Everything you need to know...
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What is the fee?
Home: £10,940 for the course
International/EU: £18,600 for the course -
How long will I study?
1 Year
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Where will I study?
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When do I start?
September 2026
January 2027
Work Placement Route
For international students wishing to undertake a placement as part of this course, you must apply to the work experience route. Click here to go to the MSc Big Data Analytics course page. Transferring to the work experience route later will not be possible due to visa restrictions.
Course summary
- Learn how to use industry relevant software.
- Explore SAS, R, Python and the Apache Hadoop Ecosystem.
- Gain knowledge of how to store, mine and statistically model data.
- Understand how to use machine learning and AI to analyse large datasets.
- Study topics related to the management, distribution and integration of data.
On this course, you’ll undertake a complete end-to-end business intelligence process within big data analytics. This includes data mining, transforming and reporting on data – with a focus of interpreting the information in a business context – preparing you to pursue a career in a sector with high demand for workers.
Come to an open day
Find out more at our postgraduate open days. Book now for your place.
How you learn
On this course you’ll gain key skills and knowledge in data collection, storage, processing, analysis and visualisation. Through lectures and practical tutorials, you’ll learn how to apply big data tools and systems to different data tasks and problems – as well as how to analyse and communicate data effectively.
The main aim is to prepare you for employment in big data analytics, so we keep a close eye on industry developments while carefully choosing the topics and technical content of the modules you’ll study. While consulting with our industry partners and connections, we work with subject matter experts to understand the latest industry trends and needs.
As part of the course, you’ll also develop your research skills, preparing to undertake your own significant piece of work in the field as a dissertation project. This allows you to develop and tailor your understanding of a specific data-related topic that’s relevant to your career aspirations. You’ll be supported by a dissertation supervisor who is a subject matter expert.
You learn through:
- lectures
- hands-on lab sessions and tutorials
- regular feedback
- teamwork and group-based learning
- practice-based applied learning
- discussions
- self-study
Key themes
The MSc in Big Data Analytics course emphasises mastering industry-relevant software like SAS, R, Python, and Hadoop. You’ll learn to apply machine learning and AI techniques to large datasets and tackle real-world problems through hands-on projects.
You’ll learn through practice – starting by understanding the organisational data and developing business questions around the ’who, what, where and when’ principles. You’ll then bring together multiple real-time datasets to develop a decision-support system, making recommendations by interpreting the data with Online Analytical Processing (OLAP) tools.
The course also focuses on business intelligence processes, teaching you to interpret data in a business context and make informed decisions. Ethical considerations and effective communication of data insights are integral – you’ll be gaining the skills and knowledge you’ll need for high-demand roles in today's data-driven industries.
Applied learning
Work Experience
This course also has a work experience route that offers a placement in industry of up to 12 months. For further information, please see MSc Big Data Analytics (Work Experience).
Live projects
You’ll use your skills and knowledge to solve data-related problems by working on real-world projects with online data.
For example, you could produce a month-by-month breakdown of flight delays, by destinations, airlines and weather, for a specific date range, to help airports better understand how to make their operations more efficient. Or you could be working with data in relation to policing, energy or road traffic accidents – creating data-driven solutions for everyday real-world challenges.
Networking Opportunities
Throughout the course you’ll have numerous networking opportunities to help you with your career – from career fairs and workshops, to employer presentations and visits, as well as guidance from professional advisers.
Sheffield Hallam University is an SAP Student Academy and a founding member of the SAP University Alliance.
Course leaders and tutors
Richard Wilson
Senior LecturerRichard Wilson is the course leader for the MSc in Big Data Analytics within the College of Business, Technology and Engineering, department of computing, and the Ma … Read more
Modules studied may differ depending on when you start your course.
Modules
Important notice: The structure of this course is periodically reviewed and enhanced to provide the best possible learning experience for our students and ensure ongoing compliance with any professional, statutory and regulatory body standards. Module structure, content, delivery and assessment may change, but we expect the focus of the course and the learning outcomes to remain as described above. Following any changes, updated module information will be published on this page.
Modules studied may differ depending on when you start your course.
Final year
Compulsory modules
This module specialises in data management.
You’ll cover topics such as:
- Choosing appropriate datasets
- Acquiring datasets
- Handling different types of data
- Identify appropriate tools for the management of data
- Integration of tools and data
- Identify and apply appropriate data cleansing techniques
- Data storage methods
- Evaluate data visualisation techniques and their application
- Interpret output in a business context
This module explores how to use Python for data analysis, machine learning, and artificial intelligence. This will include the creation of computational models that can, for example: predict outcomes, analyse emotions, recommend products, and recognise objects.
You’ll study topics such as:
- Python for scientific computing
- Data visualisation with Python
- Python libraries for machine learning and artificial intelligence
- Neural networks and deep learning models
- Generative AI models
- Reinforcement learning models
- Recurrent neural networks
- Deep learning techniques for computer vision
- Natural language processing
- Time series forecasting with deep learning
This module will apply the technical knowledge and understanding developed across the course into an original research project. This will include critical and ethical analysis of literature, synthesising the findings and tailoring them to the specific context of their research projects using a suitable research methodology.
