Program Overview
Data science uses algorithms and scientific methods to make sense of complex data sets, and Python is the language most data scientists reach for first. This course focuses on applying Python to real data problems, moving from foundational models through to the core ideas behind machine learning and artificial intelligence.
You will study regression models — linear, multiple, and polynomial — alongside classification with kNN and logistic regression, working throughout with scikit-learn, Pandas, matplotlib, and NumPy. Along the way you will cover choosing the right model complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and evaluating how well a model actually performs.
What you’ll learn
- Hands-On Python — Practise using Python to solve real data science challenges
- Modelling & Statistics — Apply Python to modelling, statistics, and data storytelling
- Core Libraries — Work confidently with Pandas, NumPy, matplotlib, and scikit-learn
- Run & Evaluate Models — Build machine learning models, measure how they perform, and apply them to real-world problems
- Foundation for ML & AI — Build the Python grounding needed for further machine learning and AI study
Skills you’ll learn
- Data Science
- Machine Learning
- Python Programming
- Regression Models
- Classification Models
- Model Evaluation
- Algorithms
- Scientific Methods
Tools you’ll learn
- Python 3
- Pandas
- NumPy
- Matplotlib
- scikit-learn
- Jupyter Notebook
Comprehensive Curriculum
3 modules, 8 weeks of guided data science with Python, ending in a capstone project.
Key Topics
- Linear Regression
- Multiple and Polynomial Regression
Hands-on Projects
- Fit and interpret a linear regression model in Python
- Extend a model with multiple and polynomial terms
