EH326-7-SU-CO:
Data Visualisation with R: Explore, Model and Communicate Social Data Analysis
2023/24
Essex Summer School in Social Science Data Analysis
Colchester Campus
Summer
Postgraduate: Level 7
Current
Monday 22 April 2024
Friday 28 June 2024
30
31 March 2021
Requisites for this module
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In modern data analysis, graphics and computational statistics are increasingly used together to explore and identify complex patterns in data and to make and communicate claims under uncertainty.
This course will go beyond traditional ideas of charts, graphs, maps (and also statistics!) to equip you with the critical analysis, design and technical skills to analyse and communicate with social science datasets.
The course emphasises real-world applications. You will work with both new, large-scale behavioural datasets, as well as more traditional, administrative datasets located within various social science domains: Political Science, Crime Science, Urban and Transport Planning.
As well as learning how to use graphics and statistics to explore patterns in these data, implementing recent ideas from data journalism you will learn how to communicate research findings – how to tell stories with data.
No information available.
On successful completion of the module, students will:
1. Have an advanced understanding of computational methods for social data science, sufficient for producing publishable work in the social sciences
2. Have experience and skills using computational methods in a range of practical applications
3. Be able to apply their knowledge of computational methods to their own project
Module information will be made available at https://essexsummerschool.com/.
Please contact essexsummerschoolssda@essex.ac.uk and govpgquery@essex.ac.uk with any queries.
The following learning and teaching methods will inform the pedagogic process of the course:
The lectures will introduce the key theoretical concepts of the social science research methods. Foundations of the social science methods and instructions on how to apply them for solving research problems will be established through the lectures (Learning outcome 1 and 3). The lectures will also equip students with a critical understanding of the strengths and weakness of the specific techniques, directing them to the most appropriate approach for various contexts.
The labs will focus on practical applications of the methods taught in the lectures. For each topic, relevant applications will be practiced in the lab sessions, which will help students to acquire the skills for implementing the methods and solving problems (Learning outcome 2 and 3).
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
No
No
No
Available via Moodle
No lecture recording information available for this module.
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