Introduction
Are you excited by the rapid advancements in big data, predictive analytics, and AI technologies such as ChatGPT? Are you looking to study a subject that can unlock tremendous social and economic benefits and is highly sought after by employers?
The BSc Data Science programme equips you with the skills for an exciting career in data analytics, data engineering, machine learning or AI.
Study Information
At a Glance
- Learning Mode
- On Campus Learning
- Degree Qualification
- BSc
- Duration
- 48 months
- Study Mode
- Full Time
- Start Month
- September
- Location of Study
- Aberdeen
- UCAS Code
- G403
Data scientists use technical analysis, design, and programming skills to tackle a wide variety of challenges, ranging from mitigating climate change and detecting diseases to optimising production processes and enhancing customer experiences.
In recent years, data science has experienced a revolution with advancements in big data, predictive analytics and AI technologies such as ChatGPT and Bard AI. While these developments have improved how organisations automate processes, forecast trends, and engage with customers, the growing volume and complexity of data available presents new challenges to how we can leverage its true power.
To overcome this challenge, organisations look to data scientists who have the advanced data analysis and modelling skills to extract meaningful insights from data. The Data Science degree programme is designed to address this challenge. You will learn essential data analysis and modelling skills, including programming, statistics, software engineering, data curation, and more. The topics you study will also provide you with advanced knowledge and techniques applicable to artificial intelligence, machine learning, deep learning, distributed systems and cybersecurity.
Throughout the programme, you will have opportunities to further enhance your employability by engaging with the legal and ethical standards relating to data while also learning how to effectively communicate data-driven ideas and solutions.
What You'll Study
- Year 1
-
Compulsory Courses
- Getting Started at the University of Aberdeen (PD1002)
-
This course, which is prescribed for level 1 undergraduate students (and articulating students who are in their first year at the University), is studied entirely online, takes approximately 5-6 hours to complete and can be taken in one sitting, or spread across a number of weeks.
Topics include orientation overview, equality and diversity, health, safety and cyber security and how to make the most of your time at university in relation to careers and employability.
Successful completion of this course will be recorded on your Enhanced Transcript as ‘Achieved’.
- Programming 1 (CS1032)
-
15 Credit Points
This course will be delivered in two halves. The first half will provide a self-contained introduction to computer programming. It will be accessible to all undergraduates. Students will be exposed to the basic principles of computer programming, e.g. fundamental programming techniques, concepts, algorithms and data structures. The course contains lectures where the principles are systematically developed. As the course does not presuppose knowledge of these principles, we start from basic intuitions. The second half will be particularly of use to those studying Science and Engineering subjects, broadly interpreted, as well as Computing and IT specialists. It will include a gentle introduction to professional issues and security concepts.
- Calculus 1 (MA1005)
-
15 Credit Points
Calculus is the mathematical study of change, and is used in many areas of mathematics, science, and the commercial world. This course covers differentiation, limits, finding maximum and minimum values, and continuity. There may well be some overlap with school mathematics, but the course is brisk and will go a long way quickly.
- Algebra (MA1006)
-
15 Credit Points
This course introduces the concepts of complex numbers, matrices and other basic notions of linear algebra over the real and complex numbers. This provides the necessary mathematical background for further study in mathematics, physics, computing science, chemistry and engineering.
- Object - Oriented Programming (CS1527)
-
15 Credit Points
This course will build on the basic programming skills acquired in the first half-session and equip the students with advanced object oriented programming knowledge, implementation of data structure and algorithms, and basic software engineering techniques. The students will be challenged with more complicated programming problems through a series of continuous assessments.
- Calculus II (MA1508)
-
15 Credit Points
The aim of the course is to provide an introduction to Integral Calculus and the theory of sequences and series, to discuss their applications to the theory of functions, and to give an introduction to the theory of functions of several variables.
This provides the necessary mathematical background for further study in mathematics, physics, computing science, chemistry and engineering.
- Understanding Data (ST1506)
-
15 Credit Points
This is a statistics course open to all first and second year students. It is a useful course for students whose degree subject involves some amount of statistical analysis. The course teaches students how to summarise data effectively and how to correctly interpret it. Among the topics covered are sampling strategies, probability theory, confidence intervals and hypothesis tests. There are also weekly computer practicals using the statistics software RStudio. The mathematical context is emphasised but students are not expected to have a high level of maths.
Optional Courses
Plus 30 credit points from courses of your choice including:
- Combinatorics (MA1510)
-
15 Credit Points
Combinatorics is the branch of mathematics concerned with counting. This includes counting structures of a given kind (enumerative combinatorics), deciding when certain criteria can be met, finding "largest", "smallest", or "optimal" objects (external combinatorics and combinatorial optimization), and applying algebraic techniques to combinatorial problems (algebraic combinatorics). The course is recommended to students of mathematics and computing science.
