1 year full time, 2 years part time
Starts September 2017

Data Science

Are you interested in data science and its application to sciences and technology? Do you want to work in industry or plan to pursue further study?

The acquisition of large datasets is now common in academic science and industrial practice. On this MSc, you’ll learn about the theory and practice of state-of-the-art data science with applications ranging from mathematics to physics, and from biology to computer science. You’ll develop the skills to provide appropriate and up-to-date tools to deal with datasets.

Key facts

  • 97% of our research output was rated world leading, internationally excellent or internationally recognised in the 2014 Research Excellence Framework (REF).
  • You’ll benefit from our collaborative links with other departments in the UK and overseas.
  • We foster an intellectually stimulating environment in which you are encouraged to develop your own research interests with the support of our faculty.

How will I study?

Our MSc has four main streams:

  • Mathematics
  • Physics
  • Computer Science
  • Life Sciences.

In the autumn and spring terms, you take core modules and options depending on the stream you choose. In the summer term, you work on your MSc dissertation.

Full-time and part-time study

You can choose to study this course full time or part time. Find the modules for the full-time course below. 

For details about the part-time course structure, contact us at

What will I study?

  • Module list

    Core modules

    Core modules are taken by all students on the course. They give you a solid grounding in your chosen subject and prepare you to explore the topics that interest you most.

    • Dissertation (Data Science)

      45 credits
      All Year Teaching, Year 1

      For this module, you carry out independent study and research under the guide of a supervisor on a designated topic. 

      You then complete a report on the subject over the summer.

    • Algorithmic Data Science

      15 credits
      Autumn Teaching, Year 1

      In this module, you will learn the computer science aspects of data science.

      You will particularly focus on how data are represented and manipulated to achieve good performance on large data sets (larger than 10GB) where standard algorithmic techniques no longer apply.

      In lectures, you will learn about data structures, algorithms and systems, including distributed computing, databases (relational and non-relational), parallel computing, and cloud computing.

      In laboratory sessions, you will work with large data sets from real world applications. This will help you to understand the impact on run-time of your algorithmic choices, and of different computing models (GPU vs CPU).

    • Data Analysis Techniques

      15 credits
      Autumn Teaching, Year 1

      This module introduces you to the mathematical and statistical techniques used to analyse data. The module is fairly rigorous, and is aimed at students who have, or anticipate having, research data to analyse in a thorough and unbiased way.

      Topics include: probability distributions; error propagation; maximum likelihood method and linear least squares fitting; chi-squared testing; subjective probability and Bayes' theorem; monte Carlo techniques; and non-linear least squares fitting.

    • Data Science Research Methods

      15 credits
      Autumn Teaching, Year 1

      The course will provide you with the practical tools and techniques required to build, analyse and interpret 'big data' datasets.

      You will also be taught how to develop and test hypotheses, prepare actionable plans and present your findings. In the laboratory, you will be given real-world datasets and, as the course proceeds, apply these techniques to that data.

      You will be required to address a current real-world problem, using your analysis to test hypotheses, develop an actionable plan and prepare a client presentation.

    • Data Science Masters Research Proposal

      15 credits
      Spring Teaching, Year 1

      In this module, you use literature to study the background to a problem in Data Science in your respective stream.

      You choose your individual supervisor and devise a strategy by which this problem can be studied - giving details of techniques and resources that you will use to address the problem.

      This research proposal forms the basis of the Data Science Dissertation that you will write in summer term.

    • Machine Learning

      15 credits
      Spring Teaching, Year 1

      In this module, you explore advanced techniques in machine learning.

      You use a systematic treatment, based on the following three key ingredients:

      • tasks
      • models
      • features.

      As part of the module, you are introduced to both regression and classification, and your studies emphasise concepts such as model performance, learnability and computational complexity.

      You learn techniques including:

      • probabilistic and non-probabilistic classification and regression methods
      • reinforcement learning approaches including the non-linear variants using kernel methods.

      You are also introduced to techniques for pre-processing the data (including PCA).

      You will then need to be able to implement, develop and deploy these techniques to real-world problems.

      In order to take this module, you need to have already taken the 'Mathematics & Computational Methods for Complex Systems' module (817G5), or have taken an equivalent mathematical module or have equivalent prior experience.

