MSc Advanced Data Science
ApplyKey facts
- Start date: September
- Study mode and duration: On campus, 12 months full-time
Study with us
Our MSc Advanced Data Science is designed for students with a degree that includes a significant mathematical component.
- gain advanced practical skills in data analysis and machine learning, processing of big data, predictive modelling and the use of statistical software packages R and Python
- understand the theory behind machine learning and other predictive algorithms
- become equipped with the necessary training to work as a statistician and data scientist in a broad range of fields such as health, insurance, finance and commerce
Why this course?
Our Advanced Data Science Masters is an advanced course that offers you the opportunity to develop theoretical and practical skills in statistics and data science.
You'll be supported by members of staff, who work directly with industry and the public sector, to develop analytical skills which are relevant to the varied industries which make use of data science.
You'll gain skills in:
- processing and analysis of complex data
- the use of statistical programming for data analysis, machine learning and prediction modelling
- understanding the key mathematical methods that underpin the analytical and predictive modelling techniques
- problem-solving
- effective communication
- conducting research in the data science field
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Our Unibuddy ambassadors can answer all your questions about courses and studying at Strathclyde and offer insight into their experiences of life in Glasgow and Scotland.
What you’ll study
There are a range of optional modules in addition to compulsory ones, so you can tailor the course to your career interests.
Semester 1
Modules in Semester 1 focus on the advanced core learning required for a career in data science. You’ll learn about the processing of big data, the mathematical theory behind prediction and classification methods, and the development of practical programming skills to run these models and analyses in R and Python.
Semester 2
Modules in Semester 2 build on the concepts from Semester 1. These will focus on developing a deep understanding of machine learning techniques and creating interactive dashboards to visualise data.
Deep Learning algorithms will be explored. Optional modules allow for learning in varied areas of statistical modelling and computer science applications such as risk analysis, networks, game theory and AI for Finance.
Semester 3
In Semester 3, you'll undertake a research project in which you'll work on a data science problem, putting the theoretical and practical skills you have learned into practice.
Department facilities
The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and University computing laboratories, with all necessary software available.
You'll also have access to a common room facility which gives you a modern and flexible area for individual and group study work and is also a relaxing social space.
Compulsory modules
Data Analytics in R
20 credits
This module will introduce the R computing environment and enable you to import data and perform statistical tests. The module will then focus on the understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures.
You can expect to cover concepts such as:
- use of functions and packages in R
- use of the tidyverse for data manipulation
- data visualisation using both base R and ggplot2
- multiple linear regression
- using variable selection techniques to cope with large data sets
- more general model comparison
Big Data Fundamentals
10 credits
This module will introduce the challenges of analysing big data with specific focus on the algorithms and techniques which are embodied in data analytics solutions.
At the end of the module, you'll understand:
- the fundamentals of Python for use in big data technologies
- how classical statistical techniques are applied in modern data analysis
- the limitations of various data analysis tools in a variety of contexts
Big Data Tools & Techniques
10 credits
This module will enhance your understanding of the challenges posed by the advent of Big Data and will introduce you to scalable solutions for data storage and usage.
You can expect to learn about:
- the design and implementation of cloud NoSQL systems
- addressing design trade-offs and their impact
- the Map-Reduce programming paradigm
Multivariate Analysis
10 credits
This module aims to provide you with a range of applied statistical techniques that can be used in professional life to analyse multivariate data. Both statistical and machine learning approaches are included.
You'll cover topics such as:
- graphical methods for investigating multivariate data
- logistic regression and discrimination
- linear and quadratic discriminant analysis
- non-parametric classification
- hierarchical and non-hierarchical clustering
- principal component analysis
Compulsory modules
Mathematics of Machine Learning
20 credits
This module will enable you to develop a more fundamental understanding of the mathematics of machine learning and of the ideas underpinning some classical algorithms in the field.
On completion of this module you'll be able to:
- critically interpret new algorithms in machine learning
- understand convergence and properties of the computed solution
- work on real-world problems using machine learning techniques
Compulsory modules
Data dashboards with RShiny
10 credits
This module will develop your skills in data presentation and statistical communication. You will learn to develop data dashboards, which are increasingly used to allow key stakeholders (and the public) to gain key insights into data via interactive visualisation.
