644 --- a/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json +++ b/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/dwILgDsbmto/maxresdefault.jpg", "title": "Suhas Pai - Making the most of test-time compute in LLMs", "videos": [ diff --git a/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json b/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json index 89a2e3152..f85c6a3d8 100644 --- a/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/PMEHdnrYyaE/maxresdefault.jpg", "title": "Will Angel - Data Wrangling with DuckDB | PyData Virginia 2025", "videos": [ diff --git a/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json b/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json index cc3aa363b..d4f357d24 100644 --- a/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json @@ -16,22 +16,7 @@ "speakers": [ "TODO" ], - "tags": [ - "Education", - "Julia", - "NumFOCUS", - "Opensource", - "PyData", - "Python", - "Tutorial", - "coding", - "how to program", - "learn", - "learn to code", - "python 3", - "scientific programming", - "software" - ], + "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/G4eXX_-S5nM/maxresdefault.jpg", "title": "Will Ayd & Matt Topol - Practical Applications of Apache Arrow | PyData Virginia 2025", "videos": [ From c5059982d36c504f258888709d744ed097ef6772 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ezequiel=20Leonardo=20Casta=C3=B1o?= <14986783+ELC@users.noreply.github.com> Date: Mon, 24 Nov 2025 00:21:04 -0300 Subject: [PATCH 3/4] Update video metadata for PyData Virginia 2025: streamlined descriptions and simplified titles for clarity. This includes removing unnecessary details and enhancing the focus on key topics for each presentation. --- ...y-responsible-ai-with-scipy-pydata-virginia-2025.json | 6 +++--- ...actical-multi-armed-bandits-pydata-virginia-2025.json | 6 +++--- ...de-to-variational-inference-pydata-virginia-2025.json | 6 +++--- ...openstreetmap-data-at-scale-pydata-virginia-2025.json | 6 +++--- ...kawu-from-pandas-to-pyspark-pydata-virginia-2025.json | 6 +++--- ...imodal-data-metadata-beyond-pydata-virginia-2025.json | 6 +++--- ...tudy-hiv-health-disparities-pydata-virginia-2025.json | 6 +++--- ...eviction-trends-in-virginia-pydata-virginia-2025.json | 7 ++++--- ...vid-19-pandemic-by-2-months-pydata-virginia-2025.json | 6 +++--- ...t-engineering-to-agentic-ai-pydata-virginia-2025.json | 6 +++--- ...organizations-opening-notes-pydata-virginia-2025.json | 8 ++++---- ...les-images-and-complex-docs-pydata-virginia-2025.json | 6 +++--- ...-own-data-science-ai-agents-pydata-virginia-2025.json | 9 ++++++--- ...valuation-framework-for-genai-productivity-tools.json | 6 +++--- ...tion-using-scikit-image-scikit-learn-and-pytorch.json | 6 +++--- ...-analysis-to-drive-safety-improvements-in-mining.json | 6 +++--- ...ring-the-search-frontier-of-text-to-image-models.json | 6 +++--- ...ds-gpu-accelerated-data-science-for-pydata-users.json | 7 ++++--- ...owered-graph-analytics-with-networkx-and-cugraph.json | 6 +++--- ...rail-using-langgraph-to-conquer-the-oregon-trail.json | 6 +++--- ...ry-introduction-to-wikidata-pydata-virginia-2025.json | 7 ++++--- ...-for-large-multi-modal-data-pydata-virginia-2025.json | 6 +++--- ...esthetic-data-visualization-pydata-virginia-2025.json | 6 +++--- ...pai-making-the-most-of-test-time-compute-in-llms.json | 6 +++--- ...-data-wrangling-with-duckdb-pydata-virginia-2025.json | 6 +++--- ...pplications-of-apache-arrow-pydata-virginia-2025.json | 7 ++++--- 26 files changed, 86 insertions(+), 79 deletions(-) diff --git a/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json b/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json index 46bd46a3f..401a1327b 100644 --- a/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/andrea-hobby-responsible-ai-with-scipy-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nSciPy is a powerful library for scientific and technical computing in Python. The primary objectives of this presentation are to explore the core concepts of Responsible AI and to demonstrate these concepts with SciPy.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "SciPy is a powerful library for scientific and technical computing in Python. The primary objectives of this presentation are to explore the core concepts of Responsible AI and to demonstrate these concepts with SciPy.", "duration": 3152, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Andrea Hobby" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/W6fTFSgyhMg/maxresdefault.jpg", - "title": "Andrea Hobby - Responsible AI with SciPy | PyData Virginia 2025", + "title": "Responsible AI with SciPy", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json b/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json index 5450deb2f..ae7b5ae7f 100644 --- a/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/benjamin-bengfort-practical-multi-armed-bandits-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nMulti-armed bandits are a reinforcement learning tool often used in environments where the cost or rewards of different choices are unknown or where those functions may change over time. The good news is that as far as implementation goes, bandits are surprisingly easy to implement; however, in practice, the difficulty comes from defining a reward function that best targets your specific use case. In this talk, we will discuss how to use bandit algorithms effectively, taking note of practical strategies for experimental design and deployment of bandits in your applications.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Multi-armed bandits are a reinforcement learning tool often used in environments where the cost or rewards of different choices are unknown or where those functions may change over time. The good news is that as far as implementation goes, bandits are surprisingly easy to implement; however, in practice, the difficulty comes from defining a reward function that best targets your specific use case. In this talk, we will discuss how to use bandit algorithms effectively, taking note of practical strategies for experimental design and deployment of bandits in your applications.", "duration": 1819, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Benjamin Bengfort" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/jP978VKBl-w/maxresdefault.jpg", - "title": "Benjamin Bengfort - Practical Multi Armed Bandits | PyData Virginia 2025", + "title": "Practical Multi Armed Bandits", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json b/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json index 14fafa13a..a0bcf8e1b 100644 --- a/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/chris-fonnesbeck-a-beginner-s-guide-to-variational-inference-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nWhen Bayesian modeling scales up to large datasets, traditional MCMC methods can become impractical due to their computational demands. Variational Inference (VI) offers a scalable alternative, trading exactness for speed while retaining the essence of Bayesian inference.