Human and Social Data Science
(MSc) Human and Social Data Science
Entry for 2023
FHEQ level
This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.
Course Outline (May Be Used in Prospectus)
Students attend a series of seminars (11) from (mainly) external speakers, where they are exposed to the problems that data scientists have to face in an academic and non-academic environment. Seminars are structured in such a way to have a 50 minutes talk from an expert in data science or in topics related to data science (data preservation, laws on data science/big data, etc), followed by a 1 hour Q&A session, where students are invited to discuss with the expert. Seminar speakers are invited to discuss real-life cases where the expertise of data scientists has been crucial, and to discuss how students can ‘market’ what they have learned in the Data Science MSc to external employers.
The assessment for this module is a 3000 word essay (to be submitted in A2) on a data science topic that the students are encouraged to decide and research independently. Guidance is given at the start of the module on possible suitable projects. Students have to submit an abstract of their essay to the module convener by the end of Week 4 in the Spring term. Essays are marked by the module convener, second marked by a separate colleague in MPS, and finally the marks are moderated by a third colleague. The resit mode for this module consists of re-submitting in A4 an improved essay following the feedback received by module convener and second marker in A2.
Course learning outcomes
systematically understand key aspects of the foundations of human and social data science in mathematics including 1) basic probability theory and statistics, 2) basic linear algebra and 3) basic calculus
systematically understand key aspects of the foundations of human and data science in computer science including 1) computer programming, 2) data infrastructure and 3) machine learning
demonstrate critical awareness of the chosen domain in human and social data science including 1) the data that might be acquired and 2) the problems that might be solved with data science
demonstrate critical awareness of the ethical implications of the application of data science methods 1) generally and 2) more specifically in the chosen domain of human and social data science
develop and critically assess mathematical models for the analysis of large data sets
analyse, interpret and critically review the current research literature in the relevant domain of human and social data science
demonstrate self-direction and originality in project design and analysis
propose and evaluate hypotheses involving the real-life interpretation of numerical and graphical model analysis
demonstrate practical skills in computer programming
deploy, assess and develop data analysis tools
demonstrate a range of transferable communication skills including 1) oral presentation on a range of platforms and 2) scientifical and technical writing skills
effectively communicate scientific results on large data sets to experts and wider audiences both orally and in writing
Full-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Data Science Research Methods (970G1) | 15 | 7 |
| Core | Mathematics and Computational Methods for Complex Systems (817G5) | 15 | 7 | ||
| Core | Systems for Information Management (976G5) | 15 | 7 | ||
| Option | Applied Natural Language Processing (955G5) | 15 | 7 | ||
| Digital Journalism (007P3) | 15 | 7 | |||
| Media, Law and Ethics (899P4) | 15 | 7 | |||
| Policy Making and Policy Analysis (962N1) | 15 | 7 | |||
| Programming through Python (823G5) | 15 | 7 | |||
| Spring Semester | Core | Data Science Masters Research Proposal (806G1) | 15 | 7 | |
| Core | Machine Learning (934G5) | 15 | 7 | ||
| Core | Wider Topics in Data Science (905F3) | 15 | 7 | ||
| Option | Artificial Intelligence and Policies for Technological Revolutions (996N1) | 15 | 7 | ||
| Industrial and Innovation Policy (984N1) | 15 | 7 | |||
| New Developments in Digital Media (804P4B) | 30 | 7 | |||
| Techno-Feminism History and Practice (P5095) | 30 | 7 |
Part-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Systems for Information Management (976G5) | 15 | 7 |
| Option | Applied Natural Language Processing (955G5) | 15 | 7 | ||
| Digital Journalism (007P3) | 15 | 7 | |||
| Media, Law and Ethics (899P4) | 15 | 7 | |||
| Policy Making and Policy Analysis (962N1) | 15 | 7 | |||
| Programming through Python (823G5) | 15 | 7 | |||
| Spring Semester | Core | Wider Topics in Data Science (905F3) | 15 | 7 | |
| Option | Artificial Intelligence and Policies for Technological Revolutions (996N1) | 15 | 7 | ||
| Industrial and Innovation Policy (984N1) | 15 | 7 | |||
| New Developments in Digital Media (804P4B) | 30 | 7 | |||
| Race, Culture and the Media (881P4) | 30 | 7 | |||
| Techno-Feminism History and Practice (P5095) | 30 | 7 | |||
| Year | Term | Status | Module | Credits | FHEQ level |
| 2 | Autumn Semester | Core | Data Science Research Methods (L7) (970G1) | 15 | 7 |
| Core | Mathematics and Computational Methods for Complex Systems (817G5) | 15 | 7 | ||
| Spring Semester | Core | Data Science Masters Research Proposal (806G1) | 15 | 7 | |
| Core | Machine Learning (934G5) | 15 | 7 |
Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.
The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.