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ECON30520

Academic Year 2024/2025

R for Economists (ECON30520)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Professor Morgan Kelly
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The purpose of the course is to introduce you to data analysis using the powerful and increasingly popular R language. The hope is that by the end you will have learned enough to carry out basic statistical projects. The course will fall roughly into three parts. The first will be how to enter and manipulate data in R. The second is how to present data and results graphically. The third part is In effect an applied econometrics module where we will run through a variety of useful techniques, showing how they can be computed in R. Alongside traditional regression techniques we will devote some time to machine learning. The emphasis throughout the course will be on taking real world data and figuring out how to analyse it in a sensible way and how to present the results of our analysis in an easily understandable way.

About this Module

Learning Outcomes:

By the end of the course you should have enough grasp of R to complete a simple statistical project: tidying your data, graphing it and analysing it econometrically.

Indicative Module Content:

1. Tidying data.
2. Graphing data.
3. Ordinary least squares.
4. Time series.
5. Other regression techniques.
6. Basic machine learning.

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

76
Lectures

24
Total

100

Approaches to Teaching and Learning:
The emphasis is on completing practical problem sets, analysing data in the ways that we discuss as we go along

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
STAT40620 - Data Programming with R


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: You will be required to apply the methods in the course to write a short project analyzing a set of data that you will be assigned. Week 15 Standard conversion grade scale 40% No
100
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 15:00 - 15:50
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Wed 11:00 - 11:50
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