\n\nIn this tutorial, we\u2019ll explore how to implement and compare VI techniques in PyMC, including the Adaptive Divergence Variational Inference (ADVI) and the cutting-edge Pathfinder algorithm.\n\nStarting with simple models like linear regression, we\u2019ll gradually introduce more complex, real-world applications, comparing the performance of VI against Markov Chain Monte Carlo (MCMC) to understand the trade-offs in speed and accuracy.\n\nThis tutorial will arm participants with practical tools to deploy VI in their workflows and help answer pressing questions, like \"What do I do when MCMC is too slow?\", or \"How does VI compare to MCMC in terms of approximation quality?\".\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "When Bayesian modeling scales up to large datasets, traditional MCMC methods can become impractical due to their computational demands. Variational Inference (VI) offers a scalable alternative, trading exactness for speed while retaining the essence of Bayesian inference.\n\nIn this tutorial, we\u2019ll explore how to implement and compare VI techniques in PyMC, including the Adaptive Divergence Variational Inference (ADVI) and the cutting-edge Pathfinder algorithm.\n\nStarting with simple models like linear regression, we\u2019ll gradually introduce more complex, real-world applications, comparing the performance of VI against Markov Chain Monte Carlo (MCMC) to understand the trade-offs in speed and accuracy.\n\nThis tutorial will arm participants with practical tools to deploy VI in their workflows and help answer pressing questions, like \"What do I do when MCMC is too slow?\", or \"How does VI compare to MCMC in terms of approximation quality?\".", "duration": 5357, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Chris Fonnesbeck" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/XECLmgnS6Ng/maxresdefault.jpg", - "title": "Chris Fonnesbeck - A Beginner's Guide to Variational Inference | PyData Virginia 2025", + "title": "A Beginner's Guide to Variational Inference", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json b/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json index d5f79372f..9fd851114 100644 --- a/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/cory-eicher-using-python-to-unlock-insights-from-openstreetmap-data-at-scale-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nGeospatial data can unlock valuable insights. OpenStreetMap includes electric power and telecommunication infrastructure geospatial data, and it is already \u201copen\u201d. This presentation will demonstrate how to use Python to \u201cunlock the insights\u201d available in OSM power and telecommunications geospatial data.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Geospatial data can unlock valuable insights. OpenStreetMap includes electric power and telecommunication infrastructure geospatial data, and it is already \u201copen\u201d. This presentation will demonstrate how to use Python to \u201cunlock the insights\u201d available in OSM power and telecommunications geospatial data.", "duration": 1522, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Cory Eicher" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/kmRyFmMThVo/maxresdefault.jpg", - "title": "Cory Eicher- Using Python to Unlock Insights from OpenStreetMap Data at Scale | PyData Virginia 2025", + "title": "Using Python to Unlock Insights from OpenStreetMap Data at Scale", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json b/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json index 8953197f8..61b74d397 100644 --- a/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/cynthia-ukawu-from-pandas-to-pyspark-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nTired of waiting for massive datasets to load on your local machine? In this beginner-friendly tutorial, we\u2019ll explore how to scale your data analysis skills from pandas to PySpark using a real-world anime dataset. We\u2019ll walk through the basics of distributed computing, discuss why Spark was created, and demonstrate the benefits of working with PySpark for big data tasks\u2014including reading, cleaning, and transforming millions of records with ease. By the end of this workshop, you\u2019ll understand how PySpark harnesses cluster computing to handle large-scale data and you\u2019ll be comfortable applying these techniques to your own projects.\n\nParticipant Requirements:\n- A laptop (any OS) with an internet connection\n- A Google account (to access Colab notebooks and slides)\n- Familiarity with Python and pandas\n\nHere's the link to the Google Colab to follow along \ud83d\udc47\ud83c\udffe\nhttps://colab.research.google.com/drive/1fi0cTQ1NIE5kDEH0ynp2sqDuVeiBJJWU?usp=sharing\n\nHere are the slides \ud83d\udc47\ud83c\udffe\nhttps://drive.google.com/file/d/11JIih1VzLxTJ9O6PeGzqD_e8vumTZQmw/view?usp=sharing\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Tired of waiting for massive datasets to load on your local machine? In this beginner-friendly tutorial, we\u2019ll explore how to scale your data analysis skills from pandas to PySpark using a real-world anime dataset. We\u2019ll walk through the basics of distributed computing, discuss why Spark was created, and demonstrate the benefits of working with PySpark for big data tasks\u2014including reading, cleaning, and transforming millions of records with ease. By the end of this workshop, you\u2019ll understand how PySpark harnesses cluster computing to handle large-scale data and you\u2019ll be comfortable applying these techniques to your own projects.\n\nParticipant Requirements:\n- A laptop (any OS) with an internet connection\n- A Google account (to access Colab notebooks and slides)\n- Familiarity with Python and pandas\n\nHere's the link to the Google Colab to follow along \ud83d\udc47\ud83c\udffe\nhttps://colab.research.google.com/drive/1fi0cTQ1NIE5kDEH0ynp2sqDuVeiBJJWU?usp=sharing\n\nHere are the slides \ud83d\udc47\ud83c\udffe\nhttps://drive.google.com/file/d/11JIih1VzLxTJ9O6PeGzqD_e8vumTZQmw/view?usp=sharing", "duration": 4399, "language": "eng", "recorded": "2025-04-18", @@ -22,11 +22,11 @@ } ], "speakers": [ - "TODO" + "Cynthia Ukawu" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/McbJMdcKp5c/maxresdefault.jpg", - "title": "Cynthia Ukawu - From Pandas to PySpark | PyData Virginia 2025", + "title": "From Pandas to PySpark", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json index 0c8494b1a..5f6770954 100644 --- a/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dmitry-petrov-versioning-multimodal-data-metadata-beyond-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nThe team behind DVC has spent years tackling data versioning challenges. With the rise of AI, we\u2019ve seen new complexities emerge - especially with multimodal datasets like images, video, audio, and text. This talk shows why multimodal data versioning is different and how Pydantic provides a powerful way to structure and integrate metadata.