COMP326Data Science Foundations

Module 1 · 3 units · 27 tutorials · Weeks 1–3

Introduction to Data Science

What data science is, how it evolved, and the environment practitioners work in. Establish the vocabulary and mental models the rest of the course depends on.

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Module overview

What this module covers

Module 1 establishes the conceptual foundation for the entire course. It answers three questions: what is data science as a discipline, what distinguishes data from information from knowledge, and what tools and workflows do practitioners actually use?

The module is deliberately broad rather than deep. Its purpose is to give you the vocabulary and mental models that Modules 2–6 assume, and to motivate why the practical skills that follow matter.

No prior experience assumed

This module assumes no background in statistics, programming or data analysis. Everything is built from first principles.

At a glance

Units
3 (U01–U03)
Tutorials
27
Weeks
1–3
Assessment
3 unit quizzes · Assignment 1 released in Week 3
Key tools
Python, Jupyter, NumPy, pandas, Matplotlib, Seaborn

Learning outcomes

By the end of Module 1 you will be able to

Explain

  • The data science lifecycle and each stage's purpose.
  • The distinction between data, information, knowledge and wisdom.
  • Structured, semi-structured and unstructured data.

Describe

  • The main roles within a data science team.
  • How data-driven organisations differ from intuition-led ones.
  • The characteristics of big data.

Use

  • Jupyter notebooks for analysis and reporting.
  • NumPy, pandas, Matplotlib and Seaborn at a foundational level.
  • Sound project organisation for reproducible work.

Units

Three units in this module

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