Postgraduate study
Develop and apply computer science, statistics and mathematical methods to diverse and complex data collections to conduct research, solve problems and enable innovation.
 
  
Duration
1 year full-time
Entry requirements
2:1 (or international equivalent) with evidence of an interest or aptitude in mathematics and computing. Graduates from other fields, with strong mathematics and/or computation background will be considered with 60% average mark.
IELTS
6.5 (no less than 6.0 in any element)

If these grades are not met, English preparatory courses may be available
Start date
September
UK/EU fees
£9,765 - Terms apply
International fees
£22,815 - Terms apply
Campus
Jubilee
School/department
 

 

Overview

  • Enjoy research-engaged teaching
  • Join a thriving student community with over 100 postgraduate students in the school
  • Benefit from our strong and diverse connections with industry
  • Study in a school ranked top ten for research power (Research Excellence Framework 2014)
 

Our MSc Data Science programme is designed by the School of Computer science and the School of Mathematical Sciences to provide students with advanced computational and mathematical skills to tackle increasingly complex data analysis tasks and devise methods for analysing, presenting, and deriving insights from large data sets.

Opportunities for data driven innovation have been broadly recognised by industry, public sector and different scientific fields. As a data scientist, you will be able to work on a wide range of problems, analysing complex and diverse collections of data.

This course offers multiple pathways, preparing you for a highly skills career in industry or research. 

If you have a background in computer science or mathematical sciences, this course provides a selection of advanced modules that can ensure your further growth.

If you have a background in other subjects, the course provides a selection of modules that enable you to acquire knowledge in mathematics and computer science starting with required fundamentals.

DataScience

Research-led teaching

The subjects you will cover reflect the ongoing research and practices in data analytics and computation undertaken at both schools:

  • statistical modelling and inference
  • uncertainty quantification
  • multivariate statistics
  • time-series and forecasting
  • machine learning
  • advanced algorithms and data structures
  • data modelling and analysis
  • simulations and optimisation for decision support
  • parallel and distributed computing
  • programming

The course aims to bring you to the forefront of research and application methods, equipping you to take leading roles with demands of principled and theoretically sound approaches to problem solving by leveraging state-of-the-art computation techniques.

Find out more about computer science research

Find out more about mathematical sciences research 

A highlight of the course is the summer research project. This will provide you with opportunities to work with University of Nottingham academic staff and industry experts on relevant topics. The skills you gain by completing this project are sought after by employers.

Academic English preparation and support

If you require additional support to take your language skills to the required level, you may be able to attend a presessional course at the Centre for English Language Education, which is accredited by the British Council for the teaching of English in the UK. Students who successfully complete the presessional course to the required level can progress to postgraduate study without retaking IELTS or equivalent. You could be eligible for a joint offer, which means you will only need to apply for your visa once.

 

Full course details

DataScienceTeaching

MSc Data Science is offered on a full-time basis over one year. It is taught by the School of Computer Science and the School of Mathematical Sciences.

The course comprises 180 credits, with an equal split of 120 credits across modules in computer science and in mathematical sciences and a 60-credit research project.

Pathways

You can pursue one of four different pathways, depending on your background in computer science and mathematical sciences. You can take a selection of modules suitable for students with strong computer science background or an alternative one, and similarly, you can chose a selection of modules for students with strong mathematical sciences background or an alternative one.

Individual Research Project

Over the summer period, you will undertake a research project in data science. This project involves conducting a piece of research in depth, carried out under the supervision of a member of academic staff.

 
 

Modules

Computer science modules

Students with a background in computer science take 60 credits from the following indicative list of computer science modules:

Machine Learning

Providing you with an introduction to machine learning, pattern recognition, and data mining techniques, this module will enable you to consider both systems which are able to develop their own rules from trial-and-error experience to solve problems, as well as systems that find patterns in data without any supervision. In the latter case, data mining techniques will make generation of new knowledge possible, including very big data sets. This is now fashionably termed 'big data' science. You'll cover a range of topics including:

  • machine learning foundations
  • pattern recognition foundations
  • artificial neural networks
  • deep learning
  • applications of machine learning
  • data mining techniques
  • evaluating hypotheses
 
Knowledge Representation and Reasoning

This course examines how knowledge can be represented symbolically and how it can be manipulated in an automated way by reasoning programs. Some of the topics you'll cover include:

  • first order logic
  • resolution
  • description logic
  • default reasoning
  • rule-based systems
  • belief networks
 
Advanced Algorithms and Data Structures

We study the theory used in the design and analysis of advanced algorithms and data structures. The topics covered include:

  • string algorithms (such as for string matching, longest common subsequence)
  • graph algorithms (such as for minimum cuts and maximum flows)
  • advanced data structures (such as Binomial heaps and Bloom filters)

The theory is practiced in weekly labs where we learn how to implement the algorithms and data structures as functional and imperative programs (using the languages Haskell and C), and apply these to solve large instances of real world problems.

