Programming for Data Analytics (PFDA)

This repository showcases my work for the "Programming for Data Analytics" module. The focus of this module is on developing programming skills for handling, visualizing, and analyzing data, as well as designing algorithms for data-intensive applications.

Learning Outcomes

Throughout this module, I have gained proficiency in:

• Data Structures: Using appropriate data structures and techniques for efficiently handling large-scale data.
• Numerical Libraries: Leveraging standard numerical and scientific libraries, including NumPy and Pandas, to perform complex data manipulation and analysis.
• Data Visualization: Programmatically creating plots and visual outputs using libraries such as Matplotlib and Seaborn.
• Regular Expressions: Using regular expressions for data cleaning and text manipulation.
• Algorithm Design: Developing and implementing algorithms to solve numerical and data-related problems.
• Machine Learning: Working with basic machine learning models using scikit-learn for predictive analytics.
• Database Interaction: Interfacing with databases, specifically working with SQLite for storing and querying data.

Repository Structure

This repository is organized into three main directories to reflect the different types of work completed during the course:

• assignments/: Contains practical assignments focusing on data manipulation, visualization, and algorithmic solutions.
• project/: Includes the final project, which demonstrates the culmination of the skills learned, with a focus on solving a real-world data problem.
• my-work/: Personal experiments and additional practice work related to the module.

Key Skills Developed

• Python Programming: Proficient in writing clean, efficient Python code, especially in the context of data analysis.
• Data Handling and Cleaning: Extensive experience using Pandas for reading, transforming, and cleaning datasets.
• Data Visualization: Skilled at creating insightful and informative visualizations using Matplotlib and Seaborn.
• Machine Learning: Implemented basic machine learning workflows, including model training, evaluation, and cross-validation using scikit-learn.
• SQL Databases: Practical knowledge of working with SQLite databases for storing and retrieving structured data.
• Algorithmic Problem-Solving: Developed algorithms to automate tasks, optimize processes, and handle large datasets efficiently.

Feel free to explore the repository to see the progression of these skills through practical assignments and projects!