EC349: Data Science for Economists
Deborah Kim
Module Leader
Introduction
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 Aims
The module will introduce students to the meaning of data science, working practically with data in R. Students will learn how to source, manipulate and analyse large data flows, extract knowledge and insights from large, noisy data, and understand how to use these data types to answer certain economics questions. Students will learn to apply data science theorems and algorithms to solve problems using the most suitable software and statistical tools for data processing.
Principal Learning Outcomes
.....understand and apply a range of supervised machine learning techniques—such as LASSO, random forests, boosting, and feedforward neural networks—and use causal inference methods to analyse economic questions. Students will also be able to implement these methods in practice by preparing data, building models, and conducting causal analysis using statistical software such as R or Python.
Syllabus
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.
Context
- Optional Module
- L100 - Year 3, L116 - Year 3, LM1D (LLD2) - Year 3, L103 - Year 4, L117 - Year 4, LM1H - Year 4
- Pre or Co-requisites
EC203-30 Applied Econometrics OR
EC226-30 Econometrics 1
Summary:Modules: EC203-30 or EC226-30
Assessment
- Assessment Method
- Coursework (40%) + Centrally-timetabled examination (On-campus) (60%)
- Coursework Details
- Centrally-timetabled examination (On-campus) (60%) , Individual Project (40%)
- Exam Timing
- Summer
Subject Specific Skills
- Applied Economics
- Economic information
- Research and debate
- Analytical reasoning
- Analytical thinking and communication
- Creative thinking
- Critical thinking
- Policy evaluation
- Strategic thinking
Transferable Skills
- Data-based skills
- IT skills
- Numeracy and quantitative skills
- Research skills
- Information technology
- Math, Statistical, data-based research skills
- Written communication
- Coding Skills (Python)
Exam Rubric
Time Allowed: 2 Hours
Read all instructions carefully - and read through the entire paper at least once before you start entering your answers.
There are TWO sections in this paper. Answer ALL FOUR questions in Section A (15 marks each) and TWO questions in Section B (20 marks each).
Use a separate booklet for each Section.
• Use a PINK booklet for Section A questions.
• Use a SEPARATE PINK booklet for Section B questions.
You must write the number(s) of the question(s) you have answered on the front cover of each booklet. Make sure the numbers are clearly visible and correspond to the questions you completed inside that booklet.
Do not submit answers to more than the required number of questions. If you do, only the first answers (in the order they appear) will be marked, up to the required number for each section.
Approved scientific (non-graphical) pocket calculator are allowed.
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.