 
Computer Vision
You'll examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You'll cover a range of methods and applications, with particular emphasis being placed on the detection and identification of objects, image segmentation, pose estimation, recovery of three-dimensional shape and analysis of motion. These problems will be approached with both traditional and modern Computer Vision approaches including Deep Learning.
 
Simulation and Optimisation for Decision Support
This module offers insight into the applications of selected methods of decision support. The foundations for applying these methods are derived from Operations Research Simulation, Social Simulation, Data Science, Automated Scheduling, and Decision Analysis. Throughout the module, you will become more competent in choosing and implementing the appropriate method for the particular problem at hand. 
 
Data Modelling and Analysis

This module will enable you to appreciate the range of data analysis problems that can be modelled computationally and a range of techniques that are suitable to analyse and solve those problems.

Topics covered include:

  • basic statistics
  • types of data
  • data visualisation techniques
  • data modelling
  • data pre-processing methods including data imputation
  • forecasting methods
  • clustering and classification methods (decision trees, naīve bayes classifiers, k-nearest neighbours)
  • data simulation and model interpretation techniques to aid decision support
 
Professional Ethics in Computing

The module looks broadly into professional ethics within the scope of the computing discipline. It covers a range of professional, ethical, social and legal issues in order to study the impact that computer systems have in society and the implications of this from the perspective of the computing profession. In particular, the module covers topics such as:

  • introduction to ethics
  • critical thinking
  • professionalism
  • privacy
  • intellectual and intangible property
  • cyber-behaviour
  • safety
  • reliability accountability
 
Computability

You will begin by considering the attempts to characterise the problems that can theoretically be solved by physically possible computational processes, along with the practical implications. You will then consider the area of complexity theory, looking at whether or not problems can be solved under limitations on resources such as time or space.

You will examine the classes P and NP, and how to show problems are NP-complete. You will also consider other practically important classes such as PSPACE, and its relevance to adversarial games, ontologies, and the semantic web; and also complexity classes relevant to limitations of the effectiveness of parallel computation.

 
Parallel Computing 

This module charts the broad spectrum of approaches that are used to increase the performance of computing tasks by exploiting parallelism and/or distributed computation. It then considers in more detail a number of contrasting examples. The course deals mainly with the principles involved, but there is the chance to experiment with some of these approaches in the supporting labs.

Topics covered include:

  • common applications of parallel computing
  • parallel machine architectures including Single Instruction Multiple Data (SIMD) or short-vector processing
  • multi-core and multi-processor shared memory
  • custom co-processors including DSPs and GPUs, and cluster and grid computing
  • programming approaches including parallelising compilers
  • explicit message-passing (such as MPI)
  • specialised co-processor programming (such as for GPUs)
 

Advanced Algorithms and Data Structures can be taken as a 20 credit module that includes a group project or as a 10 credit module, without a group project.

Students without background in computer science must start with

Programming

This module gives you a comprehensive overview of the principles of programming, including such concepts as:

  • procedural logic
  • variables
  • flow control
  • input and output
  • the analysis and design of programs

Instruction will be provided in an object-oriented programming language. You will spend around five hours per week in lectures and computer classes studying for this module.

 
then take 40 credits from the following:
Databases and Interfaces

This module considers both the structure of databases, including how to make them fast, efficient and reliable, and the appropriate user interfaces which will make them easy to interact with for users.

You will start by looking at how to design a database, gaining an understanding of the standard features that management systems provide and how you can best utilise them and then develop an interactive application to access your database.

Throughout the lectures and computing sessions you will learn how to design and implement systems using a standard database management system, web technologies and GUI interfaces through practical programming/system examples.

 
Data Modelling and Analysis

This module will enable you to appreciate the range of data analysis problems that can be modelled computationally and a range of techniques that are suitable to analyse and solve those problems. Topics covered include:

  • basic statistics
  • types of data
  • data visualisation techniques
  • data modelling
  • data pre-processing methods including data imputation
  • forecasting methods
  • clustering and classification methods (decision trees, naīve bayes classifiers, k-nearest neighbours)
  • data simulation
  • model interpretation techniques to aid decision support
 
Computer Vision
You'll examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You'll cover a range of methods and applications, with particular emphasis being placed on the detection and identification of objects, image segmentation, pose estimation, recovery of three-dimensional shape and analysis of motion. These problems will be approached with both traditional and modern Computer Vision approaches including Deep Learning. 
 
