(Full time) 2019 start
Data Science and Analytics MSc
Overview
We are surrounded by data. The variety and amount we collect and store grows every day, from the simplest of retail transactions to the complex and intimate medical records of millions.
Why do we store data? Where do we store it? How do we retrieve it? What do we use it for?
There is an increasing demand for people who can manage and control the way data is used. These individuals require an understanding of computer science and mathematics, as well as a familiarity with the data needs and processes of a number of different areas, including healthcare, business, government and the environment.
The Data Science and Analytics MSc is a highly flexible course which offers you the opportunity to develop a range of skills through a combination of compulsory and optional modules, including:
- analysing structured and unstructured data
- analysing large datasets
- critically evaluating results in context
- getting insights from data
The course combines expertise from the Schools of Computing, Geography and Mathematics with that of Leeds University Business School. By choosing the appropriate modules, you can follow specific pathways in business management, healthcare or geographic information systems (GIS).
This collaboration allows you to benefit from a range of data science perspectives and applications, allowing you to tailor the course to match your own career ambitions.
Course content
The programme will equip students with the knowledge and skills they need to meet the challenges of data science in the modern world.
Students on this programme will be benefit from being taught by experts from different academic units: the School of Mathematics; the School of Computing; the School of Geography and Leeds University Business School.
Modules can be chosen from each of these areas. Mathematics modules are available for students who are not from a mathematics/statistics background, while Computing modules will be suitable for students on this programme who are not from a computer science background.
The programme will introduce you to different perspectives on data science, including the mathematical and computational underpinnings of the subject and its applications in specific contexts.
Your dissertation will enable you to span academic disciplines with supervision from both area. For this project you will interpret a real-world problem, coving data elucidation, analysis, and application, building the key skills you will use in a professional or academic career.
Want to find out more about your modules?
Take a look at the Data Science and Analytics module descriptions for more detail on what you will study.
Course structure
The list shown below represents typical modules/components studied and may change from time to time. Read more in our Terms and conditions.
Modules
Year 1
Compulsory modules
- Data Science 15 credits
- Learning Skills through Case Studies 15 credits
- Dissertation in Data Science and Analytics 60 credits
Optional modules (selection of typical options shown below)
- Distributed Systems 10 credits
- Machine Learning 10 credits
- Information Visualization 10 credits
- Big Data Systems 15 credits
- Data Management 15 credits
- Bio-Inspired Computing 15 credits
- Knowledge Representation and Reasoning 15 credits
- Systems Programming 15 credits
- Algorithms 15 credits
- Practical Programming 15 credits
- Data Mining and Text Analytics 15 credits
- Cloud Computing 15 credits
- Semantic Technologies and Applications 15 credits
- Image Analysis 15 credits
- Scheduling 15 credits
- Scientific Computation 15 credits
- Graph Theory: Structure and Algorithms 15 credits
- Geographic Data Visualisation & Analysis 15 credits
- Geodemographics and Neighbourhood Analysis 15 credits
- Big Data and Consumer Analytics 15 credits
- Predictive Analytics 15 credits
- Applied GIS and Retail Modelling 15 credits
- Effective Decision Making 15 credits
- Advanced Management Decision Making 15 credits
- Business Analytics and Decision Science 15 credits
- Forecasting and Advanced Business Analytics 15 credits
- Linear Regression and Robustness 15 credits
- Statistical Theory 15 credits
- Stochastic Financial Modelling 15 credits
- Multivariate Analysis 10 credits
- Time Series 10 credits
- Bayesian Statistics 10 credits
- Generalised Linear Models 10 credits
- Introduction to Statistics and DNA 10 credits
- Statistical Theory and Methods 15 credits
- Statistical Learning 15 credits
- Multivariate Methods 15 credits
- Multivariate and Cluster Analysis 15 credits
- Time Series and Spectral Analysis 15 credits
- Bayesian Statistics and Causality 15 credits
- Generalised Linear and Additive Models 15 credits
- Statistical Computing 15 credits
- Statistics and DNA 15 credits
Learning and teaching
Teaching is by lectures, tutorials, seminars and supervised research projects.
Assessment
Assessment is by a range of methods, including formal examination, assignments, coursework, reports and practical activities.
Applying, fees and funding
Entry requirements
A 2:1 (hons) bachelor degree with a substantial numerate component. This may include degrees in mathematics, statistics, economics, engineering, or a physical science subject.
We accept a range of international equivalent qualifications. For information please contact the School of Mathematics admissions team.
English language requirements
IELTS 6.5 overall, with no less than 6.0 in all components. For other English qualifications, read English language equivalent qualifications.
Improve your English
International students who do not meet the English language requirements for this programme may be able to study our postgraduate pre-sessional English course, to help improve your English language level.
This pre-sessional course is designed with a progression route to your degree programme and you’ll learn academic English in the context of your subject area. To find out more, read Language for Science (6 weeks) and Language for Science: General Science (10 weeks).
If you need to study for longer than 10 weeks, read more about our postgraduate pre-sessional English course.
How to apply
Application deadlines
31 July 2019 - International applicants
31 August 2019 - Home/EU applicants
This link takes you to information on applying for taught programmes and to the University's online application system.
If you're unsure about the application process, contact the admissions team for help.
Read about visas, immigration and other information in International students. We recommend that international students apply as early as possible to ensure that they have time to apply for their visa.
Admissions policy
Faculty of Maths and Physical Sciences Postgraduate Admissions Policy 2019
Fees
- UK/EU: £10,500 (total)
- International: £24,000 (total)
Read more about paying fees and charges.
For fees information for international taught postgraduate students, read Masters fees.
Additional cost information
There may be additional costs related to your course or programme of study, or related to being a student at the University of Leeds. Read more about additional costs
Scholarships and financial support
The School of Mathematics offers a range of scholarships for UK, EU and International students.
Find out more about our Scholarships.
Career opportunities
There is increasing demand for individuals who can manage and control the way data is used. These individuals require an understanding of computer science and mathematics as well as a range of sector specific skills.
The emerging era of ‘big data’ brought about by the digital technology revolution shows no signs of abating. In this era, data scientists will become increasingly important, with a 2017 report by IBM forecasting that demand for people with these skills will rise by 28% by 2020.
Careers support
We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.
The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.