COMP326Data Science Foundations

Module 2 · 3 units · 25 tutorials · Weeks 4–6

Data Collection and Preparation

Finding, acquiring, cleaning and transforming data that is fit for analysis. The unglamorous work that determines whether every later result can be trusted.

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Module overview

What this module covers

Module 2 is where data science becomes practical. You learn how to source data from files, APIs, databases and open repositories; how to diagnose and repair quality problems; and how to transform raw data into a form suitable for analysis.

The focus throughout is judgement: knowing which cleaning decisions are defensible, when to discard data and when to repair it, and how to document every choice so that your analysis can be reproduced.

The 80% reality

Data professionals routinely spend 60–80% of their time on collection and preparation. This module reflects that reality rather than hiding it.

At a glance

Units
3 (U04–U06)
Tutorials
25
Weeks
4–6
Assessment
3 unit quizzes · Assignment 1 due Week 6 · Assignment 2 released Week 6
Key tools
pandas, requests, JSON, CSV, SQL basics

Learning outcomes

By the end of Module 2 you will be able to

Acquire

  • Identify appropriate data sources for a given question.
  • Retrieve data from files, APIs and open repositories.
  • Read and integrate CSV, JSON and tabular formats.

Clean

  • Diagnose missing, duplicate and inconsistent data.
  • Detect and handle outliers defensibly.
  • Document every cleaning decision and its rationale.

Transform

  • Normalise and standardise numeric features.
  • Encode categorical variables.
  • Aggregate, group and engineer new features.

Units

Three units in this module

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