MSc — 2026 entry Business Analytics
In today’s hyper-connected, data-driven world, organisations need professionals who can turn complex information into clear, actionable insight. Our practical MSc Business Analytics course equips you with the skills to do just that, combining artificial intelligence, machine learning, management science and analytical thinking. You’ll uncover the true potential of data, ensure smarter decision-making and help businesses drive meaningful growth and innovation.
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Why choose
this course?
- Elevate your analytical skills and transform the way you approach challenges. Our Business Analytics masters degree is designed to set you apart from the competition, graduating as a confident lateral thinker equipped with the latest theories and hands-on experience. Our graduates have gone on to secure roles at Amazon, Accenture and Deloitte.
- We understand the importance of blending theory and real-world experience – you will take part in real business projects, hear from industry guest speakers, and attend events. Recent highlights have included our Data to Impact challenge, talks from Executives-in-Residence, and professional networking and knowledge sharing events with IIBA and SAS.
- Learn from inspiring industry experts, who will share their invaluable insights in the classroom. You will be taught by academics like Professor Ali Emrouznejad, who is one of the top 2% most influential scientists globally by Stanford University.
- As a business analytics student, you’ll be based in Surrey Business School and be part of a vibrant community, focused on improving business practice and creating a sustainable and positive change.
- Our Business School has a strong focus on research and innovation. Its research centres include the Centre for Business Analytics in Practice, the Centre of Digital Economy, and the Centre for Social Innovation Management. The School also has a strong track record of entrepreneurship and enterprise.
We’re preparing you for the future by integrating AI into every course, building digital skills, confidence and creativity that employers value in tomorrow’s workplace.
Statistics
91%
Of our Surrey Business School graduates go on to employment or further study (Graduate Outcomes 2025, HESA).
Top 100 in the world
For business administration and for management in the ShanghaiRanking's Global Ranking of Academic Subjects 2025.
Top 10 for research
For the outputs of our business research (Research Excellence Framework 2021).
Accreditation and endorsment
What you will study
This degree in business analytics aims to take your analytics career to the next level and develop your ability to support and make decisions using big data confidently.
You'll master the art of descriptive, predictive, and prescriptive analytics, empowering you to unravel complex business dilemmas with ease. You will learn and apply a range of techniques and tools to analyse data related to a range of diverse business operations contexts.
Develop analytical skills employers seek
You’ll gain a deep and thorough understanding of quantitative analytical methodologies and learn through hands-on experience with a range of decision-making software and data management tools. You'll acquire the skills to decipher data-led insights and optimise businesses to their full potential.
Develop problem-solving skills
Your studies will focus on three major areas:
- Analysing business data
- Using data to solve business challenges
- Making data-driven business decisions.
On this course, you’ll learn how to apply new knowledge and demonstrate your skills prowess through practical problem-solving challenges.
Learn how to become a critical thinker
You’ll gain the ability to independently evaluate critical approaches and techniques relevant to business analytics. You'll learn how to relate existing knowledge structures and methodologies to analytical business challenges.
Learn how to manage analytics projects
You'll master the skill of running data analytics projects, from assessing and analysing raw data to preparing visualisations, and concluding by optimally communicating your results effectively to a select audience.
Software
You’ll get hands-on experience using a wide range of up-to-date software tools (such as SAS, SAP, STATA, R, Power BI, Excel, Simul8, and RISK) to support your studies, such as in data mining, resource planning and statistical analysis.
Professional recognition
MSc - Association to Advance Collegiate Schools of Business (AACSB)
Accredited by the Association to Advance Collegiate Schools of Business (AACSB).
MSc - SAS
The course's Data Mining and Text Analytics module is the first and only academic module in the country endorsed by SAS for the SAS knowledge and skills attained by students successfully completed the module, and is widely recognised by employers.
MSc - International Institute of Business Analysis (IIBA®)
This program is endorsed by IIBA (the International Institute of Business Analysis) and prequalifies for the professional development requirement of the IIBA Core Certifications: CCBA® and CBAP®.
This program also contributes towards the preparation for the IIBA®-Certification in Business Data Analytics ( IIBA® CBDA)
Students joining the programme in either September or February will study eight taught modules over the academic year, with four modules taken in each semester. The programme comprises five compulsory modules and a range of optional modules, enabling students to tailor their studies to their interests and career aspirations. Students entering in September and February follow the same curriculum and study the same modules, although the order in which some modules are delivered may differ between intakes.
In addition to the taught modules, all students undertake an individual dissertation project during a dedicated dissertation period. For students starting in September, the dissertation follows the completion of both taught semesters. For students starting in February, the dissertation is undertaken between the two taught semesters.