- Mathematics and Computations Through Programming Matlab (MA1516)
-
15 Credit Points
Making calculations is at the heart of every science. In this course we will learn the programming language MATLAB and write programs to implement mathematical concepts that frequently appear in science and engineering. Through programming we will gain better understanding of some mathematical ideas prevalent in all sciences and how related calculations are done and the reason they work. MATLAB is particularly popular among engineers but it is very similar in its principles to other scientific programming languages, such as R, commonly used by statisticians, biologists and other scientists.
- Experience, Knowledge and Reality (PH1523)
-
15 Credit Points
How “real” is reality? How does the mind relate to the world? This course introduces two approaches to answering these questions: rationalism and empiricism. By reading Rene Descartes’ Meditations on First Philosophy, we learn about Descartes’ rationalist approach to knowledge, reality, mind-body dualism, and God’s necessary existence. Through David Hume’s Enquiry Concerning Human Understanding see how Hume grounds knowledge in experience. We read Hume on impressions and ideas, induction, causality, miracles and critically compare and examine Descartes’ and Hume’s arguments by drawing on readers and critics.
- Set Theory (MA1511)
-
15 Credit Points
Set theory was introduced by Cantor in 1872, who was attempting to understand the concept of "infinity" which defied the mathematical world since the Greeks. Set Theory is fundamental to modern mathematics - any mathematical theory must be formulated within the framework of set theory, or else it is deemed invalid. It is the alphabet of mathematics.
In this course we will study naive set theory. Fundamental object such as the natural numbers and the real numbers will be constructed. Structures such as partial orders and functions will be studied. And of course, we will explore infinite sets.
- Year 2
-
Compulsory Courses
- Linear Algebra (15 credit points) plus:
- Databases and Data Management (CS2019)
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15 Credit Points
Databases are an important part of traditional information systems (offline /online) as well as modern data science pipelines. This course will be of interest to anyone who wishes to learn to design and query databases using major database technologies. The course aims to teach the material using case studies from real-world applications, both in lectures and lab classes.
In addition, the course covers topics including management of different kinds of data such as spatial data and data warehousing. The course provides more hands-on training that develops skills useful in practice.
- Software Programming (CS2020)
-
15 Credit Points
This course is concerned with tools and techniques for scalable and dependable software programming. It focusses primarily on the Java programming language and related technologies. The course gives extensive programming practice in Java. It covers in depth features of the language and how best to use them, the execution model of the language, memory management, design principles underpinning the language, and comparisons with other languages. Tools for collaboration, productivity, and versioning will also be discussed.
- Probability (MA2010)
-
15 Credit Points
Probability theory is concerned with the analysis of random phenomena by providing an abstract mathematical framework to study them within the language of set theory. This is done by the concepts of "probability spaces" and "random variables". The theory began in the 16th century in attempts to analyze games of chance; In 1812 Pierre Simon Laplace wrote: "It is remarkable that a science which began with the consideration of games of chance should have become the most important object of human knowledge."
The course is recommended to anyone interested in the foundations and applications of mathematics.
- Algorithms and Data Structures (CS2522)
-
15 Credit Points
This course provides the knowledge needed to understand, design and compare algorithms. By the end of the course, a student should be able to create or adapt algorithms to solve problems, determine an algorithm's efficiency, and be able to implement it. The course also introduces the student to a variety of widely used algorithms and algorithm creation techniques, applicable to a range of domains. The course will introduce students to concepts such as pseudo-code and computational complexity, and make use of proof techniques. The practical component of the course will build on and enhance students' programming skills.
Optional Courses
Plus 45 credit points from courses of choice, including:
- Understanding the Physical World (PX1016)
-
15 Credit Points
Did you ever wonder about the efficiency of different forms of renewable energy, the mechanisms behind the formation of double rainbows or efficient ways of counting the number of termites in a nest? This non-calculus course provides an excellent opportunity to understand the basic principles of physics necessary to answer these and many other questions relevant to multiple disciplines, ranging from geology to engineering to biology and environmental sciences.
- Web Development (CS1534)
-
15 Credit Points
Students will learn to develop modern web applications using a variety of languages and frameworks as part of their degree, and prepare them for whatever they do after graduation. A key focus will be on the integration of HTML with CSS and Javascript with other backing frameworks to develop dynamic applications. The course is open to all undergraduates, and is accessible to those with no previous experience.