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "The team behind DVC has spent years tackling data versioning challenges. With the rise of AI, we\u2019ve seen new complexities emerge - especially with multimodal datasets like images, video, audio, and text. This talk shows why multimodal data versioning is different and how Pydantic provides a powerful way to structure and integrate metadata.", "duration": 1659, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Dmitry Petrov" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/yNBoJSKl49U/maxresdefault.jpg", - "title": "Dmitry Petrov - Versioning Multimodal Data: Metadata & Beyond | PyData Virginia 2025", + "title": "Versioning Multimodal Data: Metadata & Beyond", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json index 6a0902ee4..f0c005e05 100644 --- a/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nHealth disparities remain a critical challenge in public health, demanding innovative approaches to uncover inequities and drive actionable change. This webinar will demonstrate how Python can serve as a powerful tool for creating data visualizations that illustrate the unequal burden of HIV across different populations. Participants will learn how Python\u2019s popular libraries, such as Matplotlib, Seaborn, and Plotly, can transform complex datasets into accessible, impactful visuals.\nUsing an HIV dataset containing demographic, geographic, and clinical variables, this session will guide attendees through a series of practical examples. From creating heatmaps and geospatial maps to analyzing temporal trends, the webinar emphasizes how to identify and communicate key social determinants related to race, gender, socioeconomic status, and access to care. Through hands-on demonstrations, attendees will see how Python\u2019s capabilities streamline data analysis and visualization workflows.\nKey takeaways from the session include identifying regions and communities in Texas, disproportionately affected by HIV, uncovering intersectional factors influencing health outcomes, and leveraging visual tools to inform policy and resource allocation. Special attention will be given to designing visuals that resonate with non-technical audiences, ensuring findings are actionable for public health professionals and policymakers.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Health disparities remain a critical challenge in public health, demanding innovative approaches to uncover inequities and drive actionable change. This webinar will demonstrate how Python can serve as a powerful tool for creating data visualizations that illustrate the unequal burden of HIV across different populations. Participants will learn how Python\u2019s popular libraries, such as Matplotlib, Seaborn, and Plotly, can transform complex datasets into accessible, impactful visuals.\nUsing an HIV dataset containing demographic, geographic, and clinical variables, this session will guide attendees through a series of practical examples. From creating heatmaps and geospatial maps to analyzing temporal trends, the webinar emphasizes how to identify and communicate key social determinants related to race, gender, socioeconomic status, and access to care. Through hands-on demonstrations, attendees will see how Python\u2019s capabilities streamline data analysis and visualization workflows.\nKey takeaways from the session include identifying regions and communities in Texas, disproportionately affected by HIV, uncovering intersectional factors influencing health outcomes, and leveraging visual tools to inform policy and resource allocation. Special attention will be given to designing visuals that resonate with non-technical audiences, ensuring findings are actionable for public health professionals and policymakers.", "duration": 4007, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Dr. Kimberly Deas" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/-BA2eXBoDoc/maxresdefault.jpg", - "title": "Dr. Kimberly Deas-Data Viz in Pyton as a Tool to Study HIV Health Disparities | PyData Virginia 2025", + "title": "Data Viz in Python as a Tool to Study HIV Health Disparities", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json index db4e054a8..ab94a38f0 100644 --- a/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nWhere do landlords engage in more eviction actions? What characteristics of renters or landlords increase the practice of serial filing? There is widespread interest in using administrative data -- information collected by government and agencies in the implementation of public programs -- to evaluate systems and promote most just outcomes. Working with the Civil Court Data Initiative of Legal Services Corporation, we use data collected from civil court records in Virginia to analyze the behavior of landlords. Expanding on our Virginia Evictors Catalog, we use data on court evictions to build additional data tools to support the work of legal and housing advocates and model key eviction outcomes to contribute to our understanding of landlord behavior.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Where do landlords engage in more eviction actions? What characteristics of renters or landlords increase the practice of serial filing? There is widespread interest in using administrative data -- information collected by government and agencies in the implementation of public programs -- to evaluate systems and promote most just outcomes. Working with the Civil Court Data Initiative of Legal Services Corporation, we use data collected from civil court records in Virginia to analyze the behavior of landlords. Expanding on our Virginia Evictors Catalog, we use data on court evictions to build additional data tools to support the work of legal and housing advocates and model key eviction outcomes to contribute to our understanding of landlord behavior.", "duration": 1755, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,12 @@ } ], "speakers": [ - "TODO" + "Dr. Michele Claibourn", + "Samantha Toet" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/eE0D79trL2c/maxresdefault.jpg", - "title": "Dr. Michele Claibourn & Samantha Toet - Exploring Eviction Trends in Virginia | PyData Virginia 2025", + "title": "Exploring Eviction Trends in Virginia", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json b/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json index 5a287085e..646512da4 100644 --- a/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/greg-michaelson-how-data-science-shortened-the-covid-19-pandemic-by-2-months-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nWhen every day counted during the COVID-19 pandemic, data science became an essential catalyst in accelerating the path to widespread vaccination. This talk delves into the data-driven strategies that enabled the U.S. government\u2019s vaccine trials to move faster, cutting crucial weeks\u20146 to 8, by our estimates\u2014off the timeline to deployment. Through sophisticated geospatial modeling, we identified and swiftly mobilized trial recruitment efforts in emerging hot zones, ensuring that each candidate pool was both numerically sufficient and demographically representative. Attendees will discover how advanced analytics, predictive modeling, and interdisciplinary collaboration converged to target the right communities at the right time, ultimately expediting vaccine availability. This behind-the-scenes look at rapid-response data science highlights not just the technical innovations, but the decisive cultural and operational shifts that turned real-time insights into life-saving action.