A progression of foundational data science assignments and exercises completed during the early stages of learning Python for data analysis. This repository documents the learning journey from programming basics through to applied data analytics.
These assignments cover the building blocks of data science in Python — progressing from fundamental programming concepts to exploratory data analysis, visualisation, and statistical thinking. They represent the early stages of the MSc Data Science journey.
| Assignment | Topics Covered |
|---|---|
| Assignments 1 & 2 | Python fundamentals — variables, data types, control flow, functions |
| Assignment 3 | Data structures — lists, dictionaries, NumPy arrays |
| Assignment 4 | Data manipulation with pandas — DataFrames, filtering, grouping |
| Assignment 5 | Exploratory data analysis — descriptive statistics, distributions |
| Assignment 6 | Data visualisation — Matplotlib, Seaborn, chart types |
| Assignment 7 | Applied analysis — real-world dataset exploration |
| Assignment 8 | Statistical analysis — correlation, regression fundamentals |
| Assignment 9 | End-to-end mini-project — combining all skills |
- Python — primary language
- pandas & NumPy — data manipulation and numerical computing
- Matplotlib & Seaborn — data visualisation
- Jupyter Notebooks — interactive, documented assignments
This repository reflects growth from absolute Python beginner through to confident data analyst — building the foundation for more advanced projects like:
- air-quality-analysis — multi-station environmental analysis with Streamlit
- HIV-Antiretroviral-Therapy-ART-Coverage — interactive global health EDA
Allen Chima (@Allenstrange) | Data Science Learning Portfolio