Introduction
This Master’s programme is designed to bridge the gap between data science and business management through an interdisciplinary approach that equips you with the critical business and data science skills necessary to establish or lead successful data science teams or enterprises.
Study Information
At a Glance
- Learning Mode
- On Campus Learning
- Degree Qualification
- MSc
- Duration
- 12 months or 24 months
- Study Mode
- Full Time or Part Time
- Start Month
- September
- Location of Study
- Aberdeen
Combining the study of Data Science and Business Management offers a strategic advantage in today's data-driven world. The world of data is undergoing a period of profound transformation as companies and organisations manage ever- increasing volumes and diversity of data. In response, the importance placed on data collection, management and analysis by organisations of all sizes is increasing all the time as businesses strive to stay competitive in today's market.
While the tools and techniques of advanced data science were previously largely confined to professionals with computing or other STEM backgrounds, the democratisation of data science means that business managers of all backgrounds can learn to unlock insights from data.
This Master’s programme is designed to make data science accessible to all by bridging the gap between data scientists and business managers through an interdisciplinary approach that fosters a holistic understanding of business dynamics and data analytics, enabling more accurate forecasting, targeted marketing, and improved operational efficiency, ultimately leading to smarter business strategies and better outcomes.
Our primary objective is to equip you with the critical business and data science skills necessary to establish or lead successful data science teams or enterprises. This includes a comprehensive understanding of both business management principles—such as leadership, digital marketing, HR, and market strategy planning—and the technical intricacies of data science, including coding, data collection and storage, data preparation and exploration, modelling, and predictive analytics.
What You'll Study
- Semester 1
-
Compulsory Courses
- Getting Started at the University of Aberdeen (PD5006)
-
This course, which is prescribed for all taught postgraduate students, is studied entirely online, is studied entirely online, takes approximately 2-3 hours to complete and can be taken in one sitting, or spread across the first 4 weeks of term.
Topics include University orientation overview, equality & diversity, MySkills, health, safety and cyber security, and academic integrity.
Successful completion of this course will be recorded on your Transcript as ‘Achieved’.
- The Leadership Challenge (BU501H)
-
15 Credit Points
This course provides an opportunity to explore and develop an understanding of your own leadership behaviour. Through workshops, group activities and discussions we investigate how personality, past experience, current situations and culture shape the way each of us behave in a leadership role. Using this information as a starting point we then explore how different leadership theories and approaches can be used as frameworks for developing a deeper understanding of leadership behaviour. You will also have an opportunity to try out a range of practical tools and techniques to assist you in the development of your own approach to leadership.
- Digital Marketing (BU506E)
-
15 Credit Points
The course provides both a strategic orientation and tactical orientation:
Strategic:
§ How to align digital strategy with a wider business strategy
§ The value that (internal and external) research and analytics can bring to digital marketing decisions
Tactical:
§ Assess the quality of any website based on a range of important measures
§ Benchmark a website’s performance against online competitors
§ Investigate the potential of a business idea for a given market sector
§ Present digital marketing research and advice to a generalist audience
§ Understand the potential commercial value of social media
§ Interpret onsite analytics in support of business objectives
- Introduction to Programming (PX5007)
-
15 Credit Points
This course teaches programming in high level languages and in particular the Wolfram Language (Mathematica). It will introduce all areas of this powerful language, including symbolic and numerical calculations and simulations, links to other high level languages such as R and Python, links to database languages mySQL and Mongo.
We will show how Wolfram Language allows computation to be applied to many areas of data analysis, and modelling. This allows us to gain deep insight into systems.
Optional Courses
ONE of:
- PX5023 Image Analysis (15 credit points)
- PX5019 Data Visualisation (15 credit points)
- Image Analysis (PX5023)
-
15 Credit Points
Nowadays a large volume of data is stored in form of images. This course introduces the tools needed to analyse images and extract information from them, including aspects of image enhancement, filtering, segmentation, morphological analysis and image classification based on convolutional neural networks.
- Data Visualisation (PX5019)
-
15 Credit Points
Visualising the outcome of a data analysis is critical to communicate the results. In this course we will study standard and cutting edge visualisation techniques to make sense of data, and present it in a compelling, narrative-focused story.