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "When every day counted during the COVID-19 pandemic, data science became an essential catalyst in accelerating the path to widespread vaccination. This talk delves into the data-driven strategies that enabled the U.S. government\u2019s vaccine trials to move faster, cutting crucial weeks\u20146 to 8, by our estimates\u2014off the timeline to deployment. Through sophisticated geospatial modeling, we identified and swiftly mobilized trial recruitment efforts in emerging hot zones, ensuring that each candidate pool was both numerically sufficient and demographically representative. Attendees will discover how advanced analytics, predictive modeling, and interdisciplinary collaboration converged to target the right communities at the right time, ultimately expediting vaccine availability. This behind-the-scenes look at rapid-response data science highlights not just the technical innovations, but the decisive cultural and operational shifts that turned real-time insights into life-saving action.", "duration": 1733, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Greg Michaelson" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/zXKdjBv1SGc/maxresdefault.jpg", - "title": "Greg Michaelson- How data science shortened the COVID-19 pandemic by 2 months | PyData Virginia 2025", + "title": "How data science shortened the COVID-19 pandemic by 2 months", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json b/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json index 037ec8ffd..7fe9585ff 100644 --- a/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/john-berryman-mastering-llms-from-prompt-engineering-to-agentic-ai-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nThis workshop will provide a comprehensive introduction to Large Language Models (LLMs), covering their capabilities, structure, and practical applications. Participants will learn prompt engineering techniques, retrieval-augmented generation (RAG), agentic AI design, fine-tuning strategies, and model evaluation methods. The session will conclude with a discussion on the future of AI-powered reasoning machines.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "This workshop will provide a comprehensive introduction to Large Language Models (LLMs), covering their capabilities, structure, and practical applications. Participants will learn prompt engineering techniques, retrieval-augmented generation (RAG), agentic AI design, fine-tuning strategies, and model evaluation methods. The session will conclude with a discussion on the future of AI-powered reasoning machines.", "duration": 5940, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "John Berryman" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/jmwLzX_ltbQ/maxresdefault.jpg", - "title": "John Berryman - Mastering LLMs: From Prompt Engineering to Agentic AI | PyData Virginia 2025", + "title": "Mastering LLMs: From Prompt Engineering to Agentic AI", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json b/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json index 77d1d278f..a80850db5 100644 --- a/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/keynote-rajkumar-venkatesan-building-ai-first-organizations-opening-notes-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nAs businesses strive to become AI-first, the pivotal role of AI practitioners extends beyond technical implementation to encompass strategic stewardship. This transition necessitates a profound understanding of organizational goals, data governance, and ethical considerations. By aligning AI initiatives with business objectives, fostering cross-functional collaboration, and addressing challenges such as data privacy and employee adaptation, AI professionals can drive effective transformation. This keynote explores the essential competencies and approaches required for AI practitioners to lead their organizations successfully into an AI-centric future.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "As businesses strive to become AI-first, the pivotal role of AI practitioners extends beyond technical implementation to encompass strategic stewardship. This transition necessitates a profound understanding of organizational goals, data governance, and ethical considerations. By aligning AI initiatives with business objectives, fostering cross-functional collaboration, and addressing challenges such as data privacy and employee adaptation, AI professionals can drive effective transformation. This keynote explores the essential competencies and approaches required for AI practitioners to lead their organizations successfully into an AI-centric future.", "duration": 3481, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Rajkumar Venkatesan" ], - "tags": [], + "tags": ["keynote"], "thumbnail_url": "https://i.ytimg.com/vi/jOgUY9Rcd80/maxresdefault.jpg", - "title": "KEYNOTE: Rajkumar Venkatesan- Building AI-First Organizations & Opening Notes | PyData Virginia 2025", + "title": "Building AI-First Organizations", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json b/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json index 0637155f7..4f5063e7e 100644 --- a/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/krishna-rekapalli-unlock-information-from-tables-images-and-complex-docs-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nTraditional PDF extraction tools often struggle with complex layouts, tables, and images, Docling (an opensource Python library developed at IBM) excels at extracting structured information from these elements, enabling the creation of richer, more accurate vector databases. This hands-on tutorial will guide participants through building a Retrieval Augmented Generation (RAG) system using Docling, an open-source document processing library.