Topics covered will include:
- Creating a data dashboard in RStudio
- User interface design with respect to accessibility
- Creating interactive data visualisations which reflect a specific aim
- Reactive programming in RStudio
- Static programming in R
Statistical Machine Learning
10 credits
This module provides you with the basic theories of machine learning and how to construct a machine model for a real dataset using R. You will also understand the ethical issues regarding data processing and management.
On completion of this module, you will be able to:
- clean data using RStudio and the tidyverse
- understand missing data and the role it plays
- understand ethical issues regarding data processing and management
- carry out single-value imputation
- carry out multiple imputed chained equations in R
- understand and implement artificial neural networks
- understand and implement support vector machines
- understand and implement tree-based classification and regression techniques
- understand and implement ensemble methods
Deep Learning
10 credits
This module introduces deep Learning prediction algorithms covering background theory and practical application in Python.
Topics covered include:
- activation functions
- gradient descent, backpropagation and optimisation
- convolutional Neural Networks
- recurrent Neural Networks
- generative Adversarial Networks
Optional modules
Choose 20 credits from the list below:
Mathematical Introduction to Networks
20 credits
This module demonstrates the central role network theory plays in mathematical modelling.
Topics include:
- the connection between linear algebra and graph theory
- to use of theory as a tool for revealing structure in networks
- application of algorithms on a network using programming
Game Theory & Multiagent Systems
10 credits
This module will enable you to become familiar with the mathematical theory of games and the ways in which it is applied to the study of multiagent systems and machine learning.
Topics covered include:
- utility functions, decisions under uncertainty
- normal form games, pure and mixed strategies and Nash equilibria, minmax, Pareto optimality, correlated equilibria
- extensive form games, subgame perfect equilibria, backward induction, information sets
- infinitely repeated games, the Folk theorem. Iterated prisoner’s dilemma
- Bayesian games, Bayesian Nash equilibria
- Markov games and reinforcement learning
- real-world gameplay. Alpha-beta pruning. Tic-tac-toe, Chess, Go, StarCraft
- economic games and pricing algorithms
AI for Finance
20 credits
This module provides an overview of the application of AI techniques - including those which mimic natural evolutionary processes (genetic algorithms and genetic programming in particular) - to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.
After completing this module, you'll be able to:
- understand the benefits and opportunities for evolutionary computing in the context of financial applications
- understand the principles of evolutionary computation, in particular, genetic programming and genetic algorithms, as well as neural networks (particularly those configurations most suited to time series data)
- understand how the computational approaches covered in the module may be applied to financial problem-solving and understand their limitations
- develop and evaluate practical solutions to finance-based problems
Medical Statistics
20 credits
This module will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for healthcare research. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation.
Topics covered include:
- survival analysis
- analysing categorical data using hypothesis tests
- experimental Design and sampling
- clinical measurement
Effective Statistical Consultancy
10 credits
This module covers all aspects of statistical consultancy skills necessary for being a successful statistician working in any research or customer environment. You will work on real-life problems in small groups and have the opportunity to interact with stakeholders researchers to formulate hypotheses.
This module will cover how to:
- engage with professionals working in business, industry and the public sector
- apply their statistical knowledge in different situations
- effectively communicate statistical results to non-statisticians
Survey Design & Analysis
10 credits
Surveys are an important way of collecting data. This module will introduce you to the methods commonly used to design questionnaires and analyse data resulting from these questionnaires.
You'll consider:
- how to design appropriate survey questions
- a variety of sampling methods
- analysing data for different sampling methods
Bayesian Spatial Statistics
10 credits
This module will introduce you to Bayesian statistics and the modern Bayesian methods that are used in a variety of applications. Like with other modules, the focus is on real-life data and using statistical software packages for analysis.
You will gain experience in working with the following:
- visualising spatial data
- geospatial data, including methods for prediction
- bayesian modelling using software to implement Markov Chain Monte Carlo
- areal unit modelling
Quantitative Risk Analysis
10 credits
This module will cover the theory of assessing risks under uncertainty. It will focus on the practical assessment of risk using simulation methods such as Monte Carlo simulation. You'll develop skills in communicating risk to risk managers as well as formulating practical risk questions that can influence policy decisions.
