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POL42540

Academic Year 2024/2025

Applied Data Wrangling and Visualisation (POL42540)

Subject:
Politics
College:
Social Sciences & Law
School:
Politics & Int Relations
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Stefan Muller
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Applied Data Wrangling and Visualisation offers a comprehensive introduction to the essential techniques and tools required for effective data management and visualisation in R. Students will also learn how to use AI tools as coding assistants, manage projects and handle data in various file formats, ensuring a robust understanding of data cleaning, wrangling, and merging.

The course emphasises the fundamentals of data visualisation, moving from principles to applied data visualisation strategies for compelling data storytelling. Additionally, it delves into the use of relational databases with SQL and data collection through web scraping, enabling students to manage and analyse large datasets efficiently. Applied Data Wrangling and Visualisation prepares module participants for a range of data-intensive roles, equipping them with the knowledge to leverage data visualisation and management tools effectively in their future careers.

About this Module

Learning Outcomes:

Learning Outcomes
• Acquire the ability to manage and visualise data effectively in R, using RStudio and GitHub, enhancing your proficiency in handling data in diverse file formats.
• Use relational databases (SQL) and conduct data collection through web scraping, enabling you to analyse large datasets for a range of data-intensive roles.
• Develop skills in using AI tools as coding assistants, fostering your ability to manage projects efficiently and improve replicability in their work.
• Robust understanding of data cleaning, wrangling, and merging techniques, preparing you for the challenges of managing large and complex datasets.
• Solid foundation in the principles of data visualisation, learning to apply these strategies to create compelling data stories that are both intuitive and accessible.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Autonomous Student Learning

100
Total

124

Approaches to Teaching and Learning:
Active/task-based learning;
Peer and group work;
Lectures;
Lab/studio work;
Enquiry & problem-based learning;
Case-based learning

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Multiple Choice Questionnaire (R, AI, and Data Visualisation) Week 7 Graded No
25
 No
Exam (In-person): Multiple Choice Questionnaire (SQL, Data Visualisation, Webscraping) Week 11 Graded No
25
 No
Group Work Assignment: Data Report: Descriptive analysis and visual representation of insights derived from a large dataset (Group Project) Week 12 Graded No
50
 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
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Informal feedback on programming tasks will be provided throughout, both instructor- and peer-lead. Feedback on quizzes will be automatic. Feedback on reflective essays will be provided within 20 working days from submission, in writing.

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, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Thurs 09:00 - 10:50
Autumn Lecture Offering 1 Week(s) - 2 Thurs 09:00 - 10:50
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