You’ll study topics such as:
- Critical and ethical review of the literature
- Selection and application of appropriate research tools, techniques, or methods
- Design and development of the artefact/prototype
- Testing and user evaluation
- Critical reflection – evaluating the project deliverables and project success/failure
- Legal, social, and ethical considerations in the design and development of computer-based systems
- Sustainable development and deployment
This module explores the application of statistical ideas and techniques in the analysis of data. This includes sophisticated data mining methods such as supervised and unsupervised.
You’ll study topics such as:
- The modelling cycle
- Statistical test and confidence intervals
- Data Mining Methodologies such as CRISP-DM and SEMMA
- Data Preparation and Manipulation
- Unsupervised learning and Dimension reduction techniques
- Cluster Analysis (variable and observation)
- Principal Components
- Rule association
- Supervised learning techniques
- Simple and Multiple Regression
- Logistic Regression
- Application of Regression and Logistic Regression to data mining
- Decision trees
- Evaluation of statistical modelling and data mining techniques
This module covers programming in Python and R, including key concepts of modern programming languages, such as object-oriented.
You’ll study topics such as:
- Fundamental programming concepts, such as variables, data types, operators, expressions, control structures, and loops
- Functions, methods, parameters, and arguments to organise and reuse your code
- Design and develop programmes using an algorithmic approach
- Data structures, such as lists, dictionaries, sets, and strings, to store and manipulate data
- Principles of object-oriented programming and implement classes, objects, methods, and inheritance
- Libraries and frameworks to create GUI, databases, or networking applications
This module develops skills to design and conduct rigorous research projects in computing.
You’ll study topics such as:
- Identify research problems: Explore current challenges and opportunities in computing to uncover relevant research areas
- Formulate research questions: Craft precise questions, aims, and objectives aligned with identified problems
- Review literature and artifacts: Critically analysing existing research to synthesize knowledge and identify gaps
- Propose research methods: Evaluate and select appropriate research techniques specific to your computing area
- Communicate research proposals: Craft comprehensive proposals that meet professional, legal, and ethical standards
Future careers
Our MSc in big data analytics is designed to enhance your data analysis skills – providing new personal insights and skill development to help you advance your career. You’ll gain the skills, knowledge and experience that modern organisations seek from their data analysts.
Previous graduates have gone on to develop successful careers in senior roles – in a wide range of opportunities across the world, in fields such as:
- customer insight analysis
- credit risk analysis
- fraud analysis
- forensic analysis
- big data architecture
- business intelligence development
- digital analysis
- data science
Previous graduates of this course have gone on to work for:
- The Bank of England
- HSBC
- Tata Consultancy Services
- HM Revenue and Customs
- Axis Bank
Equipment and facilities
You’ll engage in learning across our Extended Campus – which includes flexible physical and digital spaces to support applied learning, working, teaching and collaboration. On campus, our Computing Department will be your home space, with areas to take part in course activities and meet students, staff and employers.
On this course you work with:
- specialist platforms like Azure
- specialist software like Python, R Studio, SAS and Tableau
- specialist data management applications like Hadoop EcoSystem
Where will I study?
You study at City Campus through a structured mix of lectures, seminars and practical sessions as well as access to digital and online resources to support your learning.
City Campus
City Campus is located in the heart of Sheffield, within minutes of the train and bus stations.
City Campus map | City Campus tour
Adsetts library
Adsetts Library is located on our City Campus. It's open 24 hours a day, every day.
Learn moreLearn more about your department
Computing facilities tour
Take a look around the facilities and equipment in the Department of Computing at Sheffield Hallam University.
Entry requirements
All students
A good honours degree in computing, computer science, maths or statistics or other relevant areas or equivalent.
We consider your application if you do not have a relevant degree but have at least one year's direct work experience in computing or a relevant area. You may also be able to claim credit points which can reduce the amount of time it takes to complete your qualification at Sheffield Hallam.
Non-native speakers of English need an IELTS score of 6.0 with 5.5 in all skills (or equivalent).
Additional information for EU/International students
If you are an International or non-UK European student, you can find out more about the country specific qualifications we accept on our international qualifications page.
For details of English language entry requirements (IELTS), please see the information for 'All students'.
Fees and funding
Home students
Our tuition fee for UK students starting full-time study in 2026/27 is £10,940 for the course.
If you are studying an undergraduate course, postgraduate pre-registration course or postgraduate research course over more than one academic year then your tuition fees may increase in subsequent years in line with Government regulations or UK Research and Innovation (UKRI) published fees. More information can be found in our terms and conditions under student fees regulations.
International students
Our tuition fee for International/EU students starting full-time study in 2026/27 is £18,600 for the course.
Scholarships and financial support
Find information on scholarships, bursaries and postgraduate student loans.
International scholarships up to £3000 ›
Alumni scholarships up to £2000 ›
Postgraduate loans for UK students ›
Additional course costs
The links below allow you to view estimated general course additional costs, as well as costs associated with key activities on specific courses. These are estimates and are intended only as an indication of potential additional expenses. Actual costs can vary greatly depending on the choices you make during your course.
General course additional costs
Additional costs for School of Computing and Digital Technologies (PDF, 600.1KB)Legal information
Any offer of a place to study is subject to your acceptance of the University’s Terms and Conditions and Student Regulations.
