Python for Data Science & Machine Learning Bootcamp

What you'll learn

• Load, clean, and reshape real datasets with Pandas and NumPy until they're analysis-ready
• Explore data and communicate findings with Matplotlib and Seaborn visualisations
• Train regression and classification models with scikit-learn and read their results honestly
• Evaluate models properly using train/test splits, cross-validation, and the right metrics
• Engineer features and tune models to squeeze out real, measurable improvements
• Build an end-to-end ML project you can put on your resume and explain in interviews

About this course

This is the practical, no-hand-waving path into data science with Python. Instead of memorising library calls, you'll work with real, messy datasets and learn the judgement that separates someone who can *run* a model from someone who can *trust* one. You'll start by getting genuinely fluent in Pandas and NumPy — indexing, grouping, joining, and cleaning data until it's ready to analyse. Then you'll explore and communicate what you find with Matplotlib and Seaborn. From there you move into machine learning with scikit-learn: regression, classification, and clustering, always paired with the crucial skill of *evaluating* a model correctly — train/test discipline, cross-validation, and choosing metrics that actually reflect the problem. You'll engineer features, handle imbalanced data, and tune models, then tie it all together in a complete project from raw CSV to a validated, explainable result. You should know basic Python; everything data-specific is taught from the ground up. By the end you'll have real, defensible data-science skills and a portfolio project to prove them.