The structure of our programmes follows clear educational aims that are tailored to each programme. These are all outlined in the programme specifications which include further details such as the learning outcomes:
Modules
Modules listed are indicative, reflecting the information available at the time of publication. Modules are subject to teaching availability, student demand, and/or class size caps.
The University operates a credit framework for all taught courses based on a 15-credit tariff, meaning all modules are comprised of multiples of 15 credits.
Course options
Year 1
Semester 1
Compulsory
Data mining is the process of identifying anomalies, patterns, and correlations within large datasets to predict outcomes. It involves exploring databases to extract potentially useful information such as rules, structures, regularities, and hidden trends. A wide range of analytical techniques is used to transform this information into actionable insights that support decision-making, improve customer relationships, reduce risks, increase revenues, and optimise operational efficiency. In today's data-driven business environment, data mining and business analytics play a critical role in maintaining a competitive advantage. This module introduces key analytical approaches and case studies of well-known applications, including shopping basket analysis (such as loyalty card systems), fraud detection in financial transactions, stock market prediction, risk analysis in banking, web analytics, and social network analysis. In addition, the module introduces AI-driven analytics methods to enhance traditional data mining approaches. These include automated pattern discovery, predictive modelling, and the analysis of unstructured data such as text. Emphasis is placed on how these methods can support more accurate forecasting, deeper insight generation, and improved decision-making in real-world business contexts.
View full module detailsThis module provides an introduction to data analytics within a business context, focusing on the foundations of data-driven decision-making and the analytics lifecycle. Students will develop practical skills in data management, business intelligence, visualisation, and introductory machine learning techniques.The module introduces key concepts and frameworks used in business analytics, including the Cross-Industry Standard Process for Data Mining (CRISP-DM), relational databases, structured query language (SQL), exploratory data analysis, and dashboard development. Students will work with real-world datasets to develop analytical solutions that support managerial and operational decision-making.A strong emphasis is placed on transforming data into actionable insights through effective visualisation, storytelling, and business intelligence techniques. The module also introduces fundamental concepts in statistical learning and predictive analytics, providing a foundation for more advanced modules in machine learning and artificial intelligence.
View full module detailsThis module introduces methods for building, estimating, and interpreting statistical and econometric models focusing on the area of business analytics, and analyzing quantitative data for making better decisions. The module provides the theoretical foundation and intuitive knowledge, applied to business data by making use of econometric/statistical software.
View full module detailsOptional
The module provides the theoretical underpinnings of our MSc Accounting and Finance programme. It introduces the pivotal concepts which form the basis of theoretical finance under three broad headings; Portfolio Theory and Practice, Equilibrium in Capital Markets and Introductory Analysis of Asset Classes. Core concepts include the relationship between risk and return, the Capital Asset Pricing Model (CAPM) and the Efficient Market Hypothesis (EMH) but the module also extends this analysis into new theoretical areas such as Behavioural Finance.
View full module detailsThe module creates an understanding for the students about the role and importance of supply chain management and logistics for industries. It expands the perspective of students about the application of business analytics in different areas of supply chain management and provides knowledge to them about the challenges which supply chains are facing, e.g., environmental issues. The objectives of the module are: Providing theoretical and practical knowledge about: The principles, elements, and performance dimensions of logistics and supply chain management. The strategies used by companies when managing their supply chains by considering the types of products and the specifications of their market. The role of digitalisation and Information and Communication Technology (ICT) in managing logistics and supply chain operations. The role of sustainability practices across supply chains. The importance of logistics and supply chain management in eCommerce.
View full module detailsThe module examines the various approaches to equity investment analysis, providing a systematic understanding of the challenges faced and the decisions to be taken when analyzing and valuing corporate equity. It encompasses the principles and practice of valuation of companies’ shares. This is examined from several aspects; industry analysis, company analysis, valuation methods and the link between valuation and investment style. Real corporate analysis will be undertaken in order to highlight and evaluate the different approaches to investment analysis.
View full module detailsSemester 2
Compulsory
Operational Analytics is a core module for Business Analytics. Students will learn how to apply Operational Research techniques - the cornerstone of Management Science for the past 70 years - in a digital world rich with data. Students will learn various quantitative techniques (linear programming, Risk Analysis, Simulation Modelling) that are commonly used within OA. Importantly, these techniques will be studied in the context of the overall decision-making process, so that they are aware of why and how we turn data into actionable insights. Thus the module also covers more qualitative approaches such as problem-structuring and data visualisation.
