2024/25 Taught Postgraduate Programme Catalogue
MSc Data Science (Statistics) (online)
Programme code: | MSC-DSS-OD | UCAS code: | |
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Duration: | 24 Months | Method of Attendance: | Part Time |
Programme manager: | Dr Graham Murphy | Contact address: | G.J.Murphy@leeds.ac.uk |
100% online
Total credits: 180
Entry requirements:
Either a 2:1 Bachelor of Science honours degree. Transcripts should show evidence of at least 5 undergraduate modules in a combination of mathematics and statistics. At least one module should be in Statistics, and all modules should be across at least 2 years of your previous study.
Or a 3rd class (or higher) BSc (Hons) degree in any subject as well as successful completion of 2 x 15-credit Data Science (Statistics) modules. If you do not achieve a pass (50% weighted average or higher) in both of the first two modules, you will not be able to continue and will be withdrawn from the degree.
English Language requirements:
All applicants will need to have GCSE English Language at grade C or above, or an appropriate English language qualification.
Students for whom English is not their first language must meet the University of Leeds entry criteria for English language, IELTS 6.5 overall, with no less than 6.0 in any component.
TOEFL iBT (Test of English as a Foreign Language Internet-Based Test) or TOEFL iBT Home Edition at 88 overall with no less than 19 in listening, 20 in reading, 22 in speaking and 21 in writing. Please note that we do not accept TOEFL MyBest scores and expect candidates to have met the relevant requirements from a single TOEFL test.
Other accepted minimum qualifications for English Language skills are outlined in the current Taught Postgraduate Admissions policy.
Where necessary, students will be expected to improve their English further during the programme by making use of the online English tuition support that we provide.
School/Unit responsible for the parenting of students and programme:
Digital Education Service
Examination board through which the programme will be considered:
Digital Education Service
Programme specification:
The MSc Data Science (Statistics) is the result of a partnership between leading academics in the School of Mathematics and the Leeds Institute for Data Analytics (LIDA). The programme provides students with theoretical knowledge and practical skills desirable in a range of sectors where understanding of data analytics and statistical techniques are central.
The programme will provide students with a solid foundation in key topics in data science, including programming, statistical learning and machine learning as well as re-enforcing key concepts in statistics. Students will then explore more advanced topics in statistics such as multivariate statistics, linear models, Bayesian statistics and statistical computing. In each topic there will be opportunities for you to put theory into practice through selected examples.
In the latter part of the programme students will use research from LIDA, and others, to work on projects in innovative areas such as AI, health informatics, urban analytics, statistical and mathematical methods, and visualisation and immersive technologies. Experience in these areas will help you prepare for the future of data science.
Graduates would be well placed to be employed in many sectors including healthcare, retail, finance and government.
Students will normally study one module at a time.
Year1 - View timetable
[Learning Outcomes, Transferable (Key) Skills, Assessment]
In your first year you will typically study six 15 credit modules.
Compulsory modules:
You will study the following three Foundation modules in your first year of study.
OMAT5100M | Programming for Data Science Pre-requisite for: OMAT5200M, OMAT5204M | 15 credits | 1 Sep to 31 Oct (adv yr), 1 Mar to 30 Apr (2mth)(adv yr) | |
OMAT5101M | Statistical Methods Pre-requisite for: OMAT5203M, OMAT5205M | 15 credits | 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr), 1 May to 30 June, 1 May to 30 Jun (2mth)(adv yr) | |
OMAT5102M | Exploratory Data Analysis | 15 credits | 1 Jul to 31 Aug, 1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year) |
Optional modules:
You will study three out of the six Development modules in your first year of study.
OLDA5202M | Project Skills | 15 credits | 1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year) | |
OMAT5200M | Machine Learning | 15 credits | 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) | |
OMAT5201M | Linear Modelling | 15 credits | 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) | |
OMAT5203M | Statistical Learning | 15 credits | 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr) | |
OMAT5204M | Data Science | 15 credits | 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June | |
OMAT5205M | Multivariate Methods | 15 credits | 1 Jul to 31 Aug |
Year2 - View timetable
[Learning Outcomes, Transferable (Key) Skills, Assessment]
In your second year you will typically study the remaining six 15 credit modules.
Compulsory modules:
You will study three Advanced modules in your second year of study.
OLDA5302M | Capstone Project | credits | Not running until 202526 | |
OMAT5300M | Statistical Computing | 15 credits | 1 Jul to 31 Aug | |
OMAT5301M | Bayesian Statistics | 15 credits | Not running until 202526 |
Optional modules:
You will study the remaining three out of six Development modules in your second year of study.
OLDA5202M | Project Skills | 15 credits | 1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year) | |
OMAT5200M | Machine Learning | 15 credits | 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) | |
OMAT5201M | Linear Modelling | 15 credits | 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) | |
OMAT5203M | Statistical Learning | 15 credits | 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr) | |
OMAT5204M | Data Science | 15 credits | 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June | |
OMAT5205M | Multivariate Methods | 15 credits | 1 Jul to 31 Aug |
Last updated: 04/07/2024 10:00:11
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