I’m a passionate and detail-oriented aspiring Data Analyst with a strong foundation in tools like SQL, Excel, Tableau, and Python. I specialize in data cleaning, visualization, and reporting, and I’m actively building a portfolio to showcase my ability to turn raw data into meaningful insights.
I’ve completed projects that demonstrate skills in dashboard creation, messy data cleaning, typing speed apps, and web scraping, and I’m always exploring ways to combine creativity with logic. I’m currently learning Alteryx and improving my skills in Linux and ethical hacking, aiming to become a well-rounded tech professional.
I believe in consistent growth, learning by doing, and staying calm and focused even when challenges arise. My goal is to start as a Data Analyst, grow in the private sector, and eventually take my skills abroad.
My CV in [pdf]
This is a repository to showcase skills, share projects and track my progress in Data Analytics / Data Science related topics.
- About
- Portfolio Projects
- Python
- SQL
- Excel
- Tableau → Go to Tableau Public
- Power BI
- Alteryx
- Contacts
In this section I will list data analytics projects briefly describing the technology stack used to solve cases.
Code:-
Note: Binder may take 7–8 minutes to load. If you are in a hurry, see the Sales Analysis which uses static graphs for visualization.
Goal:- The goal of this project is to uncover key drivers behind app market success using real-world Play Store datasets from Kaggle.
I focused on:
- Cleaning and standardizing messy data (missing values, inconsistent formats, duplicates)
- Analyzing trends and patterns to identify factors affecting app performance
- Visualizing insights using Plotly to make patterns easier to understand
Code:- sales-python-project.ipynb
Goal:- The goal of this project is to demonstrate the complete workflow of a sales data analysis pipeline using Python.
Data Generation:- A synthetic e-commerce dataset is created using Python (faker, random, numpy) with intentional imperfections such as inconsistent date formats, mixed customer name styles, and missing values to simulate real-world challenges.
Data Cleaning & Preparation:- The generated dataset is then processed to handle messy data, fix inconsistencies, and prepare it for analysis.
Analysis & Visualization:- Exploratory analysis is performed to uncover sales patterns across multiple categories (Electronics, Footwear, Home Appliances, Sports, Beauty, Clothing, etc.). Insights are presented through visualizations highlighting product demand, category trends, and brand performance.
This end-to-end project combines dataset creation + analysis into a single workflow, showcasing practical skills in data wrangling, cleaning, and visualization.
Query:- SQL Query for data cleaning(Transaction).sql
Goal:- The goal of this project was to clean and transform messy transaction data using SQL.
I focused on:
- Removing duplicates and inconsistent records
- Handling missing or invalid values
- Standardizing formats (dates, customer names, product names, etc.)
- Creating a structured dataset ready for analysis
Description:- This project uses a synthetic Transaction dataset generated with Python (random, faker, numpy).
Code:- fake data creator(Transaction).py
Result:-
- Before Cleaning
- After Cleaning
