MSc Data Science (Part Time 2 Year)

Key details

MSC DATA SCIENCE (PART TIME 2 YEAR)

Start Year

2022

Attendance

Part Time

Award

Degree of Master of Science

Duration (years)

Great business decisions are underpinned by high quality data. As a data scientist you are absolutely integral to the success of an organisation – you will source, analyse and utilise vast amounts of data to support strategic decision-making.

If that’s where you aspire to be, then this course is for you. You will gain an advanced 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.

We have a large data mining, machine learning and statistics research group, which has made significant contributions to the field in the last 15 years, so you’ll be working directly with pioneering experts.

Data scientists are highly prized in almost any industry, so you’ll have great graduate career prospects as well as a wide range of transferable skills.

About

This degree follows the same programme as its full-time equivalent but is spread over two years.

Module enrolment for each year is discussed with the Course Director at the start of the course.

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 18 years of history – offers an excellent platform to help you forge a successful career in data science.

As a student, you'll be part of our vibrant research community and will have very good opportunities to progress to a PhD. You'll 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, Public Health England, Alston Transport, Derbyshire Police, Lanner Group, Master Foods, MET Office, National Air Traffic Services, Aviva, Process Evolution Ltd, Simultec AG Zurich, Virgin Money and the Norwich Football Club.

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.

September 2023 opportunities. Discover more

WHATEVER YOU LOVE, GO FURTHER

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

GO FURTHER >

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

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.

September 2023 opportunities. Discover more

Open Days

Assessment for Year 2

Structure

Part-time students will take the required compulsory modules and will choose optional modules according to their programme's profile. However, there will be flexibility on which year students take particular modules and choices will be made in conjunction with the academic adviser to ensure the best fit to work and other commitments.

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.

September 2023 opportunities. Discover more

Open Days

This is the 1st Year of your Taught Masters Programme. (You may choose 40 or 60 credits from the Compulsory Modules range in the first year). Students must take BOTH CMP-7030Y and CMP-7008B in year 1, but may choose to take CMP-7028A or CMP-7023B in year 2.

Assessment for Year 1

Compulsory Modules

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.

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.

DISSERTATION - P/T PROPOSAL

Code CMP-7039B

Credits 0

This module contains the Dissertation Proposal for CMP-7039X (DISSERTATION - P/T) and is a separate module because the proposal is submitted in the second half of the year proceeding the dissertation. The proposal will be evolved with your Supervisor and the mark will contribute 10% to the 60-credit Dissertation module.

Optional A Modules

(0-40 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-7028B

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

This module aims to establish a clear understanding of Applications Programming, from the initial Software Engineering design through to full development, coding and testing of a software program. You will be introduced to both Software Engineering and a modern programming language used in many Industries (Python). The module will provide you with the fundamental skills and knowledge for working in a range of computing science industries.

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-7028B

Credits 20

September 2023 opportunities. Discover more

Open Days

Important Information

Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring and review of modules. Where this activity leads to significant change to a programme and modules, the University will endeavour to consult with affected students. 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. Availability of optional modules may be restricted owing to timetabling, lack of demand, or limited places. Where this is the case, you will be asked to make alternative module choices and you will be supported during this process.

This is the 2nd Year of your Taught Masters Programme. (You may choose 60-80 credits from the Compulsory Modules range in the second year). Students must have taken CMP-7023B & CMP-7008B. It is also a requirement that students choose to study either CMP-7022B and CMP-7028A or both of these modules.Students must completed all modules under the Compulsory range between year 1 and 2.

Assessment for Year 2

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.

APPLIED STATISTICS

Code CMP-7008A

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.