- Business Analytics
MSc — 2025 entry Business Analytics
In our hyper-connected, data-driven era, the true visionaries who harness the power of big data are the ones who thrive. Imagine being at the forefront of this revolution, armed with cutting-edge tools like artificial intelligence, machine learning, and management science. With our game-changing MSc Business Analytics course, you'll uncover the true potential of data, transforming it into extraordinary insights and propelling businesses to unprecedented success.
Why choose
this course?
- As a business analytics student, you’ll be based in the Surrey Business School and be part of a vibrant community, focused on improving business practice and creating a sustainable and positive change.
- Prepare to embark on a learning experience that will elevate your analytical prowess and revolutionise the way you approach business challenges. Our highly practical Business Analytics masters is designed to mould you into a confident lateral thinker, equipped with the latest theories and hands-on practices that will set you apart from the competition.
- We understand the importance of blending theory and real-world experience, which is why our masters in Business Analytics is enriched by the wisdom and guidance of inspiring industry experts, who will share their invaluable insights both in the classroom and on-site.
- The 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. Surrey Business School also has a strong track record of entrepreneurship and enterprise.
- The School is renowned for its:
- World-class teaching and research
- Strong focus on entrepreneurship and enterprise
- Vibrant and diverse community
- Excellent career prospects.
Statistics
Fantastic graduate prospects
94% of our Surrey Business School postgraduate students go on to employment or further study (Graduate Outcomes 2024, HESA)
Top 75 in the world
We’re ranked in the top 75 for Business Administration and top 100 for Management in the Shanghai Global Subject Rankings 2023
2nd for student satisfaction
Surrey Business School is ranked 2nd in the UK for Business and Management in the 2023 Postgraduate Taught Experience Survey (PTES) 2023, with an overall satisfaction score of 95% (CAH Group 1)
Accreditation
What you will study
This Business Analytics course 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.
View our blog dedicated to our Business Analytics MSc.
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.
Module accreditation
Our 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 who have successfully completed the module. It is widely recognised by employers.
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 courses 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. Read the full story of the module accreditation here (https://www.surrey.ac.uk/news/surrey-business-school-and-sas-jointly-an…)
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)
Our course runs over three semesters. You'll take four modules in both Semester 1 (October to January) and Semester 2 (February to June). Semester 3 runs from June to September. You’ll carry out your independent project (dissertation) from Semester 2 till Semester 3.
There are five compulsory modules, and seven optional modules for you to choose from.
Students who start in February will take four modules in Semester 1, two compulsory modules in Semester 2 and two optional modules in the Semester 3. For February students, Semester 2 starts in June/July of the first year, and Semester 3 starts in October of the following academic year. You’ll also carry out your independent project (dissertation) during this time.
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 programmes based on a 15-credit tariff, meaning all modules are comprised of multiples of 15 credits, up to a maximum of 120 credits.
Course options
Year 1
Semester 1
Compulsory
Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.
View full module detailsThe primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.
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 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 detailsMachine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.
View full module detailsThe primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.
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 detailsData mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.
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 is designed to introduce to students the concepts of data and business process management and explores the opportunities, benefits and challenges business process modelling and Enterprise Resource Planning (ERP) create for the business organisation and its management. The module provides two sets of objectives: One set of objectives achieved by the class lectures highlights the data and business process management and its implications for the organisation and its managers. The module also exposes students to modelling business processes using the SAP platform. This objective accomplished in the PC lab will allow students to gain an understanding and hands-on experience of the operation and impact of processes operationalised by information technology. Each of the topics covered by the course will be critically evaluated to point out the advantages and disadvantages of information technology-driven processes.
View full module detailsThis module is designed to introduce to students how mathematical and econometric methods can be used to model diverse transformation processes, to establish benchmarks of efficiency and productivity for organisations, and how to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience of 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 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.
Year 1
Semester 1
Compulsory
Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.
View full module detailsThe primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.
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 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 detailsMachine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.
View full module detailsThe primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.
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 detailsData mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.
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 is designed to introduce to students the concepts of data and business process management and explores the opportunities, benefits and challenges business process modelling and Enterprise Resource Planning (ERP) create for the business organisation and its management. The module provides two sets of objectives: One set of objectives achieved by the class lectures highlights the data and business process management and its implications for the organisation and its managers. The module also exposes students to modelling business processes using the SAP platform. This objective accomplished in the PC lab will allow students to gain an understanding and hands-on experience of the operation and impact of processes operationalised by information technology. Each of the topics covered by the course will be critically evaluated to point out the advantages and disadvantages of information technology-driven processes.