- Human - Computer Interaction (CS2506)
-
15 Credit Points
This course looks at why a computer system that interacts with human beings needs to be usable. It covers a set of techniques that allow usability to be taken into account when a system is designed and implemented, and also a set of techniques to assess whether usability has been achieved. Weekly practical sessions allow students to practice these techniques. The assessed coursework (which is normally carried out by groups of students) gives an opportunity to go through the design process for a concrete computer system, with a particular focus on ensuring usability.
- Ethics of Artificial Intelligence (PH251D)
-
15 Credit Points
Why are some researchers arguing that future robots should be considered as persons? What will increased AI mean for the future of work? What might increased AI do to global politics and democracy? Can we trust AI to make important healthcare decisions? What about bias in systems? And who or what is to be held responsible if things go wrong? This course explores some of the most pressing philosophical problems of the modern age. No computer science or philosophy background is required.
- Understanding Statistics (PO2508)
-
15 Credit Points
This course aims to provide students with an understanding of statistical concepts and methods relevant to accounting, management, finance, real estate and economics. The course is intended to enable students
i) To understand the principles of descriptive statistics, index construction, statistical inference, correlation, regression and time series analysis
ii) To apply statistical techniques to the analysis of accounting, business and economic issues and interpret findings
iii) To identify important sources of data in accounting, business and economics
- Year 3
-
Compulsory Courses
- Principles of Software Engineering (CS3028)
-
15 Credit Points
Students will develop large commercial and industrial software systems as a team-based effort that puts technical quality at centre stage. The module will focus on the early stage of software development, encompassing team building, requirements specification, architectural and detailed design, and software construction. Group work (where each team of students will develop a system selected using a business planning exercise) will guide the software engineering learning process. Teams will be encouraged to have an active, agile approach to problem solving through the guided study, evaluation and integration of practically relevant software engineering concepts, methods, and tools.
- Artificial Intelligence (CS3033)
-
15 Credit Points
The course provides an introduction to Artificial Intelligence (AI). It discusses fundamental problems of AI and their computational solution via key concepts.
- Software Engineering and Professional Practice (CS3528)
-
15 Credit Points
In this module, which is the follow-up of CS3028, students will focus on the team-based development of a previously specified, designed, and concept-proofed software system. Each team will build their product to industrial-strength quality standards following an agile process and applying the software engineering concepts, methods, and tools introduced in CS3028. The course includes a series of mandatory participatory seminars on professional and management issues in IT and IT projects. Students will be expected to relate their engineering work to these issues.
- Enterprise Computing and Business (CS3525)
-
15 Credit Points
This course provides insight into the business reasons for large software systems such as loyalty card systems, backend systems integrating firms and their suppliers and larges systems that integrate payroll, finance and operational parts of a business. You also learn the entrepreneurial aspects of business during the practical sessions where you explore and develop your own business application idea using service design and lean startup approaches centred around customer development, which you will find useful in any future work. This course is open to anyone across the university and requires no programming experience.
Optional Courses
- Plus 60 credit points from courses of choice, including:
- Differential Equations (MX3536)
-
15 Credit Points
Differential equations play a prominent role in many disciplines including engineering, physics, economics, and biology. In this course we will study the concept of a differential equation systematically from a purely mathematical viewpoint. Such abstraction is fundamental to the understanding of this concept.
- Operating Systems (CS3026)
-
15 Credit Points
This course discusses core concepts and architectures of operating systems, in particular the management of processes, memory and storage structures. Students will learn about the scheduling and operation of processes and threads, problems of concurrency and means to avoid race conditions and deadlock situations. The course will discuss virtual memory management, file systems and issues of security and recovery. In weekly practical session, students will gain a deeper understanding of operating system concepts with various programming exercises.
- Distributed Systems and Security (CS3524)
-
15 Credit Points
This course discusses core concepts of distributed systems, such as programming with distributed objects, multiple threads of control, multi-tire client-server systems, transactions and concurrency control, distributed transactions and commit protocols, and fault-tolerant systems. The course also discusses aspects of security, such as cryptography, authentication, digital signatures and certificates, SSL etc. Weekly practical sessions cover a set of techniques for the implementation of distributed system concepts such as programming with remote object invocation, thread management and socket communication.
- Optimisation Theory (MX4086)
-
15 Credit Points
Linear optimisation is a method to achieve the best outcome in a mathematical model whose requirements are represented by linear relationships. It is widely used in business and economics, and is also utilised for some engineering problems. Industries that use linear programming models include transportation, energy, telecommunications and manufacturing. It has proved useful in modeling diverse types routing, scheduling, assignment and design.
