MSc Data Science

Organisations today have a vast amount of raw data generated from their computerised operational systems. So how will they turn this into high quality information for strategic decision-making? They need a new generation of data analysts who understand effective and efficient data analysis methods and the Knowledge Discovery and Data Mining (KDD) process.

This course – one of the most established in this area with over 15 years of history – offers an excellent platform to help you forge a successful career in data analysis.

As a student, you will be part of our vibrant research community and will have very good opportunities to progress to a PhD. You will be part of a research group that has made significant contributions in techniques for data mining and KDD – including KDD Methodologies; use of metaheuristics for rule and tree induction; all-rule induction; clustering techniques; feature subset selection; feature construction; time series classification as well as many applications in the financial services industry, medicine and telecommunications.

The research group has collaborated in research or consultancy projects with a wide range of organisations, including: the Biotechnology and Biological Sciences Research Council (BBSRC), the Engineering and Physical Sciences Research Council (EPSRC), the Institute and Faculty of Actuaries and The Royal Society, Alston Transport, Derbyshire Police, Lanner Group, Master Foods, MET Office, National Air Traffic Services, Aviva, Process Evolution Ltd, Simultec AG Zurich, Virgin Money and Norwich City Football Club.

What’s more, this degree has full Chartered IT Professional (CITP) accreditation (Further Learning Element)as well as partial fulfilment of Chartered Engineer (CEng) status from The Chartered Institute for IT (BCS).

You will graduate with a wealth of knowledge, prestigious connections and research experience – putting you one step ahead of other graduates in your career or further studies.

About

This course is designed to train highly qualified data analysts – or data scientists – to embark on careers in a wide range of industries. You’ll be given an excellent practical and theoretical grounding in data mining and statistics with the chance to customise your degree through modules in artificial intelligence, visualisation, programming and database manipulation.

Data Scientists are highly prized for their advanced, practical skill set and their increasing importance to the success of a modern business. Organisations in almost any industry need to source, analyse and utilise vast amounts of data to aid strategic decision-making, so you’ll have great graduate career prospects as well as a wide range of transferrable skills.

We have a large Data Mining, Machine Learning and Statistics research group, which has made significant contributions to the field in the last 10 years, so you’ll be working directly with pioneering experts.

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Course Reference Number: 1543997

Important Information

Whilst the University will make every effort to offer the courses listed, changes may sometimes be made arising from the regular review of course programmes. Where this activity leads to significant (but not minor) changes to programmes, there will normally be prior consultation of students and others. Changes may for example consist of variations to the content and method of delivery of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such changes to a minimum, and will inform students.

Assessment for Year 1

We’ll assess your work in different ways depending on the module content and learning objectives. These might include programming assignments, essays, class tests, problem sheets, laboratory reports, presentations and demonstrations.

Most modules are assessed through a mixture of coursework and exams, while some are entirely assessed by coursework. In your dissertation, you will be assessed particularly on your understanding and how you integrate what you’ve learnt to solve a real problem.

Structure

The MSc Data Science course is a full-time, one-year taught programme, designed for advanced students and practitioners. You can also take it part-time over two or three years.

On this course you will take compulsory modules in research techniques, data mining, statistics and artificial intelligence or visualisation.

Alongside this you’ll take two optional modules from a range – which may include applications programming, database manipulation, human computer interaction, computer vision or a research topic.

A key element of the course is your dissertation, which will give you the chance to explore a topic or work on a problem (which may be with an industry partner) in depth, under the supervision of a member of faculty.

Recent dissertation titles include:

Classification rule induction for atmospheric circulation patterns
Keyword-based email classification
Data analysis of orthopaedic operations

Teaching and Learning

You will have an average of 15 hours of contact time per week with teaching staff, depending on your module choices. This will be made up of a mixture of lectures, seminars and lab classes – where the lab and seminar classes reinforce and expand on the lecture material.

The course has both theoretical and practical elements, so you’ll get hands on experience in commercial data mining and statistical software. You will even have the opportunity to participate in commercial data mining projects as part of your assessment, gaining experience on all the stages of the KDD process.

Independent study

Your individual study (around 25 hours per week) will complement the formal teaching and will evolve along with your skills and expertise in data analysis. Beginning with an initial focus on the basics of programming and data manipulation, you’ll move on to much deeper study and appreciation of specialist topics such as data mining and statistics.

Your dissertation will also form a key part of your course, which will involve extensive independent study supported by your supervisor.

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Assessment for Year 1

Compulsory Modules

DISSERTATION

Code CMP-7027X

Credits 60

In this module, each Masters student is required to carry out project work with substantial research and practical elements on a specified topic for their MSc dissertation from January to late August. The topic can be chosen and allocated from the lists of proposals from faculty members, or proposed by students themselves with an agreement from their supervisor and also an approval from the module organiser. The work may be undertaken as part of a large collaborative or group project. A dissertation must be written as the outcome of the module.

DATA MINING

Code CMP-7023B

Credits 20

This module is designed for postgraduate students studying on MSc courses. You will explore the methodologies of Knowledge Discovery and Data Mining (KDD). You will cover each stage of the KDD process, including preliminary data exploration, data cleansing, pre-processing and the various data analysis tasks that fall under the heading of data mining, focusing on clustering, classification and association rule induction. Through this module, you will gain knowledge of algorithms and methods for data analysis, as well as practical experience using leading KDD software packages.

APPLIED STATISTICS

Code CMP-7008B

Credits 20

This is a module designed to give students the opportunity to apply statistical methods in realistic situations. While no advanced knowledge of probability and statistics is required, we expect students to have some background in probability and statistics before taking this module. The aim is to teach the R statistical language and to cover a range of topics in applied statistics, such as: Linear Regression.

