Skip to main content Skip to navigation

EC349: Data Science for Economists

  • Nathan Canen

    Module Leader
  • Love Idahosa

    Module Lecturer
15 CATS - Department of Economics
Summer Module
Autumn Module

Principal Aims

This introductory data science module will introduce core economics students to a wide array of data sources and types and how to work with them. It is intended to provide students with foundation data science skills, working in R.

Principal Learning Outcomes

Students will have an understanding of the data science methodology, the various data science tools available, and how to answer economic questions using various data types.

Students will learn to source for non-economic data, clean, manipulate, visualise, and analyse these data using programming techniques in the relevant software package (R) for real-world inspired scenarios.


Topics typically could include, but are not limited to:

1. Introduction: Defining data science, what data scientists do, the data they use, and the limitations of data science.

2. The data science methodology (E.g., CRISM-DM, TDSP, Domino, etc.)

3. Data sources and types – rectangular vs non-rectangular data (e.g., Textual data, multimedia data, spatial-temporal data, click stream data, etc.).

4. Working with data in R

5. Data extraction and acquisition

6. Getting data into shape (mining, wrangling and manipulation)

7. Statistical methods with big data

8. Data visualisation and analysis

9. AI Applications in Data Science (E.g., Supervised Machine learning, Unsupervised Machine Learning, Deep learning, etc.).

10. Data science tools: (E.g., Working with Git, RStudio, Tidyverse, etc.)

11. Data science application in economic analysis – Literature evidence.


Optional Module
LM1D (LLD2) - Year 3, LM1D (LLD2) - Year 4
Pre or Co-requisites
Modules: EC203-30 or EC226-30


Assessment Method
Coursework (40%) + Final Exam (60%)
Coursework Details
Final Exam (60%) , Individual Project (40%)
Exam Timing

Exam Rubric

Time Allowed: 2 hours

Answer ALL questions in Section A (60 marks in total) and TWO questions in Section B (20 marks each).

Approved pocket calculators are allowed.

You should not submit answers to more than the required number of questions. If you do, we will mark the questions in the order that they appear, up to the required number of questions in each section.

Previous exam papers can be found in the University’s past papers archive. Please note that previous exam papers may not have operated under the same exam rubric or assessment weightings as those for the current academic year. The content of past papers may also be different.