\n\nParticipants will learn how to harness Docling's advanced capabilities to build superior RAG systems that can understand and retrieve information from complex document elements that traditional tools might miss. Participants will learn how to handle complex documents, extract structured information, and create an efficient vector database for semantic search. The session will cover best practices for document parsing, chunking strategies, and integration with popular LLM frameworks.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Traditional PDF extraction tools often struggle with complex layouts, tables, and images, Docling (an opensource Python library developed at IBM) excels at extracting structured information from these elements, enabling the creation of richer, more accurate vector databases. This hands-on tutorial will guide participants through building a Retrieval Augmented Generation (RAG) system using Docling, an open-source document processing library.\n\nParticipants will learn how to harness Docling's advanced capabilities to build superior RAG systems that can understand and retrieve information from complex document elements that traditional tools might miss. Participants will learn how to handle complex documents, extract structured information, and create an efficient vector database for semantic search. The session will cover best practices for document parsing, chunking strategies, and integration with popular LLM frameworks.", "duration": 5414, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Krishna Rekapalli" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/41pxp4-pRmI/maxresdefault.jpg", - "title": "Krishna Rekapalli - Unlock Information from Tables, Images and Complex Docs | PyData Virginia 2025", + "title": "Unlock Information from Tables, Images and Complex Docs", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json index c5f87747a..2fbe226df 100644 --- a/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nWhen \u201cAI Agent\u201d became the buzz word, have you ever wondered: what exactly is an AI agent? What is the multi-agent system? And how can you use the power of AI agents in your day-to-day data science workflow? In this hands-on tutorial, I will introduce AI agents and demonstrate how to design, build, and manage a multi-agent system for your data science workflows. Participants will learn how to break down complex tasks, assign AI agents to collaborate effectively, and ensure accuracy and reliability in their outputs. We will also discuss the trade-offs, limitations, and best practices for incorporating AI agents into data science projects.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "When \u201cAI Agent\u201d became the buzz word, have you ever wondered: what exactly is an AI agent? What is the multi-agent system? And how can you use the power of AI agents in your day-to-day data science workflow? In this hands-on tutorial, I will introduce AI agents and demonstrate how to design, build, and manage a multi-agent system for your data science workflows. Participants will learn how to break down complex tasks, assign AI agents to collaborate effectively, and ensure accuracy and reliability in their outputs. We will also discuss the trade-offs, limitations, and best practices for incorporating AI agents into data science projects.", "duration": 5337, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,14 @@ } ], "speakers": [ - "TODO" + "Krishnan", + "Liu", + "Puri", + "Rojas" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/s5dx_4y6Iy8/maxresdefault.jpg", - "title": "Krishnan, Liu, Puri, & Rojas - Build Your Own Data Science AI Agents | PyData Virginia 2025", + "title": "Build Your Own Data Science AI Agents", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json b/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json index fc7df7211..ca3890f0e 100644 --- a/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json +++ b/pydata-virginia-2025/videos/mackenzye-leroy-building-a-robust-evaluation-framework-for-genai-productivity-tools.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nDiscover how S&P Global built an enterprise-grade evaluation framework that transformed our GenAI deployment process. Through automated monitoring, expert validation, & continuous testing, we\u2019ve streamlined the document integration step of our RAG tools, while ensuring our AI tools maintain consistent quality and reliability.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Discover how S&P Global built an enterprise-grade evaluation framework that transformed our GenAI deployment process. Through automated monitoring, expert validation, & continuous testing, we\u2019ve streamlined the document integration step of our RAG tools, while ensuring our AI tools maintain consistent quality and reliability.", "duration": 1799, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "MacKenzye Leroy" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/DJr1FSDCpGo/maxresdefault.jpg", - "title": "MacKenzye Leroy - Building a Robust Evaluation Framework for GenAI Productivity Tools", + "title": "Building a Robust Evaluation Framework for GenAI Productivity Tools", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json b/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json index 1e9025f28..026824bde 100644 --- a/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json +++ b/pydata-virginia-2025/videos/matt-litz-tutorial-on-image-classification-using-scikit-image-scikit-learn-and-pytorch.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nTutorial on building an image segmentation and classification pipeline for binary or multiclass classification using the popular packages scikit-learn, scikit-image and PyTorch\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Tutorial on building an image segmentation and classification pipeline for binary or multiclass classification using the popular packages scikit-learn, scikit-image and PyTorch", "duration": 4500, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Matt Litz" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/xLxBCPmNDaI/maxresdefault.jpg", - "title": "Matt Litz - Tutorial on Image Classification using Scikit-Image, Scikit-learn, and PyTorch", + "title": "Tutorial on Image Classification using Scikit-Image, Scikit-learn, and PyTorch", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json b/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json index e4c427a4d..35efbf672 100644 --- a/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json +++ b/pydata-virginia-2025/videos/mauricio-mathey-using-changepoint-and-bayesian-analysis-to-drive-safety-improvements-in-mining.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nIn the mining industry's pursuit of zero harm, distinguishing real safety improvements from random variation is crucial yet challenging. This talk demonstrates how classical changepoint analysis and Bayesian methods provide safety teams at Asarco LLC with rigorous tools to objectively evaluate progress towards our zero-harm goal. Using near miss reporting and lost time metrics, we will show how these statistical approaches help identify meaningful trends while avoiding misleading conclusions from natural variation. While the focus is on mining, these methods are applicable to other safety-critical and data-limited scenarios. No prior experience with changepoint analysis is required.