COMP326Data Science Foundations

Module 4 · 3 units · 25 tutorials · Weeks 9–11

Statistical Foundations for Data Science

Probability, sampling, inference and the reasoning behind statistical claims. How to move from describing what you observed to reasoning about what it means.

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Module overview

What this module covers

Module 4 introduces the statistical reasoning that separates descriptive analysis from inference. You learn how probability models uncertainty, how sampling allows conclusions about populations, and how hypothesis testing provides a framework for evidence.

The module closes with regression treated as a statistical technique for describing relationships — not as a machine learning method. Predictive modelling is introduced in COMP328.

Statistical regression only

This is statistical regression for inference and description. Machine learning regression is deliberately deferred to COMP328.

At a glance

Units
3 (U10–U12)
Tutorials
25
Weeks
9–11
Assessment
3 unit quizzes · Assignment 3 due Week 11 · Assignment 4 released Week 11
Key tools
NumPy, SciPy, statsmodels, pandas

Learning outcomes

By the end of Module 4 you will be able to

Reason probabilistically

  • Apply the rules of probability.
  • Work with common distributions.
  • Update beliefs using conditional probability and Bayes' theorem.

Infer

  • Explain sampling variability and the central limit theorem.
  • Construct and interpret confidence intervals.
  • Conduct hypothesis tests and interpret p-values correctly.

Model relationships

  • Distinguish correlation from causation.
  • Fit and interpret simple and multiple linear regression.
  • Evaluate and communicate model assumptions.

Units

Three units in this module

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