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 |
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Year | CATS Level | Semester | CATS Value |
Session 2024-25 | 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 |
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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 |
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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 |
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(LO1) Students will be able to clean data and create new variables in R. |
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(LO2) Students will be able to create figures displaying data in R. |
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(LO3) Students will be able to discuss basic descriptive statistics and present them to others. |
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(LO4) Students will be able to summarise analyses in written reports. |
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(S1) Numeracy |
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(S2) IT Literacy |
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(S3) Verbal and written communication |
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(S4) Teamwork |
Teaching and Learning Strategies |
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20 hours lectures 5 hours tutorials 125 hours self-directed learning |
Syllabus |
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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 |
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Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. |