View full module detailsThis module is designed to introduce to students how mathematical and econometric methods can be used to model diverse transformation processes, to establish benchmarks of efficiency and productivity for organisations, and how to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience of 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 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.
Year 1
Semester 1
Compulsory
Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.
View full module detailsThe primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.
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 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 detailsThe 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 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 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 detailsMachine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.
View full module detailsOptional
This module is designed to introduce to students the concepts of data and business process management and explores the opportunities, benefits and challenges business process modelling and Enterprise Resource Planning (ERP) create for the business organisation and its management. The module provides two sets of objectives: One set of objectives achieved by the class lectures highlights the data and business process management and its implications for the organisation and its managers. The module also exposes students to modelling business processes using the SAP platform. This objective accomplished in the PC lab will allow students to gain an understanding and hands-on experience of the operation and impact of processes operationalised by information technology. Each of the topics covered by the course will be critically evaluated to point out the advantages and disadvantages of information technology-driven processes.
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 detailsThe 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 is designed to introduce to students how mathematical and econometric methods can be used to model diverse transformation processes, to establish benchmarks of efficiency and productivity for organisations, and how to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience of implementing an efficiency and productivity assessment with real case studies using appropriate software.
View full module detailsAcross academic years
Compulsory
The module is available as an alternative to the Placement module offered on the MSc programme that a student is enrolled in. The Study Abroad module supports students’ academic, personal and professional development as global citizens preparing for a borderless career that requires cross-cultural awareness, ability to collaborate with international partners and navigate different business systems and complex international work arrangements. The Study Abroad module is concerned with holistic academic and non-academic learning. Additionally, the module aims to enable students to evidence and evaluate their study abroad experiences and transfer that learning to other situations.
View full module detailsOptional modules for Year 1 (full-time with study abroad - 15 months) - FHEQ Level 7
For further information regarding programme structure and module selection, please refer to the course catalogue.
Year 1
Semester 1
Compulsory
Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It involves searching through databases for potentially useful information such as knowledge rules, patterns, regularities, and other trends hidden in the data. Using a broad range of techniques, this information is leveraged to increase revenues, cut costs, improve customer relationships, reduce risks and more. Applications of data mining and business analytics are highly useful in today's competitive market. In this module several case studies of well- known data mining techniques are used; e.g. shopping basket analysis such as Tesco club card, credit card fraud detection, predicting stock market returns, risk analysis in banking, web analytics and social network analysis including Facebook and Twitter.
View full module detailsThe primary purpose of this module is to teach students how to structure a business data analysis from end to end, from business question to communication of options and insights. Such a skill is fundamental to all Business Analytics, and will help students structure their analyses throughout the rest of the programme. Students will learn how to apply data analysis within a general decision-making framework by practical first-hand experience, taking a business problem (and associated dataset) from start to finish, with each week teaching them how to progress through one step of the analytical process. Along the way they will learn key concepts that determine the quality of a data analysis, including how to generate a specific business question, how to generate reliable, clean data, how to differentiate signal from noise, such that they may identify useful business insights. At the end of the module, they will take their analyses and learn how to communicate data insights through visualisation and dashboarding.
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 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 detailsOptional
This module is designed to introduce to students the concepts of data and business process management and explores the opportunities, benefits and challenges business process modelling and Enterprise Resource Planning (ERP) create for the business organisation and its management. The module provides two sets of objectives: One set of objectives achieved by the class lectures highlights the data and business process management and its implications for the organisation and its managers. The module also exposes students to modelling business processes using the SAP platform. This objective accomplished in the PC lab will allow students to gain an understanding and hands-on experience of the operation and impact of processes operationalised by information technology. Each of the topics covered by the course will be critically evaluated to point out the advantages and disadvantages of information technology-driven processes.
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 detailsOptional modules for Year 1 (part-time) - FHEQ Level 7
For further information regarding programme structure and module selection, please refer to the course catalogue.
Year 2
Semester 1
Optional
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
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 detailsMachine Learning/AI & Visualisations are used to gain business insights for decision making. Data will be sliced, diced and visually analysed. Artificial Intelligence and statistical learning will be introduced. Techniques will be used for prediction, estimation or classification.
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 is designed to introduce to students how mathematical and econometric methods can be used to model diverse transformation processes, to establish benchmarks of efficiency and productivity for organisations, and how to carry out a benchmarking exercise using such methods. The students will gain valuable hands-on experience of implementing an efficiency and productivity assessment with real case studies using appropriate software.