    • Wider Topics in Data Science

      15 credits
      Spring Teaching, Year 1

      Your studies in this module include a series of seminars covering several topics - including national laws on data and ethical implications.

      In addition, there are seminars by Data-Science-oriented companies.

      You are expected to write a final dissertation of 3000 words, on a topic of your choice falling within the scope of the module.


    Alongside your core modules, you can choose options to broaden your horizons and tailor your course to your interests.

    • Applied Natural Language Processing

      15 credits
      Autumn Teaching, Year 1

      Applied Natural Language Processing concerns the theory and practice of automatic text processing technologies.

      In this module, you study core, generic text processing models, such as:

      • tokenisation
      • segmentation
      • stemming
      • lemmatisation
      • part-of-speech tagging
      • named entity recognition
      • phrasal chunking
      • dependency parsing.

      You also cover related problems and application areas, such as:

      • document classification
      • information retrieval
      • information extraction.

      You gain hands-on experience with the practical aspects of this module through weekly laboratory sessions.

      As part of this, you make extensive use of the Natural Language Toolkit, which is a collection of natural language processing tools written in the Python programming language.

      Your seminars in this module provide in-depth discussion of a number of important issues that arise when developing natural language processing tools, including:

      • experimentation and hypothesis testing
      • advanced data smoothing techniques
      • domain adaptation
      • topic modelling
      • active learning
      • generative versus discriminative learning
      • semi-supervised learning.
    • Introduction to Genes and Biochemistry

      15 credits
      Autumn Teaching, Year 1

      This module will provide background knowledge of five basic units of Biochemistry and the relationship between genes and proteins within the cell.

      Unit 1 of the module deals with the molecules of life, DNA, RNA, nucleotides and the central dogma of molecular biology.

      Unit 2 covers the decoding of the genetic code through the processes of transcription and translation.

      Unit 3 deals with proteins, their structure properties and amino acid building blocks.

      Unit 4 deals with enzymes and simple enzyme catalysed reactions.

      Unit 5 covers metabolism and uses glycolysis and Krebs cycle as examples of typical pathways bioenergentics is briefly introduced.

    • Mathematics and Computational Methods for Complex Systems

      15 credits
      Autumn Teaching, Year 1

      This module provides a foundation in mathematical and scientific computing techniques used widely in artificial intelligence, artificial life and related fields. The material covered in this module will facilitate the study of a number of options on other MSc courses at a deeper level than would be possible without it. In particular, it is a prerequisite for the Neural Networks and Computational Neuroscience modules. Coursework is based around Matlab packages.

      Topics include:

      • vectors and matrices
      • differential calculus
      • numerical integration
      • probability and hypothesis testing
      • dynamical systems theory.
    • Programming in C++

      15 credits
      Autumn Teaching, Year 1

      After a review of the basic concepts of the C++ language, you are introduced to object oriented programming in C++ and its application to scientific computing. This includes writing and using classes and templates, operator overloading, inheritance, exceptions and error handling. In addition, Eigen, a powerful library for linear algebra is introduced. The results of programs are displayed using the graphics interface dislin.

    • Advanced Natural Language Processing

      15 credits
      Spring Teaching, Year 1

      Advanced Natural Language Processing builds on the foundations provided by the Applied Natural Language Processing module.

      You will develop your knowledge and understanding of key topics including:

      • word sense disambiguation
      • vector space models of semantics
      • named entity recognition
      • topic modelling
      • machine translation

      Seminars will provide in-depth discussion of research papers related to the key topics and also general issues that arise when developing natural language processing tools, including:

      • hypothesis testing
      • data smoothing techniques
      • domain adaptation
      • generative versus discriminative learning
      • semi-supervised learning

      Labs will provide the opportunity for students to improve their python programming skills, experiment with some off-the-shelf technology and develop research skills.

    • Advanced Particle Physics

      15 credits
      Spring Teaching, Year 1

      You will acquire an overview of the current status of modern particle physics and current experimental techniques used in an attempt to answer today's fundamental questions in this field. 