View full module detailsThis module provides an applied introduction to machine learning and artificial intelligence (AI) within a business analytics context. It focuses on the design, implementation, evaluation, and interpretation of analytical models for data-driven decision-making.Students will develop knowledge of statistical and algorithmic machine learning methods, including supervised, unsupervised, and potentially reinforcement learning approaches. The module emphasises practical implementation using real-world datasets and modern data science workflows.Key topics include data preparation, model development, validation techniques, and ensemble methods. Neural networks, optimisation, and explainable artificial intelligence (XAI) may be considered. Students will critically evaluate analytical approaches and interpret model behaviour using appropriate evaluation and explainability techniques.The module also discusses emerging developments in AI (e.g. generative AI and large language models) and considers their opportunities and limitations in business applications.
View full module detailsThis module provides an introduction to data analytics within a business context, focusing on the foundations of data-driven decision-making and the analytics lifecycle. Students will develop practical skills in data management, business intelligence, visualisation, and introductory machine learning techniques.The module introduces key concepts and frameworks used in business analytics, including the Cross-Industry Standard Process for Data Mining (CRISP-DM), relational databases, structured query language (SQL), exploratory data analysis, and dashboard development. Students will work with real-world datasets to develop analytical solutions that support managerial and operational decision-making.A strong emphasis is placed on transforming data into actionable insights through effective visualisation, storytelling, and business intelligence techniques. The module also introduces fundamental concepts in statistical learning and predictive analytics, providing a foundation for more advanced modules in machine learning and artificial intelligence.
View full module detailsOptional
The module equips students with the knowledge and tools to implement financial models using Python. The course introduces students to the general principles of building financial models, as well as a number of specific financial modelling tools, including matrix calculations, optimization, regression analysis (both time-series modelling and panel data modelling), out-of-sample forecasting and simulation. These methods are applied to a range of practical problems in finance, including passive and active portfolio management, risk management and currency valuation. The emphasis of the course is on practical application of the theory, with lectures on each topic followed by in-depth practical classes, in which students work through real world problems using Python.
View full module detailsThis module introduces students to the principles of data and business process management and examines the opportunities, benefits and challenges that business process modelling and Enterprise Resource Planning (ERP) systems create for organisations and their management.The module has two complementary strands.First, through lectures, students develop a critical understanding of data and business process management and its organisational and managerial implications.Second, through practical laboratory sessions, students model and operationalise business processes using the SAP platform. These sessions provide hands-on experience of how information systems enable, integrate and transform organisational processes.Across all topics, students critically evaluate the advantages and limitations of technology-enabled business processes.
View full module detailsThis module is designed to introduce students to how mathematical and econometric methods can be used to model diverse transformation processes, to establish benchmarks of efficiency and productivity for organisations, and to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience in implementing an efficiency and productivity assessment with real case studies using appropriate software.
View full module detailsThis module introduces students to the concept and current practices in marketing analytics. Technology advances of the past decade have dramatically enhanced marketers' means of collecting and analysing data to measure the effectiveness of their marketing strategies. This module is designed to provide students with an overview of state-of-the-art marketing analytics practices that guide marketing executives in their strategic decisions. The module focuses on introducing students to key analytical techniques with an emphasis on interpreting results and generating strategic insights for marketing decision-making. The course will be of particular value to students planning careers in marketing and management consulting. The course is designed for students with a basic understanding of univariate and bivariate statistics. Addressing different learning styles, the following teaching methods are applied in this course: Pre-readings, Lectures, Class Exercises, Class Discussions, and Real World Cases/Industry Insights.
View full module detailsAcross academic years
Compulsory
The module is compulsory for all MSc students and is the final element of the programme, providing an opportunity for a sustained period of independent study and research. It allows students to concentrate on topics that are of particular interest to them and it draws upon a range of different aspects of the taught programme particularly the analytical and quantitative methods they learn throughout the course. It also gives an opportunity for students to work independently with individual supervision. The module can take one of two different formats: a) Dissertation - An academic piece of work. This form of dissertation follows the standard academic pattern of identifying a topic arising from a gap in the literature and developing a methodology to explore this area in depth. b) Project - A business or applied piece of work. This form of project starts with an emerging business problem, either provided from an industrial partner or with their co-operation in the process, and seeks to provide a research based solution to or exploration of the problem. Any engagement with external party needs approval. Both formats of the written piece of work seeks to develop the same learning outcomes and follow the same assessment criteria.