View full module detailsOptional modules for Year 2 (part-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
Course timetables are normally available one month before the start of the semester.
New students will receive their personalised timetable in Welcome Week, and in subsequent semesters, two weeks prior to the start of semester.
Please note that while we make every effort to ensure that timetables are as student-friendly as possible, scheduled teaching can take place on any day of the week (Monday – Friday). Wednesday afternoons are normally reserved for sports and cultural activities. Part-time classes are normally scheduled on one or two days per week, details of which can be obtained from Academic Administration.
Location
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.
By studying this course you will gain unparalleled expertise that will make you an indispensable asset in any organisation.
94 per cent of our Surrey Business School postgraduate students go on to employment or further study (Graduate Outcomes 2024, HESA).
Business analytics students often pursue careers such as:
- Data scientists
- Data analyst
- Finance analysts
- Operations managers
- Consultants (Power BI/Tableau, Business Strategy, Analytics)
- Business analytics managers
- Business intelligence analysts.
Stefan Dimitrov Stoyanov
Student - Business Analytics MSc
"I was impressed by the fact that the university is well established in the world for its close research cooperation with business. The university's ambition to create a tech hub in Guilford inspired me too."
Christiana Demetriou
Student - Business Analytics MSc
"The University of Surrey proved to be a great place to live, experience new things and develop myself further - both personally and professionally."
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
Course length | IELTS Academic requirements or equivalent |
---|---|
Full-time: 1 year | 6.5 overall including 6.0 in each category |
Full-time (with study abroad): 15 months | 7.0 overall including 6.5 in each category |
Part-time: 2 years | 6.5 overall including 6.0 in each category |
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.
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 for some courses and fees may be payable for certain claims. Please contact the Admissions team with any queries.
Placements
As part of this course you have the option to complete a placement which would require attendance off campus, depending on where you secure your placement.
Study and work abroad
Our 15-month MSc Business Analytics course enables you to spend time studying abroad.
If you choose and secure a place at a partner university, you’ll complete two semesters (nine months) of study at Surrey and spend a semester studying abroad. This means you'll complete your course in 15 months studying full-time. You'll not be writing a dissertation.
If you decide not to study abroad or don’t secure a place, you’ll complete nine months of teaching at Surrey followed by three months working on your dissertation. This means you'll complete your course in 12 months studying full-time.
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.
February 2025 - Full-time - 1 year
- UK
- £15,200
- Overseas
- £25,400
September 2025 - Full-time - 1 year
- UK
- £15,200
- Overseas
- £24,900
September 2025 - Full-time (with study abroad) - 15 months
- UK
- To be confirmed
- Overseas
- To be confirmed
September 2025 - Part-time - 2 years
- UK
- £7,600
- Overseas
- £12,500
- If you are on the two-year part-time masters programme, the annual fee is payable in Year 1 and Year 2 of the programme
- These fees apply to students commencing study in the academic year 2025-26 only. Fees for new starters are reviewed annually.
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
Commuting (local travel expenses): 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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Please note that we may have to close applications before the stated deadline if we receive a high volume of suitable applications. We advise you to submit your application as soon as it is ready.
ApplyPlease note that we may have to close applications before the stated deadline if we receive a high volume of suitable applications. We advise you to submit your application as soon as it is ready.
ApplyPlease note that we may have to close applications before the stated deadline if we receive a high volume of suitable applications. We advise you to submit your application as soon as it is ready.
ApplyPlease note that we may have to close applications before the stated deadline if we receive a high volume of suitable applications. We advise you to submit your application as soon as it is ready.
ApplyAdmissions information
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 admissions policy explains how the Admissions team considers applications and admits students. Read our postgraduate applicant guidance for more information on applying.
About the University of Surrey
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Contact our Admissions team or talk to a current University of Surrey student online.
Terms and conditions
When you accept an offer to study at the University of Surrey, you are agreeing to follow our policies and procedures, student regulations, and terms and conditions.
We provide these terms and conditions in two stages:
- First when we make an offer.
- Second when students accept their offer and register to study with us (registration terms and conditions will vary depending on your course and academic year).
View our generic registration terms and conditions (PDF) for the 2023/24 academic year, as a guide on what to expect.
Disclaimer
This online prospectus has been published in advance of the academic year to which it applies.
Whilst we have done everything possible to ensure this information is accurate, some changes may happen between publishing and the start of the course.
It is important to check this website for any updates before you apply for a course with us. Read our full disclaimer.