Presenting and visualising data and reporting on the result of an analysis are a crucial skill when making sense of data.
- Semester 2
-
Compulsory Courses
- Human Resource Essentials (BU551Q)
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15 Credit Points
Human Resource (HR) Essentials course serves as the first course in the program to provide students foundational understanding and knowledge of HR, and functions as a sound basis for other courses in the International Human Resources Management Programme. The course is focused on introducing HR theories and the application of theories and ideas into practice. In this course, students engage in a range of theories taught by lecturers as well as interactive exercises such case studies and evaluation of journal articles, through which they can reflect on and critically evaluate different HR theories. In this way students develop cognitive skills by actively and critically reflecting on practice, and practical transferable skills through assignment and in-class activities and exercises.
- Business Marketing Strategy Planning (BU552P)
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15 Credit Points
An effective marketing plan is critical to a company competing successful in its target markets. Hence, learning the skills and knowledge to develop an effective marketing plan is important for a career in marketing. The course shall explore critical issues in the marketing planning process (identifying target audience and then develop a market strategy to target the identified segment), as well as equip students with a step-by-step guide on how to develop a marketing plan.
- Machine Learning (PX5509)
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15 Credit Points
In this course we will discuss modern methods of machine learning, such as decision trees, regression, Markov models, Bayesian approaches, Nearest Neighbours, random forests, support vector machines and neural networks.
Great emphasis will be given to the actual application of all these methods to small and large data sets.
Optional Courses
ONE of:
- PX5508 Introduction to Data Science (15 credit points)
- PX5510 Statistics and Time Series Analysis (15 credit points)
- Introduction to Data Science (PX5508)
-
15 Credit Points
The goal of this course is to introduce the student into the field of data science. You will improve your data literacy, understanding the different types of existing data and data structures, and the kind of problems that can be solved using it. You will learn the tools and techniques necessary to obtain the data, store it and manipulate it. You will learn tools and techniques to preprocess it and prepare it for analysis, statistical characterization and visualization. Then, you will be introduced to simple modelling techniques aimed at providing answers for the problems you want to solve. The final lectures are dedicated to introduce the MySQL and Mongo relational and non-relational databases, respectively.
- Statistics and Time Series Analysis (PX5510)
-
15 Credit Points
This is an introductory course in statistics and statistical methods for data analysis.
We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.
This will allow us to perform typical analysis that underlie most modern data science questions.
- Semester 3
-
Compulsory Courses
- Data Science and Business Management Individual Project (60 credit points)
The taught component will be followed by a project, where joint supervision with scientists from both departments will be strongly recommended, in research topics involving both business and data science.
We will endeavour to make all course options available. However, these may be subject to change - see our Student Terms and Conditions page.
| Fee category | Cost |
|---|---|
| EU / International students | £26,250 |
| Tuition Fees for 2025/26 Academic Year | |
| UK | £12,200 |
| Tuition Fees for 2024/25 Academic Year |
Fee Information
Additional Fee Information
- In exceptional circumstances there may be additional fees associated with specialist courses, for example field trips. Any additional fees for a course can be found in our Catalogue of Courses.
- For more information about tuition fees for this programme, including payment plans and our refund policy, please visit our Tuition Fees page.
Scholarships
Self-funded international students enrolling on postgraduate taught (PGT) programmes will receive one of our Aberdeen Global Scholarships, ranging from £3,000 to £8,000, depending on your domicile country. Learn more about the Aberdeen Global Scholarships here.
To see our full range of scholarships, visit our Funding Database.
Why Study Data Science and Business Management?
- Combining Business Management and Data Science provides you with a competitive advantage in today's jobs market.
- The programme combines courses from both business management and data science domains. For students with an interest primarily in business, this programme helps form an appreciation for the scientific underpinnings of extracting actionable insights from data. Conversely, students with a background in STEM or related fields learn how to translate their scientific expertise into real-world business applications.
- The programme culminates in a research project where collaborative supervision from experts in both departments is strongly encouraged. You project allows you to delve deeper into topics that integrate business and data science topics, providing you with an opportunity to apply your knowledge in practical setting.
- Ranked 1st Scotland in Student Positivity for Business Studies and Management Studies in the National Student Survey 2024.
- Ranked 8th in the UK for Business and Management in the Guardian University Guide 2025.
