ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Data Management and Visualisation for Economics
Code ECON705
Coordinator Dr C Cheang
Economics
C.Cheang@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 7 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 20

   5

       25
Timetable (if known)              
Private Study 125
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Open book examination There is a resit opportunity Standard UoL penalty applies for late submission This is an anonymous assessment  24    60       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Individual case study There is a resit opportunity Standard UoL penalty applies for late submission This is an anonymous assessment  0    20       
Group presentation There is a resit opportunity Standard UoL penalty applies for late submission This is not an anonymous assessment  20    20       

Aims

This module aims to:

Teach students the basics of coding in R;

Prepare students to enter the workplace and handle large datasets;

Provide students with a toolkit of techniques to transform raw data into usable and actionable insight;

Enable students to gain the confidence to describe their work process and their results to people through a written report and presentations.

Learning Outcomes

(LO1) Students will be able to clean data and create new variables in R.

(LO2) Students will be able to create figures displaying data in R.

(LO3) Students will be able to discuss basic descriptive statistics and present them to others.

(LO4) Students will be able to summarise analyses in written reports.

(S1) Numeracy
Students will be taught how to calculate descriptive statistics using R.

(S2) IT Literacy
Students will gain experience producing tables and figures visualising data in R.

(S3) Verbal and written communication
Students will develop these skills by preparing for the assessments (group presentations and written case study).

(S4) Teamwork
Students will gain this skill through the group work needed to complete the presentation.

Teaching and Learning Strategies

20 hours lectures
There will be one 2-hour lecture per week for 10 weeks.

5 hours tutorials
There will be one 1-hour tutorial every other week.

125 hours self-directed learning
Students will work in groups and on their own to practice their coding skills in R completing problem sets and practice problems designed to refine their skills and complement their coursework.

Syllabus
 

This module will cover the basics of coding in R and apply this to constructing datasets, calculating descriptive statistics, and creating data visualisations. The following topics will be covered:

1. Introduction to coding in R;

2. Transforming, summarising, and analysing data;

3. Exploratory data analysis;

4. Working with ggplot;

5. Creating maps.

Other topics connecting this module to econometrics and statistics will be covered as time permits, such as regression models.

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.