Pragalya M
This repository presents my work completed as part of a structured Data Analytics Internship. It captures the complete lifecycle of a data analysis project — starting from raw data processing to deriving insights and presenting them effectively.
The goal of this portfolio is to demonstrate my ability to work with real-world data, apply analytical thinking, and communicate meaningful results.
Focused on understanding the dataset and preparing it for analysis. Includes data cleaning, handling missing values, and feature creation.
Involves uncovering patterns, trends, and relationships using statistics and visualizations.
Focuses on solving business problems and building interactive dashboards.
Transforms analysis into a structured business narrative supported by statistical reasoning.
Brings together all work into a cohesive and professional portfolio.
- Python (Pandas, NumPy) – Data cleaning and manipulation
- Matplotlib / Seaborn – Visualization
- SQL – Data querying and analysis
- Google Colab – Development environment
- Power BI / Tableau – Dashboarding
- Developed a strong understanding of data preprocessing and cleaning techniques
- Gained experience in identifying and handling data quality issues
- Learned how to perform exploratory data analysis to extract insights
- Built the ability to connect data findings with business context
- Improved skills in presenting data through visualizations and storytelling
- Successfully transformed raw, unstructured data into a clean and analysis-ready format
- Identified key patterns and trends through structured analysis
- Built a foundation for data-driven decision-making
- Strengthened practical knowledge of the complete data analytics workflow This project highlights the importance of structured thinking, attention to detail, and clear communication in solving real-world data problems.
Feel free to explore the repository and share your feedback!