View full module detailsThe module is compulsory for all MSc students and is the final element of the programme, providing an opportunity for a sustained period of independent study and research. It allows students to concentrate on topics that are of particular interest to them and it draws upon a range of different aspects of the taught programme particularly the analytical and quantitative methods they learn throughout the course. It also gives an opportunity for students to work independently with individual supervision. The module can take one of two different formats: a) Dissertation - An academic piece of work. This form of dissertation follows the standard academic pattern of identifying a topic arising from a gap in the literature and developing a methodology to explore this area in depth. b) Project - A business or applied piece of work. This form of project starts with an emerging business problem, either provided from an industrial partner or with their co-operation in the process, and seeks to provide a research based solution to or exploration of the problem. Any engagement with external party needs approval. Both formats of the written piece of work seeks to develop the same learning outcomes and follow the same assessment criteria.
View full module detailsOptional modules for Year 1 (full-time) - FHEQ Level 7
For further information regarding programme structure and module selection, please refer to the course catalogue.
General course information
Contact hours
Contact hours can vary across our modules. Full details of the contact hours for each module are available from the University of Surrey's module catalogue. See the modules section for more information.
Timetable
New students will receive their personalised timetable during Welcome Week. In later semesters, at least one week before the start of the semester.
Scheduled teaching can take place on any day of the week (Monday – Friday), with part-time classes normally scheduled for one or two days. Wednesday afternoons tend to be for sports and cultural activities.
View our code of practice for the scheduling of teaching and assessment (PDF) for more information.
Location
This course is based at Stag Hill campus. Stag Hill is the University's main campus and where the majority of our courses are taught.
We offer careers information, advice and guidance to all students whilst studying with us, which is extended to our alumni for three years after leaving the University.
Of our Surrey Business School graduates are in employment or further study within 15 months of graduating (Graduate Outcomes 2025, HESA).
By studying this course you will gain unparalleled expertise that will make you an indispensable asset in any organisation.
Business analytics students often pursue careers such as:
- Data scientist
- Data analyst
- Finance analyst
- Operations manager
- Consultant (Power BI/Tableau, business strategy, analytics)
- Business analytics manager
- Business intelligence analyst.
Stefan Dimitrov Stoyanov
Student - Business Analytics MSc
Christiana Demetriou
Student - Business Analytics MSc
UK qualifications
A minimum of a 2:2 UK honours degree in either computer science, economics, engineering, finance or mathematics, or a recognised equivalent international qualification.
We'll also consider a minimum of three years relevant work experience in an analytical and data-intensive field if you don’t meet these requirements.
English language requirements
IELTS Academic: 6.5 overall and 6.0 in each other element.
These are the English language qualifications and levels that we can accept.
If you do not currently meet the level required for your programme, we offer intensive pre-sessional English language courses, designed to take you to the level of English ability and skill required for your studies here.
International Pre-Masters
Prepare for postgraduate study and boost your career prospects. This is an intensive programme of academic subjects, study skills and English language preparation designed to help you succeed.
Credit Transfer and Recognition of Prior Learning
We recognise that many students enter their course with valuable knowledge and skills developed through a range of ways.
If this applies to you, the recognition of prior learning process may mean you can join a course without the formal entry requirements, or at a point appropriate to your previous learning and experience.
There are restrictions on some courses, and fees may be payable for certain claims. Please contact the Admissions team with any queries.
Scholarships and bursaries
Discover what scholarships and bursaries are available to support your studies.
Fees per year
Explore UKCISA’s website for more information if you are unsure whether you are a UK or overseas student. View the list of fees for all postgraduate courses.
- These fees apply to the academic year 2026-27 only. Fees are reviewed annually, and tuition fees may increase for courses running over more than one year.
Payment schedule
- Students with Tuition Fee Loan: the Student Loans Company pay fees in line with their schedule (students on an unstructured self-paced part-time course are not eligible for a Tuition Fee Loan).
- Students without a Tuition Fee Loan: pay their fees either in full at the beginning of the programme or in two instalments as follows:
- 50% payable 10 days after the invoice date (expected to be October/November of each academic year)
- 50% in January of the same academic year.
- Students on part-time programmes where fees are paid on a modular basis: cannot pay fees by instalment.
- Sponsored students: must provide us with valid sponsorship information that covers the period of study.
The exact date(s) will be on invoices.
Additional costs
Students are required to pay the upfront cost of travel and accommodation expenses incurred when on placement when not covered by the placement provider. These may vary depending on the location.
Funding
You may be able to borrow money to help pay your tuition fees and support you with your living costs. Find out more about postgraduate student finance.
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Once you apply, you can expect to hear back from us within 14 days. This might be with a decision on your application or with a request for further information.
Our code of practice for postgraduate taught admissions explains how the Admissions team considers applications and admits students. Read our postgraduate applicant guidance for more information on applying.
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View our generic registration terms and conditions (PDF) for the 2025/26 academic year, as a guide on what to expect.
Disclaimer
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Whilst we have done everything possible to ensure this information is accurate, some changes may happen between publishing and the start of the course.
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