- Ranked 15th in the UK for Business, Management and Marketing in the Times and Sunday Times Good University Guide 2025
- Join an EQUIS accredited Business School. Out of more than 15,000 business and management schools around the world, just over 200 across 45 countries have gained the international-recognised mark of distinction.
- The Business School hosts three Professional Development Weeks a year. We invite external speakers from industry to enhance employability. These speakers bring real-world insights, experiences, and expertise, providing practical knowledge, inspiration, networking opportunity, and a deeper understanding of industry trends.
- The University of Aberdeen has a strong history and worldwide reputation in computing science, in particular around Data Science, Natural Language Generation and Artificial Intelligence and is home to the research success of ARRIA NLG - the global leader in the field of natural language generation (NLG).
- The University of Aberdeen is a member of the Turing University Network, a network of UK universities engaged in cutting-edge teaching and research in data science and AI.
Entry Requirements
Qualifications
The information below is provided as a guide only and does not guarantee entry to the University of Aberdeen.
The information below is provided as a guide only and does not guarantee entry to the University of Aberdeen.
2:2 (lower second class) Honours degree or equivalent in any subject will be considered.
Please enter your country to view country-specific entry requirements.
English Language Requirements
To study for a Postgraduate Taught degree at the University of Aberdeen it is essential that you can speak, understand, read, and write English fluently. The minimum requirements for this degree are as follows:
IELTS Academic:
OVERALL - 6.5 with: Listening - 5.5; Reading - 5.5; Speaking - 5.5; Writing - 6.0
TOEFL iBT:
OVERALL - 90 with: Listening - 17; Reading - 18; Speaking - 20; Writing - 21
PTE Academic:
OVERALL - 62 with: Listening - 59; Reading - 59; Speaking - 59; Writing - 59
Cambridge English B2 First, C1 Advanced, C2 Proficiency:
OVERALL - 176 with: Listening - 162; Reading - 162; Speaking - 162; Writing - 169
Read more about specific English Language requirements here.
Document Requirements
You will be required to supply the following documentation with your application as proof you meet the entry requirements of this degree programme. If you have not yet completed your current programme of study, then you can still apply and you can provide your Degree Certificate at a later date.
- Degree Transcript
- a full transcript showing all the subjects you studied and the marks you have achieved in your degree(s) (original & official English translation)
- Personal Statement
- a detailed personal statement explaining your motivation for this particular programme
Aberdeen Global Scholarship
Eligible self-funded postgraduate taught (PGT) students will receive the Aberdeen Global Scholarship. Explore our Global Scholarships, including eligibility details, on our dedicated page.
Aberdeen Global ScholarshipsCareers
Combining Data Science and Business Management will enhance your career prospects in today's data-driven job market. The combination of business leadership skills and proficiency in data science you will gain on this programme will help lead to roles in business forecasting, targeted marketing, and using data to optimise processes.
This programme is designed to equip aspiring business managers with a unique blend of data modelling skills essential for deriving valuable insights from data as well as equipping students from a STEM background with the commercial knowledge to deliver real value for organisations.
As per data from Prospects.ac.uk, starting salaries for Data Scientists in the business management field typically range from £19,000 to £25,000. With a few years of experience, one can anticipate earning between £30,000 to £50,000. Moreover, seasoned professionals and contractors in this domain often command salaries exceeding £60,000, with some reaching over £100,000.
Upon completion of this program, graduates will be primed for a variety of rewarding career paths, including:
- Business Intelligence Analyst
- Data Architect
- Data Mining Engineer
- Data Scientist
According to a recent LinkedIn Jobs on the Rise report, Data Engineer emerges as one of the fastest-growing professions in the UK, further underlining the vast opportunities available in the field for aspiring business management enthusiasts.
Our Experts
- Other Expert
- Professor M Carmen Romano
- Programme Coordinator
- Professor Marco Thiel
Information About Staff Changes
You will be taught by a range of experts including professors, lecturers, teaching fellows and postgraduate tutors. However, these may be subject to change - see our Student Terms and Conditions page.
Get in Touch
Contact Details
- Address
-
Student Recruitment & Admissions
University of Aberdeen
University Office
Regent Walk
Aberdeen
AB24 3FX