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.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
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Candidates must enroll on exactly 180 or 185 credits overall, with at least 135 credits at level 5M. Please note that in order to obtain the MSc award students need to pass 150 credits with at least 135 at level 5 with a minimum classification average of 5.0 . Please refer to the 'rules of award' document for further details, with particular attention to section 16.
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).
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| COMP5122M | Data Science | 15 | Semester 1 (Sep to Jan) | |
| MATH5747M | Learning Skills through Case Studies | 15 | Semester 2 (Jan to Jun) | |
| MATH5872M | Dissertation in Data Science and Analytics | 60 | 1 Jun to 30 Sep, 1 Jun to 30 Sep | PFP |
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.
List A
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| COMP3736 | Information Visualization | 10 | Semester 1 (Sep to Jan) | |
| COMP5450M | Knowledge Representation and Reasoning | 15 | Semester 1 (Sep to Jan) | |
| COMP5611M | Machine Learning | 15 | Semester 2 (Jan to Jun) | |
| COMP5625M | Deep Learning | 15 | Semester 2 (Jan to Jun) | |
| COMP5712M | Programming for Data Science | 15 | Semester 1 (Sep to Jan) | |
| COMP5840M | Data Mining and Text Analytics | 15 | Semester 2 (Jan to Jun) |
List B
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH3092 | Mixed Models | 10 | Semester 2 (Jan to Jun) | |
| MATH3714 | Linear Regression and Robustness | 15 | Semester 1 (Sep to Jan) | |
| MATH3723 | Statistical Theory | 15 | Semester 2 (Jan to Jun) | |
| MATH3802 | Time Series | 10 | Semester 1 (Sep to Jan), Semester 1 (Sep to Jan) | |
| MATH3823 | Generalised Linear Models | 10 | Semester 2 (Jan to Jun) | |
| MATH5092M | Mixed Models with Medical Applications | 15 | Semester 2 (Jan to Jun) | |
| MATH5714M | Linear Regression, Robustness and Smoothing | 20 | Semester 1 (Sep to Jan) | |
| MATH5741M | Statistical Theory and Methods | 15 | Semester 1 (Sep to Jan) | |
| MATH5743M | Statistical Learning | 15 | Semester 2 (Jan to Jun) | |
| MATH5745M | Multivariate Methods | 15 | Semester 2 (Jan to Jun) | |
| MATH5772M | Multivariate and Cluster Analysis | 15 | Semester 1 (Sep to Jan) | |
| MATH5802M | Time Series and Spectral Analysis | 15 | Semester 1 (Sep to Jan) | |
| MATH5824M | Generalised Linear and Additive Models | 15 | Semester 2 (Jan to Jun) | |
| MATH5835M | Statistical Computing | 15 | Semester 1 (Sep to Jan) |
List C
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| GEOG5042M | Geographic Data Visualisation & Analysis | 15 | Semester 1 (Sep to Jan) | |
| GEOG5255M | Geodemographics and Neighbourhood Analysis | 15 | Semester 2 (Jan to Jun) | |
| GEOG5917M | Big Data and Consumer Analytics | 15 | Semester 2 (Jan to Jun) | |
| GEOG5927M | Predictive Analytics | 15 | Semester 2 (Jan to Jun) | |
| GEOG5937M | Applied GIS and Retail Modelling | 15 | Semester 1 (Sep to Jan) | |
| LUBS5308M | Business Analytics and Decision Science | 15 | Semester 1 (Sep to Jan) | |
| LUBS5309M | Forecasting and Advanced Business Analytics | 15 | Semester 2 (Jan to Jun) | |
| LUBS5990M | Machine Learning in Practice | 15 | Semester 2 (Jan to Jun) | |
| TRAN5340M | Transport Data Science | 15 | Semester 2 (Jan to Jun) |
Last updated: 29/04/2024 16:07:40
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