Machine Learning

Providing you with an introduction to machine learning, pattern recognition, and data mining techniques, this module will enable you to consider both systems which are able to develop their own rules from trial-and-error experience to solve problems, as well as systems that find patterns in data without any supervision. In the latter case, data mining techniques will make generation of new knowledge possible, including very big data sets. This is now fashionably termed 'big data' science. You'll cover a range of topics including:

  • machine learning foundations
  • pattern recognition foundations
  • artificial neural networks
  • deep learning
  • applications of machine learning
  • data mining techniques
  • evaluating hypotheses
 
Simulation and Optimisation for Decision Support
This module offers insight into the applications of selected methods of decision support. The foundations for applying these methods are derived from Operations Research Simulation, Social Simulation, Data Science, Automated Scheduling, and Decision Analysis. Throughout the module, you will become more competent in choosing and implementing the appropriate method for the particular problem at hand. 
 

 

Mathematical sciences modules

Students without background in mathematical sciences start with

Statistical Foundations

In this module the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. The module will cover a 'common core' consisting of:

  • statistical concepts and methods
  • linear models
  • probability techniques
  • Markov chains

Students will gain experience of using a statistical package and interpreting its output. The common core material will be covered primarily at the beginning of the semester. 

 
then take 40 credits from the following:
Applied Statistical Modelling

This module extends the theoretical aspects of statistical inference by developing the theory of the generalised linear model and its practical implementation. Initially, designing of experiments in order to explore relationship between factors and a response is viewed within the context of Linear models.

The module then extends the understanding and application of statistical methodology established in previous courses to the analysis of discrete data and survival, which frequently occur in diverse applications. In the module students will be trained in the use of an appropriate high-level statistical package.

 
Applied Multivariate Statistics

This module is concerned with the analysis of multivariate data, in which the response is a vector of random variables rather than a single random variable. A theme running through the module is that of dimension reduction.

Key topics to be covered include:

  • principal components analysis
  • whose purpose is to identify the main modes of variation in a multivariate data set
  • modelling and inference for multivariate data, including multivariate regression data, based on the multivariate normal distribution
  • classification of observation vectors into subpopulations using a training sample
  • canonical correlation analysis, whose purpose is to identify dependencies between two or more sets of random variables

Further topics to be covered include factor analysis, methods of clustering and multidimensional scaling.

 
Time Series and Forecasting

This module will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real time series data will be described. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. Students will gain experience of using a statistical package and interpreting its output.

The course will cover:

  • concepts of stationary and non-stationary time-series
  • philosophy of model building in the context of time series analysis
  • simple time series models and their properties
  • the model identification process
  • estimation of parameters
  • assessing the goodness of fit
  • methods for forecasting
  • use of a statistical package
 

Students with background in mathematical sciences start with

Statistical Machine Learning
Machine Learning is a topic at the interface between statistics and computer science that concerns models that can adapt to and make predictions based on data. This module builds on principles of statistical inference and linear regression to introduce a variety of methods of regression and classification, trade-off, and on methods to measure and compensate for overfitting. The learning approach is hands on, with students using R to tackle challenging real world machine learning problems.
 
then take 40 credits from the following:
Applied Multivariate Statistics 

This module is concerned with the analysis of multivariate data, in which the response is a vector of random variables rather than a single random variable. A theme running through the module is that of dimension reduction.

Key topics to be covered include:

  • principal components analysis
  • whose purpose is to identify the main modes of variation in a multivariate dataset
  • modelling and inference for multivariate data, including multivariate regression data, based on the multivariate normal distribution
  • classification of observation vectors into subpopulations using a training sample
  • canonical correlation analysis, whose purpose is to identify dependencies between two or more sets of random variables

Further topics to be covered include factor analysis, methods of clustering and multidimensional scaling.

 
Applied Statistical Modelling

This module extends the theoretical aspects of statistical inference by developing the theory of the generalised linear model and its practical implementation. Initially, designing of experiments in order to explore relationship between factors and a response is viewed within the context of Linear models.

The module then extends the understanding and application of statistical methodology established in previouscourses to the analysis of discrete data and survival, which frequently occur in diverse applications. In thecourse students will be trained in the use of an appropriate high-level statistical package.

