Mathematics for Data Science
Every data scientist needs the same foundation: the statistics, probability and calculus intuition that every machine learning technique is built on. This module builds that mathematical fluency before any coding or modelling begins.
What You Will Learn
A detailed, industry-aligned breakdown of every topic covered in this module.
- Linear algebra: vectors & matrices
- Probability & statistics fundamentals
- Descriptive statistics & distributions
- Hypothesis testing & confidence intervals
- Correlation & regression basics
- Calculus for ML: derivatives & gradients
- Combinatorics & probability theory
- Statistical inference for data decisions
Tools You Will Use
Hands-on time with the same tools used by working data analysts and data scientists today.
NumPy
Numerical computing library used for array operations and mathematical computation in data science.
SciPy
Scientific computing library used for statistics, optimization and advanced mathematical functions.
Excel
Spreadsheet tool used for quick data analysis, calculations and lightweight reporting.
Google Sheets
Cloud-based spreadsheet tool used for collaborative data analysis and quick calculations.
Hands-On Labs
Production-style data science lab scenarios, built using real, messy datasets.
Compute descriptive statistics and visualise distributions for a real dataset.
Run a hypothesis test and interpret a confidence interval on sample data.
Calculate correlation and fit a basic regression line between two variables.
Work through gradient and derivative calculations behind a simple ML update step.
Apply combinatorics and probability theory to a real decision-making scenario.
Assessment
Knowledge Assessment
Quiz covering probability, statistical inference, correlation/regression basics and calculus for ML.
Practical Evaluation
Students must compute and interpret descriptive statistics, a hypothesis test and a regression fit for an assigned dataset.
Projects
Industry-style deliverables added directly to your project portfolio.
Statistical Analysis Brief
Apply descriptive statistics, hypothesis testing and correlation analysis to a real dataset and document the findings.
What This Module Builds
Students build the mathematical and statistical intuition that underpins every data science and machine learning technique used later in the program.