Module 0 — Developer Fundamentals & Tools Before data science, learn to work like a developer. Set up your environment, navigate the Linux command line, manage code with Git and GitHub, and learn how to use AI tools effectively as a productivity layer throughout your work. Tools: Git, GitHub, Linux CLI, Docker, VS Code

Module 1 — Programming Foundations with Python Learn Python from scratch — data types, functions, loops, object-oriented programming, and algorithmic thinking. Build clean, modular code and get comfortable with the core libraries every data scientist uses daily. Tools: Python, NumPy, Pandas, Matplotlib, Jupyter

Module 2 — SQL, Pandas & Data Manipulation Work with relational databases, write SQL queries, and use Pandas to clean, wrangle, and merge messy datasets. Learn to connect Python to databases and prepare data for analysis. Tools: SQL, SQLite, SQLAlchemy, Pandas

Module 3 — Data Analysis & Visualization Apply descriptive statistics and exploratory data analysis (EDA) to real datasets. Build charts, graphs, and interactive dashboards that turn raw numbers into clear, communicable insights. Tools: Matplotlib, Seaborn, Plotly, Streamlit/Dash

Module 4 — Machine Learning Foundations Understand the difference between supervised and unsupervised learning. Build, train, and evaluate models using regression, decision trees, random forests, and KNN. Learn how to measure model performance correctly. Tools: Scikit-Learn, Jupyter

Module 5 — System Design & Model Deployment Move beyond the notebook. Learn how to design data pipelines, deploy models as APIs, and package solutions using Docker. Build an end-to-end workflow from raw data ingestion to a live, usable product. Tools: Flask/FastAPI, Docker, GitHub

Capstone Project Complete an individual or team end-to-end data science project: select a real-world problem, process the data, build and evaluate a model, create a dashboard or visual report, and deploy it as a working application. This is your portfolio centrepiece.