- Year 4
-
Compulsory Courses
- Single Honours Data Science Project (30 credit points), plus:
- Research Methods (CS4040)
-
15 Credit Points
In this course, you will conduct an individual research project into the behaviour of a computing system. You will develop knowledge and understanding of rigorous methods to: explore computing system behaviour; identify questions about behaviour; design experiments to answer those questions; analyse experimental results; and report on the outcomes of your research. You will develop your understanding of research ethics and how this relates to professional behaviour.
- Introduction to Machine Learning and Data Mining (CS4049)
-
15 Credit Points
This course provides an introduction to machine learning and data mining. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time-series analysis, neural networks, and many other techniques. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.
- Modelling Theory (MX4553)
-
15 Credit Points
This course was designed to show you what you can do with everything you learnt in your degree. We will use mathematical techniques to describe a fast variety of “real-world” systems: spreading of infectious diseases, onset of war, opinion formation, social systems, reliability of a space craft, patterns on the fur of animals (morphogenesis), formation of galaxies, traffic jams and others. This course will boost your employability and it will be exciting to see how everything you learnt comes together.
Optional Courses
45 credit points from courses of choice, including:
- Data Engineering (15 credit points)
- Natural Language Processing (15 credit points)
- Languages and Computability (15 credit points)
- Security (15 credit points)
- Operating Systems (15 credit points, if not taken during Year 3)
- Topological Data Analysis (15 credit points)
- Statistical Analysis of Biological Data (15 credit points)
- Financial Mathematics (15 credit points)
- Applied Marine Biology - Fisheries and Aquaculture (15 credit points)
- Environmental Analysis (15 credit points)
- Computer Vision (15 credit points)
- Reinforcement Learning (15 credit points)
- Bayesian Statistics (15 credit points)
- Geospacial Data Analysis (15 credit points)
- Marketing Analytics (15 credit points)
- Social Network Analysis (15 credit points)
We will endeavour to make all course options available; however, these may be subject to timetabling and other constraints. Please see our InfoHub pages for further information.
How You'll Study
Assessment Methods
Students are assessed by any combination of three assessment methods:
- coursework such as essays and reports completed throughout the course;
- practical assessments of the skills and competencies they learn on the course; and
- written examinations at the end of each course.
The exact mix of these methods differs between subject areas, years of study and individual courses.
Honours projects are typically assessed on the basis of a written dissertation.
Why Study Data Science?
- This programme is designed to take advantage of the growing demand for data scientists across almost every industry sector today including energy, finance, health, government, retail, mining and manufacturing.
- We go beyond classical software engineering, user experience, statistics and data analysis to teach students how to apply logical thinking, machine learning and computer algorithms to come up with solutions to problems faced across many academic disciplines and industry sectors.
- The Aberdeen University Artificial Intelligence Society and the Aberdeen University Computing Society provide a forum for students to share their interest and expertise in computing through workshops, guest talks, coding challenges and social events
- Computing at Aberdeen has strong links with industry organisations who support our teaching through case studies within lectures and seminars and prizes (including for example Amazon, CGI and ScotlandIS).
- You will learn both the theory and the practice of data science with special emphasis given to Artificial Intelligence Systems and Knowledge Technologies. We also focus on developing your teamwork, leadership and entrepreneurship skills in preparation for your career.
- The Aberdeen Software Factory is a student-run software house that enables computing science
students gain experience working on larger software projects for external clients. - 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.
General Entry Requirements
- 2024 Entry
-
SQA Highers
Standard: AABB*
Applicants who have achieved AABB (or better), are encouraged to apply and will be considered. Good performance in additional Highers/ Advanced Highers may be required.
Minimum: BBB*
Applicants who have achieved BBB (or are on course to achieve this by the end of S5) are encouraged to apply and will be considered. Good performance in additional Highers/Advanced Highers will normally be required.
Adjusted: BB*
Applicants who have achieved BB, and who meet one of the widening access criteria are are guaranteed a conditional offer. Good performance in additional Highers/Advanced Highers will be required.
* Including good performance in at least two Mathematics/ Science subjects by the end of your senior phase of education.
More information on our definition of Standard, Minimum and Adjusted entry qualifications.
A LEVELS
Standard: BBB*
Minimum: BBC*
Adjusted: CCC*
* Including good performance in at least two Mathematics/ Science subjects by the end of your senior phase of education.
More information on our definition of Standard, Minimum and Adjusted entry qualifications.
International Baccalaureate
32 points, including 5, 5, 5 at HL, with two Mathematics/ Science subjects at HL.
Irish Leaving Certificate
5H with 3 at H2 AND 2 at H3 including a minimum of H3 from two Science or Mathematics subjects.
