There is an increasing demand for data scientists across all industry sectors, from healthcare and tech to finance, retail and beyond. This programme enables graduates of various disciplines to take advantage of the growing career opportunities in this field, by teaching the essential Computational Thinking (CT) and data analysis skills needed by employers today.
This programme is studied on campus.
Over the last 5 years, there has been an enormous increase in demand for Data Scientists across almost every industry sector, from tech and finance to energy, retail, healthcare and cybersecurity.
As the volume, diversity and complexity of data being gathered continue to increase, the key challenge facing organisations today is how to make sense of data, and more importantly how to use data to inform business decisions.
To solve this problem, companies need data scientists who not only are highly skilled in a wide range of statistical and data analysis tools, but who can go far beyond classical statistics and machine learning to gain real insights from data. The MSc Data Science programme is specifically designed to address this challenge.
Unlike many other data science programmes, this MSc goes beyond statistics and big data, to use a Computational Thinking (CT) approach to data science, one that applies logical thinking, sequencing and algorithms to create solutions to problems across many disciplines and industries.
The multidisciplinary focus of the programme also means that it is open to qualified applicants from across a wide range of academic backgrounds, including science, technology, engineering and medicine (STEM), but also business and social sciences.
This programme has been designed in collaboration with industry partners, including Wolfram Research, to ensure that the algorithms, tools and workflows required by industry are covered. You will also gain proficiency in the use of Wolfram Mathematica programme.
Key Programme Information
At a Glance
- Learning Mode
- On Campus Learning
- Degree Qualification
- 12 months or 24 months
- Study Mode
- Full Time or Part Time
- Start Month
- Location of Study
What You'll Study
- Semester 1
- 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.
- Introduction to Data Science (PX5008) - 15 Credit Points
In this course we study the typical workflow for a data analysis project. We will learn how to access and collect data, how then to clean the data, and organise it in databases to prepare it for later analysis.
We will then perform descriptive and exploratory data analysis and finally visualise the results and create a report.
- Statistics and Time Series Analysis (PX5010) - 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.
- Machine Learning (PX5009) - 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.
- Semester 2
- Data Visualisation (PX5503) - 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.
- Advanced Statistics and Special Applications (PX5504) - 15 Credit Points
In this module we will discuss advanced and cutting-edge statistical tools and techniques.
Some of the topics covered are likelihood, advanced hypothesis testing, outlier detection, data imputation, bootstrap, nonparametric regression and mixed effect models.
- Audio, Image and Video Analysis (PX5505) - 15 Credit Points
This course introduces the tools needed to analyse audio recordings, images and videos.
It will contain aspects of image enhancements, content detection, segmentation analysis (e.g. detecting tumours in medical imaging data), handwritten character recognition, subtitle analysis, and many other techniques.
- Case Studies in Data Science (PX5506) - 15 Credit Points
This course brings together all aspects of data science, from gathering data, to analysis and visualisation, by exploring real world applications of data science. There will be a discussion of some significant achievements of data science when applied to various areas from fundamental business practices to physics. Students will then apply these skills to a group project.
- Semester 3
- Data Science Project (PX5901) - 60 Credit Points
This is a project course for the MSc in data science. Students will be given a data science project, which will be supervised by two members of staff.
The project will involve a typical data science workflow, from data collection, cleaning, to analysing and visualising the results.
The students will have to deliver a presentation and hand in a report about the results.
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
- Individual Projects
Why Study Data Science?
- This programme equips you with the essential data analysis and computational thinking skills needed to extract knowledge and insights from data.
- There has been a huge increase in demand for data specialists across almost every industry, from energy and manufacturing to healthcare and cybercrime.
- Aimed at undergraduate students from a wide range of academic backgrounds, including science, technology, engineering and medicine (STEM) but also business, arts and the humanities.
- Designed in collaboration with industry partners, including Wolfram Research, which means the the algorithms, tools and workflows required by industry are extensively covered.
- The overall objective of this programme is to create experts who can combine their mathematical modelling and programming skills with an ability to work effectively in multidisciplinary teams to extract knowledge and insights from data.
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.
‘Academic Technology Approval Scheme (ATAS) certificate
Please note that international applicants for this programme require an Academic Technology Approval Scheme (ATAS) certificate. The ATAS certificate must be obtained before applying for a Tier 4 visa. You can submit an ATAS application up to 6 months before the programme start date, even if the offer is still conditional.’
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:
OVERALL - 6.5 with: Listening - 5.5; Reading - 5.5; Speaking - 5.5; Writing - 6.0
OVERALL - 90 with: Listening - 17; Reading - 18; Speaking - 20; Writing - 21
OVERALL - 62 with: Listening - 51; Reading - 51; Speaking - 51; Writing - 54
Cambridge English Advanced & Proficiency:
OVERALL - 176 with: Listening - 162; Reading - 162; Speaking - 162; Writing - 169
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.
- an up-to-date CV/Resumé
- Degree Certificate
- a degree certificate showing your qualifications
- 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
Fees and Funding
You will be classified as one of the fee categories below.
|Home / EU / RUK Students||£10,000|
|Tuition Fees for 2020/21 Academic Year|
|Tuition Fees for 2020/21 Academic Year|
International non-EU Applicants
- 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.
The overall objective of this programme is to create experts who can combine their mathematical modelling and programming skills with an ability to work effectively in multidisciplinary teams to extract knowledge and insights from data.
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
- Programme Leader
- Marco Thiel , The University of Aberdeen
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.
Get in Touch
Student Recruitment & Admissions Service
University of Aberdeen