Data Science
Statistics, Python and machine-learning fundamentals — the full path from raw data to a working model.
Why learn data science?
Data science sits a step beyond analytics — instead of just reporting on what happened, you build models that explain patterns and make predictions. It's one of the fastest-growing fields in tech, and Python is the standard tool for it.
- Builds on analytics — if you've done our Data Analytics or Python course, this is the natural next step.
- Broad and in-demand — used across finance, healthcare, e-commerce, logistics and more.
- Hands-on with real data — every module works with actual datasets, not toy examples.
- A path into Machine Learning — this course lays the foundation our Machine Learning course builds on.
Before you start: basic Python (covered in our Python course) will help you move faster here.
Data science syllabus
Six modules, from statistics to your first machine-learning model.
Module 01
Statistics & probability foundations
- Descriptive statistics
- Probability basics
- Distributions and sampling
Module 02
Data wrangling & cleaning
- Handling missing data
- Dealing with outliers
- Reshaping messy real-world data
Module 03
Exploratory data analysis
- Finding patterns and relationships
- Visual exploration with Python
Module 04
Introduction to machine learning concepts
- Supervised vs unsupervised learning
- The model-building workflow
Module 05
Model building basics
- Regression with scikit-learn
- Classification with scikit-learn
- Evaluating a simple model
Module 06
Capstone project & storytelling
- Working a real dataset end to end
- Communicating findings clearly
Going further
Want to specialise in modelling and algorithms specifically? Our Machine Learning course goes deeper into everything introduced here.