Data Analysis Using Python

This project demonstrates the use of Python for full-cycle data science, from raw data extraction to advanced statistical insights. While visual tools are great for reporting, this project showcases my ability to use code to solve complex data problems, automate repetitive tasks, and perform deep-dive Exploratory Data Analysis (EDA).

Technical Highlights:

Data Wrangling: Utilized the Pandas and NumPy libraries to clean, transform, and merge disparate datasets, handling missing values and outliers with precision.

Exploratory Data Analysis (EDA): Performed statistical analysis to identify correlations, trends, and distributions within the data.

Automation: Developed Python scripts to automate data cleaning processes, significantly reducing manual effort and human error.

Visualization: Created custom, publication-quality plots using Matplotlib and Seaborn to communicate complex findings simply.

Algorithmic Thinking: Applied advanced data structures (like Linked Lists or Stacks) where necessary to optimize data processing speed.

Technical Stack:

Language: Python

Libraries: Pandas, NumPy, Matplotlib, Seaborn

Environment: Jupyter Notebooks / VS Code