RESEARCH TECHNIQUES (RESEARCH METHODS)

Code CMP-7030Y

Credits 20

This module aims to prepare postgraduate students with necessary intellectual and practical skills for successfully carrying out research work for their MSc Dissertation in Computing Sciences and Computational Biology. Specifically, it teaches research methodologies, techniques and tools used in computing sciences. More importantly, it provides systematic training to enhance students' transferable skills and their understanding in ethics, social and legal issues involved in computing professions.

Optional A Modules

(20 credits)

INFORMATION VISUALISATION

Code CMP-7022B

Credits 20

This module is an introduction to information visualisation. You will learn techniques for summarising and presenting a wide range of data. There is a strong emphasis on understanding the appropriate context and use of visualisation techniques. You will also learn about problems and techniques for dealing with large data flows and issues of integrating multiple data sources.

ARTIFICIAL INTELLIGENCE

Code CMP-7028A

Credits 20

This module will introduce you to core techniques in Artificial Intelligence. Topics covered may include state space representation and search algorithms, knowledge representation, expert systems, Bayesian networks, Markov Models, Neural networks, Deep learning and an Introduction to Robotics and Drone.

Optional B Modules

(40 credits)

APPLICATIONS PROGRAMMING

Code CMP-7000A

Credits 20

The module aims to establish a clear understanding of Object Oriented Programming (OOP) and essential Objected Oriented Methodologies for developing application software. It teaches Java programming language and uses it as a vehicle to learn important concepts, such as objects, classes, inheritance, encapsulation and polymorphism. It also covers the Unified Modelling Language (UML) as a tool for object-oriented analysis and design, software development life cycle models, software testing strategies and techniques and version control.

AUDIO AND VISUAL PROCESSING

Code CMP-7016A

Credits 20

This module explores how computers process audio and video signals. In the audio component, the focus is on understanding how humans produce speech and how this can be processed by computer for speech recognition and enhancement. Similarly, the visual component considers the human eye and camera, and how video is processed by computer. The theoretical material covered in lectures is reinforced with practical laboratory sessions. The module is coursework only and requires you to build a speech recogniser capable of recognising the names of students studying the module using both audio and visual speech information.

HUMAN COMPUTER INTERACTION

Code CMP-7018A

Credits 20

Human Computer Interaction (or UX) covers a very wide range of devices, including conventional computers, mobile devices and “hidden” computing devices. In this module you will learn about interactions from a variety of perspectives, such as cognitive psychology, ethnographic methods, security issues, UI failures, the principles of good user experience, heuristic and experimental evaluation approaches and the needs of a range of different audiences.

INFORMATION VISUALISATION

Code CMP-7022B

Credits 20

DATABASE MANIPULATION

Code CMP-7025A

Credits 20

This module introduces you to important aspects of databases, database manipulation and database management systems. The module is based on the relational model. You will explore the tools and methods for database design and manipulation as well as the programming of database applications. Part of the practical experience you will gain will be acquired using a modern relational database management system. You will also gain programming experience using SQL, and using a high level programming language to write applications that access the database.

COMPUTER VISION

Code CMP-7026B

Credits 20

Computer Vision is about “teaching machines how to see”. You will study methods for acquiring, analysing and understanding images in both lectures and laboratories. The practical exercises and projects that you undertake in the laboratory will support the underpinning theory and enable you to implement contemporary computer vision algorithms.

ARTIFICIAL INTELLIGENCE

Code CMP-7028A

Credits 20

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Important Information

Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, the University will endeavour to consult with students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff. In some cases optional modules can have limited places available and so you may be asked to make additional module choices in the event you do not gain a place on your first choice. Where this is the case, the University will inform students.

MSc DATA SCIENCE

MSC DATA SCIENCE

About

WHATEVER YOU LOVE, GO FURTHER

Important Information

After the Course

Career destinations

WHATEVER YOU LOVE, GO FURTHER

Assessment for Year 1

Structure

Teaching and Learning

Assessment for Year 1

Compulsory Modules

Optional A Modules

Optional B Modules

Important Information

Entry Requirements

Degree classification

Degree subject

Students for whom english is a foreign language

Intakes

WHATEVER YOU LOVE, GO FURTHER

Fees and Funding

Course related costs

WHATEVER YOU LOVE, GO FURTHER

How to apply

FURTHER INFORMATION

WHATEVER YOU LOVE, GO FURTHER

WHATEVER YOU LOVE, GO FURTHER

Course Variants

MSC Data Science (Part Time 2 Year)

MSC Data Science (Part Time 3 Year)

Computing Sciences

MSc DATA SCIENCE

MSC DATA SCIENCE

About

WHATEVER YOU LOVE, GO FURTHER

Important Information

After the Course

Career destinations

WHATEVER YOU LOVE, GO FURTHER

Assessment for Year 1

Structure

Teaching and Learning

Assessment for Year 1

Compulsory Modules

Optional A Modules

Optional B Modules

Important Information

Entry Requirements

Degree classification

Degree subject

Students for whom english is a foreign language

Intakes

WHATEVER YOU LOVE, GO FURTHER

Fees and Funding

Course related costs

WHATEVER YOU LOVE, GO FURTHER

How to apply

FURTHER INFORMATION

WHATEVER YOU LOVE, GO FURTHER

WHATEVER YOU LOVE, GO FURTHER

Course Variants

MSC Data Science (Part Time 2 Year)

MSC Data Science (Part Time 3 Year)

Related Courses

MSC Computing Science

MSC Advanced Computing Science

MSC Cyber Security

Computing Sciences