      The topics discussed will be: 

      • Essential skills for the experimental particle physicist
      • Neutrino physics: Neutrino oscillations and reactor neutrinos
      • Neutrino physics: SuperNova, geo- and solar- neutrinos and direct neutrino mass measurements
      • Cosmic ray physics
      • Dark matter
      • Introduction to QCD (jets, particles distribution functions, etc)
      • Higgs physics
      • BSM (including supersymmetry)
      • Flavour physics & CP violation
      • Electric dipole measurements
      • Future particle physics experiments.
    • Coding Theory

      15 credits
      Spring Teaching, Year 1

      Topics covered include: 

      • Introduction to error-correcting codes. The main coding theory problem. Finite fields.
      • Vector spaces over finite fields. Linear codes. Encoding and decoding with a linear code.
      • The dual code and the parity check matrix. Hamming codes. Constructions of codes.
      • Weight enumerators. Cyclic codes. MDS codes.
    • Current Topics in Life Sciences

      15 credits
      Spring Teaching, Year 1

      Gain an introduction to a range of active areas of research in life sciences.

      You are taught via a series of advanced research seminars given by University of Sussex staff, research students and invited speakers in either the:

      • Evolution, Behaviour and Environment (EBE) seminar series
      • Sussex Neuroscience seminar series
      • Genome seminar series
      • Biochemistry and Biomedicine seminar series.

      Through the seminars, you learn about the latest developments in a range of topics, gain insight into the nature of scientific research, and meet a range of researchers.

      You are assessed via a portfolio of work summarising, synthesising and communicating the research for a scientific audience and the general public.

    • Genomics and Bioinformatics

      15 credits
      Spring Teaching, Year 1

      This module will introduce the common types of genomic and proteomic data available in biological databases; including DNA and protein sequences, motifs, gene structure, protein interactions and expression profiles. The aims and methods of DNA and protein sequence analysis will be covered, including analysis of homology, identification of motifs and domains, pair-wise and multiple alignments and prediction of gene structure.

      The practical sessions will include the analysis of DNA and protein sequence data from biological databases. In these sessions you will learn how to integrate data to find the functional links between disease related genes and proteins.

    • Image Processing

      15 credits
      Spring Teaching, Year 1

      You will cover topics including:
      • introduction to machine vision and relation to image processing
      • camera technologies, lenses for machine vision, image formation and resolution, display technologies
      • image acquisition hardware
      • histogram manipulations
      • linear invariant systems in two dimensions
      • the convolution operation and its discrete implementation as mask operators
      • first and second order differential edge detection operators, edge-filling techniques, Hough transform
      • the 2D Fourier transform and frequency domain filters, 2D correlation 
      • scene segmentation methods and region filling
      • pattern recognition techniques, shape descriptors, Fourier descriptors, template matching
      • examples of machine vision systems in industry.
    • Monte Carlo Simulations

      15 credits
      Spring Teaching, Year 1

      The module will cover topics including:

      • Introduction to R 
      • Pseudo-random number generation 
      • Generation of random variates 
      • Variance reduction 
      • Markov-chain Monte Carlo and its foundations 
      • How to analyse Monte Carlo simulations 
      • Application to physics: the Ising model 
      • Application to statistics: goodness-of-fit tests
    • Numerical Solution of Partial Differential Equations

      15 credits
      Spring Teaching, Year 1

      Topics covered include: variational formulation of boundary value problems; function spaces; abstract variational problems; Lax-Milgram Theorem; Galerkin method; finite element method; examples of finite elements; and error analysis.
    • Particle Physics Detector Technology

      15 credits
      Spring Teaching, Year 1

      The module explores the technical manner in which some of the scientific questions in the fields of experimental particle physics, including high energy physics, neutrino physics etc., are being addressed. The student is introduced to many of the experimental techniques that are used to study the particle phenomena. The focus is on the demands those scientific requirements place on the detector technology and current state-of-the-art technologies.

      This module will provide you with:

      • an introduction to some of the basic concepts of particle physics
      • an overview of some of the topical cutting edge questions in the field
      • an understanding of some key types of experiments
      • a detailed understanding of the underlying detector technologies.