You can expect to learn about:
- uncertainty and variability
- bootstrapping
- Monte Carlo Simulation
- selecting appropriate probability distributions based on given scenarios
Database Fundamentals
10 credits
This module will help you develop skills in creating and managing database systems, including:
- developing initial database specifications
- formulating database queries using SQL
- understanding the facilities and services which should be provided by a fully featured database management system
- experiencing using a relational database management system in a client-server environment
- understanding future trends in database systems
Compulsory module
Research project
60 credits
You'll undertake a research project to work on a data science problem, putting the theoretical skills you have learned into practice.
Learning & teaching
Modules are delivered by several teaching methods:
- lectures (using a variety of media including presentations and computer demonstrations)
- tutorials
- computer laboratories
- coursework
- projects
Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.
Assessment
The form of assessment varies across modules. For most modules, the assessment involves both coursework and practical computer-based or written examinations.
The assessment will require you to demonstrate your statistical and data science knowledge by analysing, creating predictive models, and visualising data to interpret the results in the context of the research question.
Projects will involve writing code, interpreting outputs, and producing a report, interactive visualisation or presentation outlining the findings from your analysis.
Group work may be undertaken in some modules.
Entry requirements
| Academic requirements/experience | Minimum second-class (2:2) Honours degree or overseas equivalent* in a mathematical discipline. Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply. *For Australia and Canada, normal degrees are accepted. |
|---|---|
| English language requirements | You must have an English language minimum score of IELTS 6.0 (with no component below 5.5). We offer comprehensive English language courses for students whose IELTS scores are below 6.0. In addition to IELTS, the University now accepts a wider range of English language tests for overseas applicants, such as TOEFL and PTE Cambridge. View the full list of accepted English language tests. |
Pre-Masters preparation course
The Pre-Masters Programme is a preparation course held at the University of Strathclyde International Study Centre, for international students (non-UK/Ireland) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde.
Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.
Fees & funding
Fees may be subject to updates to maintain accuracy. Tuition fees will be notified in your offer letter.
All fees are in £ sterling, unless otherwise stated, and may be subject to revision.
Annual revision of fees
Students on programmes of study of more than one year (or studying standalone modules) should be aware that the majority of fees will increase annually. The University will take a range of factors into account, including, but not limited to, UK inflation, changes in delivery costs and changes in Scottish and/or UK Government funding. Changes in fees will be published on the University website in October each year for the following year of study and any annual increase will be capped at a maximum of 10% per year.
| Scotland | £11,900 |
|---|---|
| England, Wales & Northern Ireland | £11,900 |
| Republic of Ireland |
If you are an Irish citizen and have been ordinary resident in the Republic of Ireland for the three years prior to the relevant date, and will be coming to Scotland for Educational purposes only, you will meet the criteria of England, Wales & Northern Ireland fee status. For more information and advice on tuition fee status, you can visit the UKCISA - International student advice and guidance - Scotland: fee status webpage. Find out more about the University of Strathclyde's fee assessments process. |
| International | £25,500 |
| Additional costs | If you are an international student, you may have associated visa and immigration costs. Please see student visa guidance for more information. |
| Available scholarships | Take a look at our scholarships search for funding opportunities. |
Please note: the fees shown are annual and may be subject to an increase each year. Find out more about fees.
How can I fund my course?
Scottish postgraduate students
Scottish postgraduate students may be able to apply for support from the Student Awards Agency Scotland (SAAS). The support is in the form of a tuition fee loan and for eligible students, a living cost loan. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from England
Students ordinarily resident in England may be to apply for postgraduate support from Student Finance England. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from Northern Ireland
Postgraduate students who are ordinarily resident in Northern Ireland may be able to apply for support from Student Finance Northern Ireland. The support is a tuition fee loan of up to £5,500. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from Wales
Students ordinarily resident in Wales may be to apply for postgraduate support from Student Finance Wales. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
International students
We've a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.
International students
We've a thriving international community with students coming here to study from over 140 countries across the world. Find out all you need to know about studying in Glasgow at Strathclyde and hear from students about their experiences.
Careers
Our MSc in Advanced Data Science will provide graduates with skills in the statistical analysis of big data. Many employers require these skills in sectors such as:
- investment companies
- financial institutions
- pharmaceutical industry
- medical research
- government organisations
- retailers
- internet information providers
Graduate roles
Typical job roles include:
- statistician
- data analyst
- software developer or engineer
- statistical programmer
- data scientist
Apply
Start date: Sep 2025
Advanced Data Science
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