MSc Data Analytics

Key facts

  • Start date: September
  • Accreditation: AACSB, EQUIS & AMBA
  • Application deadline: applications are accepted throughout the year
  • Study mode and duration: MSc: 12 months full-time, 24 months part-time

Study with us

On our MSc Data Analytics programme you'll:

  • gain a comprehensive skill set and expertise through input from three contributing departments
  • use data analytics techniques within business contexts to become rounded problem-solvers
  • choose from a range of optional classes for specialisation

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Why this course?

The MSc Data Analytics is designed to create rounded data analytics problem-solvers.

The course focuses on the uses of data analytics techniques within business contexts, making informed decisions about appropriate technology to extract knowledge from data and understanding the theoretical principles by which such technology operates.

You'll gain a comprehensive skill set that will enable you to work in a variety of sectors using a blended learning approach that combines theory, intensive practice and industrial engagement.

The degree is unique by bringing together essential skills from three departments across the University in order to address the needs of a fast-growing industry. It's jointly delivered by:

This unique collaboration avoids the narrow interpretation of the subject offered by similar degrees and presents significant opportunities for businesses to recruit data analytics experts with a high-level expertise and knowledge.

Every year, guest speakers attend our course, sharing their invaluable experiences. As part of the Data Analytics in Practice class, we host representatives from external originations, who present case studies and challenging projects to our students.

THE Awards 2019: UK University of the Year Winner

What you’ll study

The core Data Analytics in Practice class runs over both semesters and provides you with a practical environment to apply methodological learnings from other classes into challenging projects from industry.

Semester 1

Semester 1 is designed to provide you with the fundamental technical analytics knowledge from all three departments. Computer & Information Sciences courses will cover core techniques including machine learning and data mining as well as data visualisation and big data platforms

Mathematics courses will ensure you gain strong computational skills while establishing a broad knowledge of statistical tools essential for analytics. Management Science courses will build the foundations of business skills including problem structuring as well as decision analysis, in addition to providing essential practical skills.

Semester 2

Semester 2 is designed to extend your core skills and provide you with opportunities through a broad range of electives to specialise in areas that you are particularly interested to excel. To ensure breadth of knowledge, you'll be required to choose electives from at least two departments.

Summer project

The final component of the MSc course will be a summer dissertation project. You will have optional opportunities to complete your MSc summer dissertation projects in client-based projects, where a number of host organisations will be arranged by the department. These projects will be normally unpaid, however, all costs such as travel and accommodation will normally be covered by the host organisation if out of town.

Student sitting in a pod in the Business School.

Strathclyde Business School

Strathclyde Business School was founded in 1948 and is a pioneering, internationally renowned academic organisation with a reputation for research excellence. One of four faculties forming the University of Strathclyde, SBS is a triple accredited business school (AMBA, EQUIS and AACSB) and was the first business school in Scotland to achieve this accolade in 2004. The Business School is home to seven subject departments and a number of specialist centres, all of which collaborate to provide a dynamic, fully-rounded and varied programme of specialist and cross-disciplinary courses.

Strathclyde Business Network

As a postgraduate student at Strathclyde Business School, you may choose to join the Strathclyde Business Network, a student led initiative that facilitates interaction with business and industry leaders. The Network aims to foster knowledge sharing, facilitate discussion and enable networking opportunities with the very best business professional in industry. Every year the Network organises Glasgow Business Summit, which is the first ever student led business conference in Scotland, and brings together students with leading businesses from across the UK.

Chat to a student ambassador

Want to know more about what it’s like to be a Strathclyde Business School student at the University of Strathclyde? A selection of our current students are here to help!

Our Unibuddy ambassadors can answer all the questions you may have about their course experiences and studying at Strathclyde, along with offering insight into life in Glasgow and Scotland.

Chat now!

Triple-accredited business school

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Course content

For those in full-time employment, it may be possible to take the course over three years and spread the workload after discussions with the course director.

Year 1

  • Data Analytics in R (Semester 1)
  • Business and Decision Modelling (Semester 1)
  • Data Analytics in Practice (Semesters 1 & 2)*
  • Two elective classes (Semester 2)

Year 2

  • Big Data Tools and Techniques (Semester 1)
  • Big Data Fundamentals (Semester 1)
  • Optimisation for Analytics (Semester 2)
  • Two elective classes (Semester 2)
  • Dissertation

*This class is completed over both semesters and possibly over two years, after confirmation with the lecturer of the class. In addition part-time students are required to discuss this class with the course director as attendance is not required if they can fit this into their current job role.

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

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

Business & Decision Modelling

This course will provide the fundamental business modelling skills such as generic problem-solving and basic methodological issues, as well as a good detailed overview of decision analysis techniques relevant to analytics, including decision trees and multi-criteria decision analysis.