      Topics covered include:

      1. Intro to particle structure
        1. particles and forces, masses and lifetimes
        2. coupling strengths and interactions
        3. cross sections and decays
      2. Accelerators
        1. principles of acceleration
        2. kinematics, center of mass
        3. fixed target experiments, colliders
      3. Reactors
        1. nuclear fission reactors, fission reactions, types of reactors
        2. neutron sources, absorption and moderation, neutron reactions
        3. nuclear fusion, solar and fusion reactors
      4. Detectors
        1. gaseous
        2. liquid (scintillator, cerenkov, bubble chamber)
        3. solid-state
        4. scintillation
        5. calorimeters, tracking detectors
        6. particle identification
      5. Monte Carlo modelling
        1. physics
    • Web Applications and Services

      15 credits
      Spring Teaching, Year 1

      This module provides an introduction to the models and technologies used to provide distributed applications and services over the Internet. You will study the features and problems of building distributed applications, such as naming, security, synchronisation, replication, object persistence and content distribution. You will use the framework provided by the Java Enterprise Edition to build distributed web applications.

Entry requirements

An upper second-class (2.1) undergraduate honours degree or above with an engineering, science, computing, mathematics or life sciences background.

English language requirements

Lower level (IELTS 6.0, with not less than 6.0 in each section)

Find out about other English language qualifications we accept.

English language support

Don’t have the English language level for your course? Find out more about our pre-sessional courses.

Additional information for international students

We welcome applications from all over the world. Find out about international qualifications suitable for our Masters courses.

Visas and immigration

Find out how to apply for a student visa

Fees and scholarships

How much does it cost?


Home: £7,700 per year

EU: £7,700 per year

Channel Islands and Isle of Man: £7,700 per year

Overseas: £15,100 per year

Note that your fees may be subject to an increase on an annual basis.

How can I fund my course?

Postgraduate Masters loans

Borrow up to £10,280 to contribute to your postgraduate study.

Find out more about Postgraduate Masters Loans


Our aim is to ensure that every student who wants to study with us is able to despite financial barriers, so that we continue to attract talented and unique individuals.

Chancellor’s Masters Scholarship (2017)

Open to students with a 1st class from a UK university or excellent grades from an EU university and offered a F/T place on a Sussex Masters in 2017

Application deadline:

1 August 2017

Find out more about the Chancellor’s Masters Scholarship

Sussex Graduate Scholarship (2017)

Open to Sussex students who graduate with a first or upper second-class degree and offered a full-time place on a Sussex Masters course in 2017

Application deadline:

1 August 2017

Find out more about the Sussex Graduate Scholarship

Sussex India Scholarships (2017)

Sussex India Scholarships are worth £3,500 and are for overseas fee paying students from India commencing Masters study in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex India Scholarships

Sussex Malaysia Scholarships (2017)

Sussex Malaysia Scholarships are worth £3,500 and are for overseas fee paying students from Malaysia commencing Masters study in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex Malaysia Scholarships

Sussex Nigeria Scholarships (2017)

Sussex Nigeria Scholarships are worth £3,500 or £5,000 and are for overseas fee paying students from Nigeria commencing a Masters in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex Nigeria Scholarships

Sussex Pakistan Scholarships (2017)

Sussex Pakistan Scholarships are worth £3,500 and are for overseas fee paying students from Pakistan commencing Masters study in September 2017.

Application deadline:

1 August 2017

Find out more about the Sussex Pakistan Scholarships

How Masters scholarships make studying more affordable

Living costs

Find out typical living costs for studying at Sussex.


Research in the Department of Mathematics focuses on the non-mutually exclusive areas of:

“I am a probabilist, and probability is at the foundation of data science.” Professor Enrico ScalasProfessor of Statistics and Probability and Head of the Department of Mathematics


Graduate destinations

100% of students from the Department of Mathematics were in work or further study six months after graduating. Recent graduates have gone on to jobs including:

  • accountant, Ernst & Young
  • graduate analyst, Invesco
  • performance analyst, Legal and General Investment Management.

(HESA EPI, Destinations of Post Graduate Leavers from Higher Education Survey 2015)

Your future career

Our graduates become data scientists in industries such as engineering, computing, and financial and banking institutes. Some of them go on to careers in:

  • government institutions
  • scientific research
  • teaching
  • academia
  • management.

Working while you study

Our Careers and Employability Centre can help you find part-time work while you study. Find out more about career development and part-time work

Contact us