Postgraduate Programme and Module Handbook 2024-2025
Module PSYC42730: Applied Data Science
Department: Psychology
PSYC42730:
Applied Data Science
| Type |
Tied |
Level |
4 |
Credits |
30 |
Availability |
Not available in 2024/2025 |
Module Cap |
None. |
Prerequisites
Corequisites
Excluded Combination of Modules
Aims
- To acquire knowledge about data science.
- To develop an understanding of the suitability of different statistical techniques to address different research questions.
Content
- This module provides an introduction to data science in the context of behavioural science. It develops and extends students’ knowledge of statistical analyses and explore applications to behavioural science problems. Illustrative topics:
- Introduction to statistics
- Data integrity
- Open Science practices
- Basic and advanced inferential statistics
- Big data
- Data visualisation
- Ethical practice
- Producing research and business reports
Learning Outcomes
- Range of widely-used statistical tests
- Importance of the role of statistics in any successful data analysis
- Limitations of the techniques covered
- Advantages and limitations of statistical software
- The ability to select appropriate statistical techniques to address specific research problems.
- The ability to write up concise research reports.
- The ability to communicate research findings to a lay audience.
- Good written and oral communication skills
- Good IT skills in word processing, data manipulation and data presentation
- Ability to work independently in scholarship and research within broad guidelines
Modes of Teaching, Learning and Assessment and how these contribute to
the learning outcomes of the module
- This module will be delivered by the Department of Psychology
- This module will follow a online learning approach, including 12 hours of live webinars and lectures, which will be recorded for students who cannot attend due to work commitments.
- In the workshops students will have hands on training in data analysis using widely available statistical packages capable of executing sufficiently complex analysis.
- Students will have access to online discussion boards.
- Formative student assessments will be undertaken throughout the duration of the module.
- This module is assessed summatively through a written report.
Teaching Methods and Learning Hours
| Activity |
Number |
Frequency |
Duration |
Total/Hours |
|
| Online Lecture |
15 |
Outside of in-person block teaching |
24 minutes |
6 |
|
| Live webinar |
6 |
6 times outside of in-person block teaching |
1 hour |
6 |
|
| Asynchronous activities: Online teaching, discussion forum, other taught activities |
|
|
|
32 |
|
| Asynchronous activities: Independent study and assessment preparation |
|
|
|
256 |
|
|
|
|
|
300 |
|
Summative Assessment
| Component: Report |
Component Weighting: 100% |
| Element |
Length / duration |
Element Weighting |
Resit Opportunity |
| Assignment - (analysis of specified data sets: production of a report) |
3000 words maximum |
100% |
yes |
Formative student assessments will be undertaken throughout the duration of the module. These will be assessed by the tutor to enable students to gauge their own individual rate of progress.
â– Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University
