2020/21 Taught Postgraduate Programme Catalogue
MSc Data Science and Analytics
| Programme code: | MSC-DS&A | UCAS code: | |
|---|---|---|---|
| Duration: | 12 Months | Method of Attendance: | Full Time |
| Programme manager: | Dr Luisa Cutillo | Contact address: | L.Cutillo@leeds.ac.uk |
Total credits: 180
Entry requirements:
BSc (or equivalent) in a subject containing a substantial numerate component, usually at level 2.1 or above (or equivalent).
School/Unit responsible for the parenting of students and programme:
School of Mathematics
Examination board through which the programme will be considered:
School of Mathematics
Programme specification:
The programme will equip students with the necessary knowledge and skills in data science. Students on this programme will be taught by experts from different academic units: the School of Mathematics (SoM), the School of Computing (SoC), the School of Geography (SoG), and the School of Business (LUBS). In addition to that, three new modules in total are proposed in the SoM for students who are not from a mathematics/statistics background, while modules in the SoC will be suitable for students on this programme who are not from a computer science background. The programme will therefore expose students to different perspectives on data science, including the mathematical and computational underpinnings of the subject and practical understanding of application in a specific context. In particular, we anticipate many projects for the dissertation will span at least two units with joint supervision. As well as emphasizing the application nature of the programme, the dissertation will feature strongly data elucidation, analysis, and interpretation of real-world problems.
Year1 - View timetable
[Learning Outcomes, Transferable (Key) Skills, Assessment]
Candidates must enrol on exactly 180 or 185 credits overall, with at least 135 credits at level 5M. Please note that students must pass 135 credits at level 5M to gain the MSc.
Students will be awarded the PGCert if they exit with 60 credits (including 45 at Level 5M), or the PGDip if they exit with 90 credits (including 75 at Level 5M).
Compulsory modules:
Candidates will be required to study the following compulsory modules:
| COMP5122M | Data Science | 15 credits | Semester 2 (Jan to Jun), 1 Sep to 31 Jan (adv yr), 1 Jun to 30 Sep | |
| MATH5747M | Learning Skills through Case Studies | 15 credits | 1 Jan to 31 May, 1 May to 30 Sep | |
| MATH5872M | Dissertation in Data Science and Analytics | 60 credits | 1 Aug to 31 Oct, 1 Jun to 30 Sep |
Optional modules:
Remaining credits need to be chosen from the following lists, with at least 30 credits from each of lists A and B. Options may be selected from list C. The final choice requires approval from the Programme Manager.
| COMP3736 | Information Visualization | 10 credits | Semester 1 (Sep to Jan) | |
| COMP5611M | Machine Learning | 15 credits | Semester 1 (Sep to Jan), 1 Sep to 31 Jan (adv yr) | |
| COMP5623M | Artificial Intelligence | 15 credits | Semester 2 (Jan to Jun) | |
| COMP5712M | Programming for Data Science | 15 credits | 1 Jan to 31 May, Semester 1 (Sep to Jan), 1 Jun to 30 Sep, 1 Sep to 31 Jan (adv yr) | |
| COMP5840M | Data Mining and Text Analytics | 15 credits | Semester 2 (Jan to Jun) | |
| GEOG5042M | Geographic Data Visualisation & Analysis | 15 credits | 1 Jan to 31 May | |
| GEOG5937M | Applied GIS and Retail Modelling | 15 credits | 1 Jan to 31 May | |
| LUBS5308M | Business Analytics and Decision Science | 15 credits | 1 Jan to 31 May | |
| MATH2775 | Survival Analysis | 10 credits | Semester 2 (Jan to Jun) | |
| MATH3092 | Mixed Models | 10 credits | Semester 2 (Jan to Jun), 1 Jan to 31 May | |
| MATH3714 | Linear Regression and Robustness | 15 credits | Semester 1 (Sep to Jan) | |
| MATH3723 | Statistical Theory | 15 credits | Semester 1 (Sep to Jan) | |
| MATH3802 | Time Series | 10 credits | 1 Sep to 31 Dec (adv yr), 1 Jan to 31 May | |
| MATH3823 | Generalised Linear Models | 10 credits | 1 Jan to 31 May | |
| MATH5092M | Mixed Models with Medical Applications | 15 credits | 1 Jan to 31 May | |
| MATH5714M | Linear Regression, Robustness and Smoothing | 20 credits | Semester 1 (Sep to Jan), 1 May to 30 Sep | |
| MATH5741M | Statistical Theory and Methods | 15 credits | Semester 1 (Sep to Jan), 1 Jan to 31 May | |
| MATH5743M | Statistical Learning | 15 credits | 1 Jan to 31 May, 1 May to 30 Sep | |
| MATH5745M | Multivariate Methods | 15 credits | 1 Jan to 31 May | |
| MATH5772M | Multivariate and Cluster Analysis | 15 credits | 1 May to 30 Sep, Semester 1 (Sep to Jan) | |
| MATH5802M | Time Series and Spectral Analysis | 15 credits | 1 Jan to 31 May, 1 May to 30 Sep | |
| MATH5824M | Generalised Linear and Additive Models | 15 credits | 1 Jan to 31 May | |
| MATH5835M | Statistical Computing | 15 credits | Semester 1 (Sep to Jan), 1 May to 30 Sep | |
| TRAN5340M | Transport Data Science | 15 credits | Semester 2 (Jan to Jun) | |
| YCHI5010M | Informatics in Health Care | 15 credits | Semester 1 (Sep to Jan) |
Last updated: 24/05/2021 09:54:00
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