 
Computational Statistics

The increase in speed and memory capacity of modern computers has dramatically changed their use and applicability for complex statistical analysis. This module explores how computers allow the easy implementation of standard, but computationally intensive, statistical methods and also explores their use in the solution of non-standard analytically intractable problems by innovative numerical methods.

Particular topics to be covered include a selection from simulation methods, Markov chain Monte Carlo methods, the bootstrap and nonparametric statistics, statistical image analysis, and wavelets. Students will gain experience of using a statistical package and interpreting its output.

 
Statistical Inference

This course is concerned with the two main theories of statistical inference, namely classical (frequentist) inference and Bayesian inference. The classical inference component of the module builds on the ideas of mathematical statistics.

Topics such as sufficiency, estimating equations, likelihood ratio tests and best-unbiased estimators are explored in detail.

There is special emphasis on the exponential family of distributions, which includes many standard distributions such as the normal, Poisson, binomial and gamma. In Bayesian inference, there are three basic ingredients: a prior distribution, a likelihood and a posterior distribution, which are linked by Bayes' theorem.

Inference is based on the posterior distribution, and topics including conjugacy, vague prior knowledge, marginal and predictive inference, decision theory, normal inverse gamma inference, and categorical data are pursued. Common concepts, such as likelihood and sufficiency, are used to link and contrast the two approaches to inference. You will gain experience of the theory and concepts underlying much contemporary research in statistical inference and methodology.

 
Time Series and Forecasting

This module will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real time series data will be described. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. Students will gain experience of using a statistical package and interpreting its output.

The course will cover:

  • concepts of stationary and non-stationary time-series
  • philosophy of model building in the context of time series analysis
  • simple time series models and their properties
  • the model identification process
  • estimation of parameters
  • assessing the goodness of fit
  • methods for forecasting
  • use of a statistical package
 

Note: you can't take both the Applied Statistical Modelling and the Statistical Inference modules. 

Students who take a year-long module in Uncertainty Quantification or Computational Statistics will need to take 10 credits worth of computer science modules in both terms in order to achieve a 60:60 split of credits across two terms. Students are permitted to pursue maximum 70 and minimum 50 credits split across semesters if that provides them with a better suited selection of modules. Out of total of 180 credits at least 150 credits must come from level four modules.

 

The above is a sample of the typical modules that we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Due to the passage of time between commencement of the course and subsequent years of the course, modules may change due to developments in the curriculum and information is provided for indicative purposes only.

 
 

Funding

See information on how to fund your masters, including our step-by-step guide. 

As a student on this course, we do not anticipate any extra significant costs, alongside your tuition fees and living expenses. You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget.

Government loans for masters courses

The Government offers postgraduate student loans for students studying a taught or research masters course. Applicants must ordinarily live in England or the EU. Student loans are also available for students from Wales, Northern Ireland and Scotland.

International and EU students

Masters scholarships are available for international students from a wide variety of countries and areas of study. You must already have an offer to study at Nottingham to apply. Please note closing dates to ensure your course application is submitted in good time.

Information and advice on funding your degree, living costs and working while you study is available on our website, as well as country-specific resources.

 
 

Careers and professional development

This course prepares its students for careers in advanced software development, particularly where reliability and efficiency are vital requirements. Its graduates are likely to assume leading roles in major software-development projects in one of the areas of specialisation.

This course also provides an excellent foundation for further study and you may decide to progress to a PhD in order to continue your research.

Average starting salary and career progression

In 2017, 94.4% of postgraduates in the School of Computer Science who were available for employment had secured work or further study within six months of graduation. The average starting salary was £29,250 with the highest being £30,000.* 

* Known destinations of full-time home postgraduates 2016/17. Salaries are calculated based on the median of those in full-time paid employment within the UK. 

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Career prospects and employability

The University of Nottingham is consistently named as one of the most targeted universities by Britain’s leading graduate employers**.

Those who take up a postgraduate research opportunity with us will not only receive support in terms of close contact with supervisors and specific training related to your area of research, you will also benefit from dedicated careers advice from our Careers and Employability Service

Our Careers and Employability Service offers a range of services including advice sessions, employer events, recruitment fairs and skills workshops – and once you have graduated, you will have access to the service for life.

** The Graduate Market 2013-2019, High Fliers Research.

 
 
 

Disclaimer
This online prospectus has been drafted in advance of the academic year to which it applies. Every effort has been made to ensure that the information is accurate at the time of publishing, but changes (for example to course content) are likely to occur given the interval between publishing and commencement of the course. It is therefore very important to check this website for any updates before you apply for the course where there has been an interval between you reading this website and applying.

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