The course

Delivery
Full-time, Part-time
Course type
Taught
Location
Edinburgh
Entry date
September

Contact

Overview

This programme is for students looking to gain mathematical, computational, and analytical skills that enable them to analyse large data sets to support decisions and conclusions under uncertainty. This is an area of science and technology that is attracting significant research efforts and that will continue to grow for the foreseeable future, as more and more industries adopt data-driven and data-centric approaches.


Course content

Detailed course guide

This MSc is a new programme designed in collaboration between both the School of Mathematical and Computer Sciences and the School of Engineering and Physical Sciences. It consists of two coherent and distinctive streams; The first and more theoretical stream will build the student knowledge on the stochastic aspects of data science (Stream SM : Stochastic Modelling and data science) The second and more applied stream will build the student knowledge on the computational aspects and engineering applications (Stream CDSE : Computational Data Science and Engineering).

It is important to note that students will only be able to study one of these streams. For more information on the streams, see below:

 

Stochastic Modelling and Data Science

Students interested in the 'Stochastic Modelling and Data Science' stream will have a degree with a substantial mathematical component.

Students should study this if they are looking to learn how to model processes with random components (such as, for instance, social networks and spread of information on them, or renewable energy production, or claims occurrence in an insurance business), how to make statistical inference based on data, and how to apply all these mathematical concepts in some of the application areas with ever-growing demand for graduates with skills to make and explain data-driven decisions.

Mandatory courses

  • F21ML: Statistical Machine Learning
  • F71MA: Statistical Models
  • F11BI: Bayesian Inference and Computation Methods
  • F71PM: Probabilistic Methods
  • F71SP: Applied Stochastic Processes
  • F71SR: Research & Industry Topics

Optional courses

Semester 1

  • F71DV: Derivative Markets and Pricing (F71DV)
  • F11AM: Mathematical Ecology
  • B31XO: Sampling and Computational Imaging (B31XO).

Semester 2

  • F11SS: Stochastic Simulation 
  • F71DA: Data Analytics and Time Series Analysis
  • F11DA: Data Assimilation with Applications to Climate Change
  • F71AE: Survival Models
  • B3XN: Scalable inference and deep learning 

Project and dissertation component

The students will undertake a research project supervised by an academic member of staff in the School of Mathematics and Computer Science

Computational Data Science and Engineering

Students interested in the 'Computational Data Science and Engineering' stream will have previously studied in engineering, physics, computer science, or mathematics or similar.

This stream is for you if you are interested in computational aspects of data science and in-depth analysis of its engineering applications, from computational imaging, to robotics, and telecommunications.

The students will attend 8 compulsory courses over semesters 1 and 2 (September - May):

Semester 1

  • F21ML: Statistical Machine Learning
  • F71MA: Statistical Models
  • B31XM: Information Theory and Communications
  • B31XO: Sampling and Computational Imaging

Semester 2

  • F11BI: Bayesian Inference and Computation Methods
  • B31XN: Scalable Inference and Deep Learning
  • B31XP: Multi-disciplinary Group Project
  • B31EZ: Critical Analysis and Research Preparation

Project and dissertation component

The students will undertake a research project supervised by an academic member of staff in the School of Engineering and Physical Science

Choosing your stream

All applicants must clearly state which stream they wish to study on their application.

Entry requirements

A good Honours degree (first or second class), or its non-UK equivalent, from a recognised British or overseas university. The degree can be from various disciplines, ranging from mathematics and engineering, to physics and computer science, provided that it provides a solid background in mathematics.

English language requirements

Important: If your first language is not English, or your first degree was not taught in English, we’ll need to see evidence of your English language ability.

The minimum requirement for English language is IELTS 6.5, we also accept TOEFL (scores of 79 and higher).

We also offer a range of English language courses to help you meet the English language requirement prior to starting your master’s programme:

  • 14 weeks English (for IELTS of 5.5 with no more than one skill at 4.5);
  • 10 weeks English (for IELTS of 5.5 with minimum of 5.0 in all skills);
  • 6 weeks English (for IELTS 5.5 with minimum of 5.5 in reading & writing and minimum of 5.0 in speaking & listening)
  • 6 weeks English (for IELTS 5.5 with minimum of 5.5 in reading & writing and minimum of 5.0 in speaking & listening)

Fees

Tuition fees for 2019 entry (by residency status)
Status* Full-time Part-time
Scotland / Non-UK EU £9000 £4500
England / Northern Ireland / Wales £9000 £4500
Overseas £18680 £9340

* If you are unsure which category you fall in to, you should complete a fee status enquiry form, which allows us to assess your fees.

Scholarships and bursaries

We aim to encourage well-qualified, ambitious students to study with us and we offer a wide variety of scholarships and bursaries to achieve this. Over £6 million worth of opportunities are available in fee and stipend scholarships, and more than 400 students benefit from this support.

View our full range of postgraduate taught scholarships.