Entry from College
Advanced entry to this degree may be possible from some HNC/HND qualifications, please see www.abdn.ac.uk/study/articulation for more details.
- 2025 Entry
-
SQA Highers
Standard: BBBB*
Applicants who have achieved BBBB (or better), are encouraged to apply and will be considered. Good performance in additional Highers/ Advanced Highers may be required.
Minimum: BBB*
Applicants who have achieved BBB (or are on course to achieve this by the end of S5) are encouraged to apply and will be considered. Good performance in additional Highers/Advanced Highers will normally be required.
Adjusted: BB*
Applicants who have achieved BB, and who meet one of the widening access criteria are are guaranteed a conditional offer. Good performance in additional Highers/Advanced Highers will be required.
Foundation Apprenticeship: One FA is equivalent to a Higher at A. It cannot replace any required subjects.
* Including good performance in at least two Mathematics/ Science subjects by the end of your senior phase of education.
More information on our definition of Standard, Minimum and Adjusted entry qualifications.
A LEVELS
Standard: BBC*
Minimum: BCC*
Adjusted: CCC*
* Including good performance in at least two Mathematics/ Science subjects by the end of your senior phase of education.
More information on our definition of Standard, Minimum and Adjusted entry qualifications.
International Baccalaureate
32 points, including 5, 5, 5 at HL, with two Mathematics/ Science subjects at HL.
Irish Leaving Certificate
5H with 3 at H2 AND 2 at H3 including a minimum of H3 from two Science or Mathematics subjects.
Entry from College
Advanced entry to this degree may be possible from some HNC/HND qualifications, please see www.abdn.ac.uk/study/articulation for more details.
The information displayed in this section shows a shortened summary of our entry requirements. For more information, or for full entry requirements for Sciences degrees, see our detailed entry requirements section.
English Language Requirements
To study for an Undergraduate 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 - 6.0 with: Listening - 5.5; Reading - 5.5; Speaking - 5.5; Writing - 6.0
TOEFL iBT:
OVERALL - 78 with: Listening - 17; Reading - 18; Speaking - 20; Writing - 21
PTE Academic:
OVERALL - 59 with: Listening - 59; Reading - 59; Speaking - 59; Writing - 59
Cambridge English B2 First, C1 Advanced or C2 Proficiency:
OVERALL - 169 with: Listening - 162; Reading - 162; Speaking - 162; Writing - 169
Read more about specific English Language requirements here.
International Applicants
Fees and Funding
You will be classified as one of the fee categories below.
| Fee category | Cost |
|---|---|
| EU / International students | £24,800 |
| Tuition Fees for 2024/25 Academic Year | |
| Home Students | £1,820 |
| Tuition Fees for 2024/25 Academic Year | |
| RUK | £9,250 |
| Tuition Fees for 2024/25 Academic Year |
Scholarships and Funding
Students from England, Wales and Northern Ireland, who pay tuition fees may be eligible for specific scholarships allowing them to receive additional funding. These are designed to provide assistance to help students support themselves during their time at Aberdeen.
International Applicants
Further Information about tuition fees and the cost of living in Aberdeen
Additional Fees
- 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 InfoHub Tuition Fees page.
Our Funding Database
View all funding options in our Funding Database.
Careers
According to the World Economic Forum's Future of Jobs Report 2023, the employment of data analysts and scientists, big data specialists, AI machine learning specialists and cybersecurity professionals is expected to grow on average by 30% by 2027.
According to Prospects.ac.uk, entry-level salaries for Data Scientists range from £19,000 to £25,000. With a few years' experience you could expect to earn £30,000 to £50,000, while experienced, high-level, data scientists or contractors can earn upwards of £60,000, in some cases reaching more than £100,000.
Graduates of this programme will be well placed to pursue careers such as:
- Business Intelligence Analyst
- Data Architect
- Data Mining Engineer
- Data Scientist
This LinkedIn Jobs on the Rise report listed Data Engineer as one of the fastest-growing jobs in the UK.
Our Experts
- Director
- Professor Marco Thiel
- Other Experts
- Dr Nigel Beacham
- Professor M Carmen Romano
- Professor Nir Oren
- Dr Raja Akram
Information About Staff Changes
You will be taught by a range of experts including professors, lecturers, teaching fellows and postgraduate tutors. Staff changes will occur from time to time; please see our InfoHub pages for further information.
Discover Uni
Discover Uni draws together comparable information in areas students have identified as important in making decisions about what and where to study. You can compare these and other data for different degree programmes in which you are interested.
Get in Touch
Contact Details
- Address
-
Student Recruitment & Admissions
University of Aberdeen
University Office
Regent Walk
Aberdeen
AB24 3FX