Unit 1: What is Data Science?

Overview

Data science sits at the intersection of statistics, computer science and domain expertise. This unit explains what that means in practice: what data scientists actually do, how the field evolved, and why organisations that make decisions from data consistently outperform those that do not.

You will meet the data science lifecycle — the sequence of stages from problem definition through to communication — which provides the structure for the entire course.

Learning outcomes

By the end of this unit you will be able to

Define data science and distinguish it from related disciplines.
Describe the stages of the data science lifecycle.
Identify the main roles within a data science team.
Give examples of data science applied in real organisations.
Explain what it means to be a data-driven organisation.

Lesson sequence

t-01

Course Introduction

What the course covers, how it is assessed and how to succeed in it.

t-02

Evolution of Data Science

From statistics and databases to the modern data science discipline.

t-03

Data Science Workflow

The lifecycle stages and where each one adds value.

t-04

Data Science Careers

Analyst, scientist, engineer, ML specialist — and how they differ.

t-05

Real-World Applications

Healthcare, finance, retail, government and scientific research.

t-06

Data-Driven Organizations

What distinguishes data-driven organisations from intuition-led ones.

t-07

Case Study

A detailed look at a real data science project end to end.

t-08

Knowledge Check

Self-check questions covering the unit's concepts.

t-09

Unit Summary

Consolidated key points and a forward look to Unit 2.

Key concepts

Data science: An interdisciplinary field that extracts knowledge and insight from data using methods from statistics, computer science and domain expertise.
Data science lifecycle: The sequence of stages — problem definition, acquisition, preparation, analysis, modelling, communication — through which a data science project progresses.
Data-driven decision making: Basing organisational decisions on evidence from data rather than intuition, experience or hierarchy alone.
Data science team roles: The complementary specialisms — analyst, data scientist, data engineer, ML engineer — that together deliver a data science project.

Assessment alignment

How this unit feeds the assignments

The lifecycle model introduced here structures every assignment in the course. Assignment 1 (Data Collection Project) explicitly requires you to place your work within the lifecycle, and the capstone requires you to work through every stage.