MSc Data Science (Part Time 3 Year)

Key details

MSC DATA SCIENCE (PART TIME 3 YEAR)

Start Year

2020

Attendance

Part Time

Award

Degree of Master of Science

The unique structure of this course offers a truly part-time route for mature professionals to supplement their work with an accredited qualification. The MSc requires students to study one module per semester over a 3 year period. The timetable for this course is designed so that students are able to study alongside working, and contact time for each module is scheduled as a maximum of one day per week.

The course follows the successful model of the KDD MSc (Industry Based), which we have run for our partner Aviva for over 14 years. During this time we have produced 80 outstanding graduates for Aviva, bolstering their workforce with Master’s-level recognition.

The course was short-listed as one of the most innovative collaborations between Business and a University by the East of England Development Agency.

This MSc is one of the few similar academic qualifications to have been conceived and developed to meet the specific needs of industry partners.

The course teaches skills which are directly relevant to industry, as the sector increasingly seeks to expand its use of data analytics.

Students will be able to see the direct impact of the course on their vocational work, as some of the modules will involve projects using their company’s data. This will allow students to integrate their university projects into their job, putting the principles they have learnt from the classroom into practice.

Companies will directly benefit from a relationship with the university which will involve access to state-of-the-art expertise in topical subjects such as data mining, statistics and information retrieval as well as artificial intelligence and database manipulation.

Some modules consider the experience and knowledge which students have acquired at work, and may seek to involve line managers in assessing participants’ skills.

The MSc represents excellent value for money, as employers are able to part fund high quality training to incentivise staff and increase retention. The added bonus of only having to give participants a single day off per module makes the programme much more cost effective than other similar programmes.

Full details about the course structure can be found here.

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, statistics and artificial intelligence with the chance to customise your degree through modules in programming, database manipulation and NLP.

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 one of the largest Data Mining, Machine Learning and Statistics research groups in the UK, which has made significant contributions to the field in the last 10 years, so you’ll be working directly with pioneering experts.

WHATEVER YOU LOVE, GO FURTHER

Whether you want to diversify or specialise – explore your options.

GO FURTHER >

Course Reference Number: 664662

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

Teaching

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.

Assessment for Year 2

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.

Recent dissertation titles include:

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

Teaching and Learning

Teaching

Independent study

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

Assessment for Year 3

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.

Recent dissertation titles include:

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

Teaching and Learning

Teaching

Independent study

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

Assessment for Year 1

Compulsory Modules

Optional A 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

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.

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.

DATABASE MANIPULATION

Code CMP-7025A

Credits 20

This module looks into how best to perform database manipulation, exploring techniques that allow us to use and manage data of various types efficiently. We will examine methods to ensure that correctness of data, in terms of availability, reliability, consistency and scalability can be achieved and maintained. In turn, this will provide opportunities to turning data into knowledge intelligently and informing stakeholders alike and helping them to make better decisions.

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

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.

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.

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.

This is your SECOND year of study.

Assessment for Year 2

Compulsory Modules

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)

AUDIO AND VISUAL PROCESSING

Code CMP-7016A

Credits 20

HUMAN COMPUTER INTERACTION

Code CMP-7018A

Credits 20

INFORMATION VISUALISATION

Code CMP-7022B

Credits 20

DATA MINING

Code CMP-7023B

Credits 20

DATABASE MANIPULATION

Code CMP-7025A

Credits 20

COMPUTER VISION

Code CMP-7026B

Credits 20

ARTIFICIAL INTELLIGENCE

Code CMP-7028A

Credits 20

APPLICATIONS PROGRAMMING

Code CMP-7000A

Credits 20

APPLIED STATISTICS

Code CMP-7008B

Credits 20

Important Information

This is the THIRD year of study

Assessment for Year 3

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.