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Data Science and Analytics MSc

(Full time) 2018/19

MSc Data Science and Analytics

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 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 which can be applied in a variety of business environments.

The Data Science and Analytics MSc is a highly flexible course which offers the opportunity to develop a range of skills, including analysing structured and unstructured data, analysing large datasets and critically evaluating results in context, through a combination of compulsory and optional modules. By choosing appropriate modules you can follow specific pathways, in business management, healthcare or geographic information systems (GIS), which will allow you to tailor the programme to suit your background and needs.

The course combines expertise from the Schools of Computing, Geography and Mathematics with that of Leeds University Business School and the Yorkshire Centre for Health Informatics. This collaboration allows you to benefit from a range of data science perspectives and applications, supporting you to tailor your learning to your career ambitions.

The programme will equip students with the necessary knowledge and skills in data science.

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 Yorkshire Centre for Health Informatics; the Faculty of Medicine and Health; the School of Geography and Leeds University Business School.

Modules are available 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. You 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.

Course structure

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

  • 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
  • Applied Population and Demographic 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
  • Informatics in Health Care 15 credits
  • Process Modelling, Benefits and Change 15 credits
  • Clinical Knowledge Management and Decision Support Systems 15 credits
  • Mobile Health 15 credits

These are typical modules/components studied and may change from time to time. Read more in our Terms and conditions.

For more information on typical modules, read Data Science and Analytics MSc in the course catalogue

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.

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.

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.

How to apply

Application deadlines

31/07/18 - International

31/08/18 - Home/EU

Apply

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 Mathematics and Physical Sciences Taught Postgraduate Admissions Policy

Fees

UK/EU: £10,000 (total)

International: £21,000 (total)

Read more about paying fees and charges.

For fees information for international taught postgraduate students, read Masters fees.

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.

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, demand for data scientists will continue to grow, with one report forecasting a shortage of 140,000 – 190,000 data scientists by 2018 in the US alone.

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.

Key facts

Start date:
September 2018

Duration/Mode:
1 year full time

UK/EU fees:
£10,000 (total)
International fees:
£21,000 (total)

Entry requirements:
A bachelor degree with a 2:1 (hons) with a substantial numerate component

Language requirements:
IELTS 6.5 overall, with no less than 6.0 in all components

Apply

Contact us:
School of Mathematics Admissions Team
+44 (0)113 343 0945
maths-msc@leeds.ac.uk