Data Science MSc

Start date: September
Duration: 1 year full-time or 2/3 years part-time
Programme codes: INFT85 (full-time), INFT121 (3yr part-time), INFT158 (2yr part-time)
Accreditation: Accredited by the Chartered Institute of Library and Information Professionals (CILIP).



All organisations face the challenge of how to analyse and use data, and the most successful are those that handle and exploit it effectively. More and more organisations therefore want Data Scientists.

Why should I take this course?

On this course, you will learn data science concepts and their application within organisations to support data-driven approaches to problem solving. You will be provided with a set of fundamental principles that support principled extraction of information and knowledge from data. Case studies will help to show the practical application of these principles to real life problems. You will also learn how data can be captured, cleaned, aggregated, organised, analysed and used, together with practical experience of data modelling, data mining, data analysis and visualisation. Throughout the programme there will be opportunities to gain hands-on experience using a variety of tools, such as R and SPSS, Weka, and Tableau/Spotfire. Your dissertation will enable you to undertake in-depth research into your particular areas of interest.

What will I gain?

The Data Science Masters degree will provide you with an in-depth understanding of the theory and practice of Data Science and its application in different organisational contexts.

You will also gain practical skills in handling structured and unstructured data, analysing and visualising data, data mining, as well as gaining hands-on experience of software tools used and their use in real-world settings. You will gain the skills of a “data manager” who understands what the algorithms (e.g., for data mining or handling ‘Big Data’) can do and when to use them for the benefit of the organisation.

What will I learn?

The programme is centred on three key aspects of data science: (i) fundamental data-related principles, (ii) supporting infrastructures, and (iii) organisational context.

The first semester introduces the key concepts and principles of Data Science to help you put into context what is being taught in the programme. Right from the start you will be engaging with Data Science as an emerging research field to develop your own understanding of the subject. The first semester will also introduce approaches used to analyse data and organise and manage unstructured data, one of the fastest growing sources of data. You will be able to select from elective modules covering topics such as information systems modelling and information architecture.

In the second semester you will continue to learn fundamental data analysis principles, and in particular be taught methods from data mining and visualisation and how these are used in supporting decision-making. You will also be taught methods for modelling and organising structured data within the context of database design. You will also have the opportunity to choose elective modules from topics including Business Intelligence, Information Governance, Researching Social Media and Research Data Management. At the same time, you will start to think about your dissertation or project, and will learn more about developing a proposal, literature reviews, research methods, as well as qualitative approaches to data analysis that will complement the quantitative methods taught in the first semester.

The below video is an example of the kinds of questions we explore on the Data Science programme relating to the social impace of data science techniques such as data mining.

During the summer, you will undertake a research project or dissertation, for submission at the beginning of September.

How will I be taught and assessed?

A variety of teaching methods are used combining lectures from academic staff and professional practitioners with seminars, tutorials, small-group work, and computer laboratory sessions. There is a strong emphasis on new ways of exploiting data to support decision-making for a range of domains and problems in an organisational context. In addition to the taught components, you will be expected to engage in independent study, reading and research in support of your coursework.

Assessments are designed to test your grasp of the theoretical principles, technologies and frameworks used to collect, store, analyse and exploit information; they may include essays, report writing, oral presentations, in-class tests and group projects.
There is a dissertation of 10-15,000 words, which provides you with the opportunity to focus in depth on a topic of your choice with one-to-one supervision. Opportunities exist for both project and dissertation studies to be carried out with our collaborating organisations, which can provide the opportunity to tackle 'real-life' problems.

In addition, an important element of the course is the acquisition and development of the transferable skills needed in today’s workplace. These include skills in oral and written communication, developed through doing presentations and report writing as part of assessed work. Organisational and teamworking skills are developed through group work. We seek to develop your management and leadership capabilities on the course too.

Where will I study?

Our campus and how we use it

We timetable teaching across the whole of our campus, the details of which can be found on our campus map. Teaching may take place in a student’s home department, but may also be timetabled to take place within other departments or central teaching space.


The full-time Data Science MSc is a 12 month programme, running from September to September. Teaching consists of two 15-week semesters, from late September to the following June. You will then write your dissertation and finish in mid-September.

Students studying part time will complete their course over two years.

Modules have a value of credits: 180 credits are required for graduation. 75 of these are compulsory (Core) modules, 45 are elective and 60 credits are allocated to the dissertation.