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "In the mining industry's pursuit of zero harm, distinguishing real safety improvements from random variation is crucial yet challenging. This talk demonstrates how classical changepoint analysis and Bayesian methods provide safety teams at Asarco LLC with rigorous tools to objectively evaluate progress towards our zero-harm goal. Using near miss reporting and lost time metrics, we will show how these statistical approaches help identify meaningful trends while avoiding misleading conclusions from natural variation. While the focus is on mining, these methods are applicable to other safety-critical and data-limited scenarios. No prior experience with changepoint analysis is required.", "duration": 1672, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Mauricio Mathey" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/B0eGMlYQkiw/maxresdefault.jpg", - "title": "Mauricio Mathey - Using Changepoint and Bayesian Analysis to Drive Safety Improvements in Mining", + "title": "Using Changepoint and Bayesian Analysis to Drive Safety Improvements in Mining", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json b/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json index 0d65ee055..c374f1962 100644 --- a/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json +++ b/pydata-virginia-2025/videos/nathan-day-maximizing-multimodal-exploring-the-search-frontier-of-text-to-image-models.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nText-to-Image models, like CLIP, have brought us into a new frontier of visual search. Whether it's searching by circling a section of a photo or powering image generators like Dalle-E the gap between pixels and tokens has never been smaller. This talk discusses how we are improving search and empowering designers with these models at Eezy, a stock art marketplace.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Text-to-Image models, like CLIP, have brought us into a new frontier of visual search. Whether it's searching by circling a section of a photo or powering image generators like Dalle-E the gap between pixels and tokens has never been smaller. This talk discusses how we are improving search and empowering designers with these models at Eezy, a stock art marketplace.", "duration": 1549, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Nathan Day" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/Xzd25tbKFLA/maxresdefault.jpg", - "title": "Nathan Day - Maximizing Multimodal: Exploring the search frontier of text-to-image models", + "title": "Maximizing Multimodal: Exploring the search frontier of text-to-image models", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json b/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json index 509ba1b5d..0fa1f1279 100644 --- a/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json +++ b/pydata-virginia-2025/videos/naty-clementi-mike-mccarty-rapids-gpu-accelerated-data-science-for-pydata-users.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nIn this introductory hands-on tutorial, participants will learn how to accelerate their data workflows with RAPIDS (https://rapids.ai/), an open-source suite of libraries designed to leverage the power of NVIDIA (https://www.nvidia.com/) GPUs for end-to-end data pipelines. Using familiar PyData APIs like cuDF (GPU-accelerated pandas) and cuML (GPU-accelerated machine learning), attendees will explore how to seamlessly integrate these tools into their existing workflows with minimal code changes, achieving significant speedups in tasks such as data processing and model training.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "In this introductory hands-on tutorial, participants will learn how to accelerate their data workflows with RAPIDS (https://rapids.ai/), an open-source suite of libraries designed to leverage the power of NVIDIA (https://www.nvidia.com/) GPUs for end-to-end data pipelines. Using familiar PyData APIs like cuDF (GPU-accelerated pandas) and cuML (GPU-accelerated machine learning), attendees will explore how to seamlessly integrate these tools into their existing workflows with minimal code changes, achieving significant speedups in tasks such as data processing and model training.", "duration": 5729, "language": "eng", "recorded": "2025-04-18", @@ -22,11 +22,12 @@ } ], "speakers": [ - "TODO" + "Naty Clementi", + "Mike McCarty" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/IJ8rjVD4-yE/maxresdefault.jpg", - "title": "Naty Clementi & Mike McCarty - RAPIDS: GPU-Accelerated Data Science for PyData Users", + "title": "RAPIDS: GPU-Accelerated Data Science for PyData Users", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json b/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json index 61d9f8706..652cb445d 100644 --- a/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json +++ b/pydata-virginia-2025/videos/ralph-liu-zero-code-change-gpu-powered-graph-analytics-with-networkx-and-cugraph.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nGraphs are a fundamental form of storing data. This is because everything is connected! Hence, Graphs are very useful for modeling and solving a wide variety of real-world problems.\n\nWhile NetworkX is amazing for getting started with Graphs, the library encounters bottlenecks in performance at scale.\n\nIs there a solution out there for users who want more performance from NX and also Open-Source developers who want to implement fast algorithms? Yes! Thanks to the magic of dispatching.\n\nNetworkX now supports dispatching to various backends, including the GPU accelerated cuGraph library by Nvidia RAPIDS.\n\nAttend this talk to learn about how you can use nx-cugraph \u2013 the cuGraph-powered backend for NetworkX \u2013 and how it unlocks exciting new possibilities for you to solve real-world graph analytics problems.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Graphs are a fundamental form of storing data. This is because everything is connected! Hence, Graphs are very useful for modeling and solving a wide variety of real-world problems.\n\nWhile NetworkX is amazing for getting started with Graphs, the library encounters bottlenecks in performance at scale.\n\nIs there a solution out there for users who want more performance from NX and also Open-Source developers who want to implement fast algorithms? Yes! Thanks to the magic of dispatching.\n\nNetworkX now supports dispatching to various backends, including the GPU accelerated cuGraph library by Nvidia RAPIDS.