MSc Data Science (Statistics) (online)

Programme overview

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a pass (50% weighted average or higher) in both of the first two modules, you will not be able to continue and will be withdrawn from the degree.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.

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.

Year 1

(online)

[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable

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.

Code	Title	Credits	Semester	Pass for Progression
OMAT5100M	Programming for Data Science	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)
OMAT5101M	Statistical Methods	15	1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)
OMAT5102M	Exploratory Data Analysis	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year), 1 Jul to 31 Aug

Optional Modules

You will study three out of the six Development modules in your first year of study.

Code	Title	Credits	Semester	Pass for Progression
OLDA5202M	Project Skills	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year)
OMAT5200M	Machine Learning	15	1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)
OMAT5201M	Linear Modelling	15	1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)
OMAT5203M	Statistical Learning	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr)
OMAT5204M	Data Science	15	1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June
OMAT5205M	Multivariate Methods	15	1 Jul to 31 Aug

Year 2

(online)

[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable

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.

Code	Title	Credits	Semester	Pass for Progression
OLDA5302M	Capstone Project	15
OMAT5300M	Statistical Computing	15	1 Jul to 31 Aug
OMAT5301M	Bayesian Statistics	15

Optional Modules

You will study the remaining three out of six Development modules in your second year of study.

Code	Title	Credits	Semester	Pass for Progression
OLDA5202M	Project Skills	15	1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year)
OMAT5200M	Machine Learning	15	1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr)
OMAT5201M	Linear Modelling	15	1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr)
OMAT5203M	Statistical Learning	15	1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr)
OMAT5204M	Data Science	15	1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June
OMAT5205M	Multivariate Methods	15	1 Jul to 31 Aug

Last updated: 04/07/2024 10:00:11

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