Core modules (modules that you have to take):
Semester Credits
INF4000 Data Visualization 1 15
INF6027 Introduction to Data Science 1 15
INF6029 Data Analysis 1 15
INF6033 Data and Society 1 15
INF6028 Data Mining 2 15
INF6050 Database Design 2 15
You must also take units to the value of 30 credits from the following:
INF6040 Business Intelligence 2 15
INF6025 Information Governance and Ethics 2 15
INF6024 Researching Social Media 2 15
INF6034 Digital Advocacy 2 15
INF6032 Big Data Analytics 2 15
INF6430 User Interface Design and Human Computer Interaction 2 15
You will also take:
INF6000 Dissertation Summer 45
INF6340 Research Methods and Dissertation Preparation 2 15

Other Courses

  • Postgraduate Certificate requires a total of 60 Credits
  • Postgraduate Diploma requires a total of 120 Credits
Entry Requirements

The Data Science MSc programme is aimed at graduate students with a good honours degree, who have enquiring minds, a practical and analytical approach to problem solving and an ambition for a career in this area. Work experience is not essential and you do not need prior knowledge of statistics or data analysis.

Standard Entry Requirements

You are normally expected to have a minimum 2:1 undergraduate honours degree, or its equivalent, in any subject discipline.

Work experience is not essential and you do not need prior knowledge of statistics or data analysis. However, the course is analytical and does require that you are prepared to learn and engage with more analytical and technical topics, such as data analysis, database design, data mining and data visualisation. The course does not involve programming but you will be expected to be able to make use of analytical and data-centric tools, such as R, SPSS and Oracle.

English Language Entry Requirements

If your first language is not English you need to provide documentary evidence of English Language competence. You must meet the following minimum requirements:

Overall Score 6.5
Listening 6.0
Reading 6.0
Speaking 6.0
Writing 6.0

Details of other qualifications recognised by the University of Sheffield can be found on the English language requirements webpage. You can also compare grades for English language assessments on the English Language Teaching Centre website.

If your application is successful you may need to attend an English Language class in the University before or during the course.

Staged admissions process

Applications to this course are assessed using our staged admissions process.

Stage For applications received by: We aim to return decisions by:
A 14 October 2019 28 October 2019
B 30 November 2019 14 December 2019
C 14 January 2020 28 January 2020
D 29 February 2020 14 March 2020
E 14 April 2020 28 April 2020
F 31 May 2020 14 June 2020
G 14 July 2020 28 July 2020

In some cases, because of the high volume of applications we receive, we may need more time to assess your application. If this is the case, we will assess your application in the next stage. We will let you know if we intend to do this.

We may be able to consider applications received after 14 July 2020 if places are still available.

Study places are offered subject to availability. Given the popularity of these courses, places may not be available if you apply later in the cycle.

If we offer you a place, we will ask you to accept the offer and pay a tuition fee deposit (relevant International students only). If you do not accept the offer and pay the deposit within four weeks of the date of the offer letter, we may withdraw our offer.

You can find information about the process on our staged admissions web page:

Staged admissions for postgraduate applications

Open Days

If your application is successful we will invite you to attend an Open Day at the School. We hold these several times a year, and will inform you of possible dates when we contact you. Attendance at an open day is optional but will give you the opportunity to see our facilities and to meet the academic staff and current students.

There is currently no closing date for applications for the coming academic year, but we encourage you to apply as early as possible.


Your career prospects

Our course has been designed with employers to make sure you develop the skills they're looking for. The experience and skills you gain on this course will prepare you for a range of jobs within data management and analysis in a wide variety of sectors, ranging from local government to healthcare. You can find out more in the video on the right which features Andy Ball from business intelligence consultancy Peak Indicators.

What people are saying about careers in data science

Information from central government, vendors such as Oracle and IBM, and consultancies such as PWC and Accenture, shows that the area of Data Science is the growth area over the next five years, with predictions of 20,000 jobs being created in the sector year on year.

In their ‘Big Data Analytics: An assessment of the demand for labour and skills, 2012 – 2017’ report, e-Skills UK and SAS predicted “at least 28,000 openings for big data staff in the UK each year” by 2017.

The 2011 McKinsey Global Institute report predicted that by 2018 in the USA, there could be a shortage “of 140,000 to 190,000 people with the deep analytical skills and know-how needed to analyse data and discover new insights”.

What jobs could I do?

Individuals working in data science hold a variety of business-focused and data-focused roles including:

  • Business Analyst
  • Business Intelligence Analyst
  • Data Scientist
  • Data Engineer
  • Data Manager
  • Data Analyst
  • Data Architect
  • Data Modelling
  • Data Mining Engineer

Who is employing graduates?