\n\nAttend this talk to learn about how you can use nx-cugraph \u2013 the cuGraph-powered backend for NetworkX \u2013 and how it unlocks exciting new possibilities for you to solve real-world graph analytics problems.", "duration": 1916, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Ralph Liu" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/TUT4GLDo2pU/maxresdefault.jpg", - "title": "Ralph Liu - Zero Code Change GPU-Powered Graph Analytics with NetworkX and cuGraph", + "title": "Zero Code Change GPU-Powered Graph Analytics with NetworkX and cuGraph", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json b/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json index 38c1848aa..76f88f772 100644 --- a/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json +++ b/pydata-virginia-2025/videos/robert-shelton-blazing-the-ai-trail-using-langgraph-to-conquer-the-oregon-trail.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nAgents have become one of the most talked-about topics in the AI community, but much of the discussion focuses on its potential impact rather than practical implementation. This hands-on workshop will guide data scientists and engineers through building a complete workflow using LangGraph, and will show how to define custom tools, implement vector retrieval, leverage semantic caching, incorporate allow/block list routing, and structure model output for downstream consumption. In order to participate, attendees will need to have Python (3.11 or later), docker, an OpenAI api key, and the starter code for the project cloned.\n\nStarter code: https://github.com/redis-developer/agents-redis-lang-graph-workshop\n\nNote: participants can test their environment setup ahead of time by following the Readme and running python test_setup.py before the workshop.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Agents have become one of the most talked-about topics in the AI community, but much of the discussion focuses on its potential impact rather than practical implementation. This hands-on workshop will guide data scientists and engineers through building a complete workflow using LangGraph, and will show how to define custom tools, implement vector retrieval, leverage semantic caching, incorporate allow/block list routing, and structure model output for downstream consumption. In order to participate, attendees will need to have Python (3.11 or later), docker, an OpenAI api key, and the starter code for the project cloned.\n\nStarter code: https://github.com/redis-developer/agents-redis-lang-graph-workshop\n\nNote: participants can test their environment setup ahead of time by following the Readme and running python test_setup.py before the workshop.", "duration": 5195, "language": "eng", "recorded": "2025-04-18", @@ -18,11 +18,11 @@ } ], "speakers": [ - "TODO" + "Robert Shelton" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/kCpZ22_XluM/maxresdefault.jpg", - "title": "Robert Shelton - Blazing the AI Trail: Using LangGraph to Conquer the Oregon Trail", + "title": "Blazing the AI Trail: Using LangGraph to Conquer the Oregon Trail", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json b/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json index 0593be384..bd7fa8a61 100644 --- a/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/robin-isadora-brown-lane-rasberry-introduction-to-wikidata-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nWe will review Wikipedia, introduce Wikidata, then demonstrate queries to access wiki content\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "We will review Wikipedia, introduce Wikidata, then demonstrate queries to access wiki content", "duration": 4404, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,12 @@ } ], "speakers": [ - "TODO" + "Robin Isadora Brown", + "Lane Rasberry" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/0eGNxqvW89M/maxresdefault.jpg", - "title": "Robin Isadora Brown & Lane Rasberry - Introduction to Wikidata | PyData Virginia 2025", + "title": "Introduction to Wikidata", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json b/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json index 9a266ab48..a987c3a5e 100644 --- a/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/sihang-jiang-bayesian-risk-analysis-for-large-multi-modal-data-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nIn the era of big data, multi-modal data from multiple sources or modalities has become increasingly prevalent in various fields such as healthcare. The National COVID Cohort Collaborative (N3C) provides researchers with abundant clinical data in different forms by aggregating and harmonizing Electronic Health Records (EHR) data across different clinical organizations in the United States, making it convenient for researchers to analyze COVID-related topics and build models with large multimodal data. Bayesian risk analysis has advantages in handling the complexities and heterogeneities of multi-modal healthcare data, specifically in cohort studies when researchers try to answer questions of interest in public health or medicine field regarding COVID and Long COVID.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "In the era of big data, multi-modal data from multiple sources or modalities has become increasingly prevalent in various fields such as healthcare. The National COVID Cohort Collaborative (N3C) provides researchers with abundant clinical data in different forms by aggregating and harmonizing Electronic Health Records (EHR) data across different clinical organizations in the United States, making it convenient for researchers to analyze COVID-related topics and build models with large multimodal data. Bayesian risk analysis has advantages in handling the complexities and heterogeneities of multi-modal healthcare data, specifically in cohort studies when researchers try to answer questions of interest in public health or medicine field regarding COVID and Long COVID.", "duration": 1878, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Sihang Jiang" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/Ul7ndUBtx2Y/maxresdefault.jpg", - "title": "Sihang Jiang - Bayesian Risk Analysis For Large Multi-Modal Data | PyData Virginia 2025", + "title": "Bayesian Risk Analysis For Large Multi-Modal Data", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json b/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json index 9271d490e..2c55e6eaa 100644 --- a/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/siwen-liao-celebrating-neurodiversity-through-aesthetic-data-visualization-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nIn our project, we took MRI-derived brain data and reinterpreted it through an aesthetic lens. Using multidimensional scaling (MDS) to distill complex patterns in cortical anatomy, we transformed these insights into physical 3D-printed brain models. Each sculpture serves as a tangible narrative, celebrating both the subtle and striking differences between male and female brains, whether neurotypical or affected by ASD.