Recent graduates have been employed by:

  • Plusnet
  • Cancer Research UK
  • Intel
  • The LADbible Group
  • Ebyf
  • Peak INdicators
  • JPMorgan Chase
Course Team

The course lecturers are all research active in the areas that they teach. We also invite guest lecturers, who are leading thinkers and practitioners in the field so you will have a chance to talk with professionals about real life problems and solutions, as well as making contacts to build your professional network.

Course Coordinator

"I am Senior Lecturer in Data Science. I co-ordinate the Big Data Analytics module which is elective to Data Science. My research to date can be placed in the intersection of information systems (e.g., information retrieval and recommender systems), document analysis and data science.

I gained manifold work experience at research-led science & engineering centers in the US, Ireland, and Germany, as well as at leading universities in Germany, China, and the UK. Building on this experience, a further theme of my work is to bridge the gap between academia and industry, e.g., by collaborating closely with industry to incorporate real-world challenges in my teaching, or by creating opportunities for academics to benefit from companies’ resources or expertise."

Frank Hopfgartner

Dr Frank Hopfgartner

Deputy Coordinator

"I am a lecturer in Information Politics and Policy. I co-ordinate the Data and Society module, which is core to the data science programme. My research interests include the cultural and policy factors that shape the movement of data between different people and organisations, social biases in the outputs of search algorithms, and cultures of data mining practice.

I studied Politics and American Studies as an undergraduate at Keele University. I also have MA degrees in American Studies, and Library and Information Management. I completed my PhD on the Politics of the UK's Open Government Data initiative in 2013. I am an editor of Online Information Review - a well-known information science journal, and I am the Faculty of Social Sciences representative on the University's Research Ethics Committee."

Jo Bates 2014

Dr Jo Bates

Our course team benefits from an industrial advisor who has experience in data science and business intelligence:

Industrial Advisors

Andrew Ball is co-founder of Peak Indicators, and has over 20 years’ experience working in the BI & Data Warehousing area. Prior to Peak Indicators Andrew's roles included BI Practice Director for Oracle U.K. and Practice Director for Siebel's EMEA Analytics Competency Centre. Andy acts as an external advisor for the programme and worked with the academic team to shape the content of the course.

Andy Ball

Andrew Ball, Peak Indicators

Paul Clough is Data Scientist at Peak Indicators and helps to develop data products and services, produce educational resources and grow data science capability within Peak. Paul is also an academic and works part-time as Professor of Search and Analytics at the Information School, University of Sheffield. During his time in the department, as well as contributing to research and teaching activities, Paul has been head of the Information Retrieval Group, Director of Research, and founded the Data ScienceMSc programme. Paul continues to conduct research in areas including information retrieval, data analytics and natural language processing/text analytics. Prior to working at the University of Sheffield, Paul worked in R&D for British Telecommunications Plc.

Paul Clough 2014

Professor Paul Clough

I have noticed that similar courses at other Universities often require a mathematical or computer programming background, unlike this one. Is there a reason for this?

Yes, there are a number of reasons for this:

  1. As you say, there are lots of data science courses and we wanted to stand apart from them;
  2. We are aiming for "Type II" data scientists. Courses aimed at Type I data scientists, which are more technical and include programming for data science (e.g. python), are more usually delivered by Computer Science departments.
  3. We wanted to play to our strengths as an Information School - we have a lot of experience with managing and processing data and information, so a course for Type II data scientists is a good fit with our expertise. We aim to make our programmes accessible to people from a range of backgrounds.
  4. Our research found that there is a greater demand for this kind of role.
  5. Our programme coordinator was inspired by this book which will be one of the course textbooks. He particularly liked the focus on applying data analytics to helping solve business problems.
How many places are available on the course?

As this is a new programme we don't really have a cap on the number of students. Our other programmes vary from around 30 (Librarianship) to around 70 (Information Management).

I notice there is an frequently quoted statistic about the predicted increase in ‘Big Data’ jobs and an associated shortfall in suitable applicants; is this why you are introducing this course?

We have talked to representatives from business, current undergraduate students, and done some market research which, along with interests in the department in Big Data, social media and data analysis, led us to develop this course. The business people we spoke to told us that they were in need of people who were aware of data analysis and data associated technologies. In particular, they told us that they wanted people who could not just analyse data, but know how to use it to solve business problems (Type II data scientist).

Are there any other sources of information that you can recommend to help me decide whether I should take this course?

You might find it helpful to take a look at Jeff Stanton’s freely available data science book (which may be used for the Introduction to Data Science module).

Anything else you’d like to know? Please get in touch on

The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers.In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.