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "In our project, we took MRI-derived brain data and reinterpreted it through an aesthetic lens. Using multidimensional scaling (MDS) to distill complex patterns in cortical anatomy, we transformed these insights into physical 3D-printed brain models. Each sculpture serves as a tangible narrative, celebrating both the subtle and striking differences between male and female brains, whether neurotypical or affected by ASD.", "duration": 1592, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Siwen Liao" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/4riqJcrDVcw/maxresdefault.jpg", - "title": "Siwen Liao - Celebrating Neurodiversity Through Aesthetic Data Visualization | PyData Virginia 2025", + "title": "Celebrating Neurodiversity Through Aesthetic Data Visualization", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json b/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json index 44682a3f4..b32d9ac5b 100644 --- a/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json +++ b/pydata-virginia-2025/videos/suhas-pai-making-the-most-of-test-time-compute-in-llms.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "", "duration": 1841, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Suhas Pai" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/dwILgDsbmto/maxresdefault.jpg", - "title": "Suhas Pai - Making the most of test-time compute in LLMs", + "title": "Making the most of test-time compute in LLMs", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json b/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json index f85c6a3d8..8279e7ed8 100644 --- a/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/will-angel-data-wrangling-with-duckdb-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nLearn how to wrangle data in Python with DuckDB, a fast, open source, in-process analytical SQL database!\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Learn how to wrangle data in Python with DuckDB, a fast, open source, in-process analytical SQL database!", "duration": 2066, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,11 @@ } ], "speakers": [ - "TODO" + "Will Angel" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/PMEHdnrYyaE/maxresdefault.jpg", - "title": "Will Angel - Data Wrangling with DuckDB | PyData Virginia 2025", + "title": "Data Wrangling with DuckDB", "videos": [ { "type": "youtube", diff --git a/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json b/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json index d4f357d24..6948cba2a 100644 --- a/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/will-ayd-matt-topol-practical-applications-of-apache-arrow-pydata-virginia-2025.json @@ -1,5 +1,5 @@ { - "description": "www.pydata.org\n\nData system interoperability remains a significant challenge in open source ecosystems, with high costs in development time and resources when moving data across complex infrastructures. The Apache Arrow project offers a standardized solution to reduce these integration challenges.\n\nWill Ayd (Apache Arrow Committer and pandas maintainer) and Matt Topol (Apache Arrow PMC Member and author of \"In Memory Analytics with Apache Arrow\") will discuss how Apache Arrow is changing the data landscape. A brief overview of Arrow standards will be provided, while also reviewing real world implementations of where the Arrow specification has driven down the cost of data interoperability.\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. \n\nPyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.\n\n00:00 Welcome!\n00:10 Help us add time stamps or captions to this video! See the description for details.\n\nWant to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps", + "description": "Data system interoperability remains a significant challenge in open source ecosystems, with high costs in development time and resources when moving data across complex infrastructures. The Apache Arrow project offers a standardized solution to reduce these integration challenges.\n\nWill Ayd (Apache Arrow Committer and pandas maintainer) and Matt Topol (Apache Arrow PMC Member and author of \"In Memory Analytics with Apache Arrow\") will discuss how Apache Arrow is changing the data landscape. A brief overview of Arrow standards will be provided, while also reviewing real world implementations of where the Arrow specification has driven down the cost of data interoperability.", "duration": 2099, "language": "eng", "recorded": "2025-04-18", @@ -14,11 +14,12 @@ } ], "speakers": [ - "TODO" + "Will Ayd", + "Matt Topol" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/G4eXX_-S5nM/maxresdefault.jpg", - "title": "Will Ayd & Matt Topol - Practical Applications of Apache Arrow | PyData Virginia 2025", + "title": "Practical Applications of Apache Arrow", "videos": [ { "type": "youtube", From 4a357bb8d526008afa6476ce83dae66117179d8c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ezequiel=20Leonardo=20Casta=C3=B1o?= <14986783+ELC@users.noreply.github.com> Date: Mon, 24 Nov 2025 00:59:42 -0300 Subject: [PATCH 4/4] Update speaker names in video metadata for PyData Virginia 2025 by removing titles for clarity. This includes changing "Dr. Kimberly Deas" to "Kimberly Deas" and "Dr. Michele Claibourn" to "Michele Claibourn", as well as updating the speaker list for the session led by Krishnan Liu Puri Rojas to include full names. --- ...study-hiv-health-disparities-pydata-virginia-2025.json | 2 +- ...-eviction-trends-in-virginia-pydata-virginia-2025.json | 2 +- ...r-own-data-science-ai-agents-pydata-virginia-2025.json | 8 ++++---- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json index f0c005e05..eadc30931 100644 --- a/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dr-kimberly-deas-data-viz-in-pyton-as-a-tool-to-study-hiv-health-disparities-pydata-virginia-2025.json @@ -14,7 +14,7 @@ } ], "speakers": [ - "Dr. Kimberly Deas" + "Kimberly Deas" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/-BA2eXBoDoc/maxresdefault.jpg", diff --git a/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json index ab94a38f0..3efb3618a 100644 --- a/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/dr-michele-claibourn-samantha-toet-exploring-eviction-trends-in-virginia-pydata-virginia-2025.json @@ -14,7 +14,7 @@ } ], "speakers": [ - "Dr. Michele Claibourn", + "Michele Claibourn", "Samantha Toet" ], "tags": [], diff --git a/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json index 2fbe226df..4de7a2032 100644 --- a/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json +++ b/pydata-virginia-2025/videos/krishnan-liu-puri-rojas-build-your-own-data-science-ai-agents-pydata-virginia-2025.json @@ -14,10 +14,10 @@ } ], "speakers": [ - "Krishnan", - "Liu", - "Puri", - "Rojas" + "Niharika Krishnan", + "Chuxin Liu", + "Astha Puri", + "Michelle Rojas" ], "tags": [], "thumbnail_url": "https://i.ytimg.com/vi/s5dx_4y6Iy8/maxresdefault.jpg",