Optimisation for Analytics

This course will provide the fundamental optimisation knowledge necessary to the students, such as network optimisation and integer programming, and develop their practical understanding by modelling challenging problems and understanding algorithmic aspects.

Data Analytics in Practice

This class will provide the crucial opportunity for the students to apply their broad knowledge of tools and techniques from other data analytics classes to messy business problems that are presented to them by real clients.

Students are required to choose 40 credits worth of elective classes, and at least from two departments. All optional classes take place in Semester 2.

Department of Computer & Information Sciences

Database Fundamentals

This class will help students 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

Evolutionary Computation for Finance 1

Evolutionary computing techniques are computational algorithms that use inspiration from systems and phenomena that occur in the natural world. This class will introduce students to the nature of evolutionary computing, in particular genetic algorithms and genetic programming, and enable them to develop and apply these algorithmic techniques to financial applications.

Evolutionary Computation for Finance 2

This class will explore the more advanced aspects of evolutionary computing and machine learning with special emphasis on financial applications and large times-series datasets. The class will focus on strategies such as neural networks and deep learning.

Legal, Ethical & Professional Issues for the Information Society

This class will give an overview of the legal, ethical and social issues involved in managing digital data. This will cover topics such as privacy, security, intellectual property and various aspects of cybercrime. This will allow both designers and managers to make informed decisions about data management.

Fundamentals of Machine Learning for Data Analytics

To aim of this class is to equip students with a sound understanding of the principles of machine learning and a range of basic approaches, along with the knowledge of how and when to apply the techniques.

Machine Learning for Data Analytics

The aim of the class is to:

  • understand the aims and fundamental principles of machine learning
  • understand a range of the key algorithms and approaches to machine learning
  • be able to apply the algorithms covered and interpret the outcomes
  • understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations

Department of Mathematics & Statistics

Financial Econometrics

10 credits

You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.

Topics covered will include:

  • basic statistics in finance
  • Time Series modelling
  • financial volatility modelling
  • forecasting

Bayesian Spatial Statistics (20 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

Mathematical Introduction to Networks

This class will demonstrate the central role network theory plays in mathematical modelling. It'll also show the intimate connection between linear algebra and graph theory and how to use this connection to develop a sound theoretical understanding of network theory. Finally, it'll apply this theory as a tool for revealing structure in networks.

Department of Management Science

Stochastic Modelling for Analytics

This elective course will offer the students an opportunity to learn methods to analyse systems with uncertainty, as uncertainty modelling is key to a number of applications.

Business Simulation Modelling

The module will focus on the main two forms of business simulation:

  • discrete-event simulation (DES)
  • system dynamics (a continuous simulation technique)

The class will provide a rational approach to simulation using a number of examples from manufacturing and service operations.

Risk Analysis & Management

10 credits

This module will explore the entire process of structuring a risk problem, from modelling it to communicating recommendations, both theoretically and in practice.

Risk management is linked with decision analysis in so far as we explore decision making under uncertainty and it has links with quantitative business analysis as we explore the use of statistics in understanding risk. However, the topic has some unique attributes such as risk communication and the role that experts play in risk assessment.

Business Information Systems

The class adopts a process-based approach, ie all discussion follows the logic of the business processes. You'll be introduced to Business and Information Systems (BIS) as well as the various types of IS, including the databases, Enterprise Resource Planning (ERP) systems, knowledge-based systems, corporate portals and collaboration support systems.

Lectures will provide you with the conceptual knowledge, and in tutorials you'll gain hands-on experience of using packages of the various IS categories.

Learning & teaching

Core and elective classes will be taught across two semesters running from September to December and January to March. Classes will be taught through a combination of lectures, tutorials, hands-on software sessions, projects and case studies. The dissertation is undertaken during the summer months.


Classes are assessed by various methods, including written assignments, exams, practical team projects, presentations and individual projects. Exams will take place at the end of each semester in December and April/May.


Strathclyde Business School (SBS) is one of a few triple-accredited business schools in the world and is one of the largest of its kind in Europe. SBS was also selected as the Business School of the Year in Times Higher Education (THE) Awards 2016.

The three departments involved in this course work together to provide a dynamic, fully-rounded and varied programme of specialist and cross-disciplinary postgraduate course.

Guest lectures

Every year, guest speakers attend our course, sharing their invaluable experiences. As part of the Data Analytics in Practice module, we host several presentations from external bodies.

FT European Business Schools 2023 Ranking logo
FT European Business Schools 2023 Ranking logo

Our students

Abdul Rehman, MSc Data Analytics student

Abdul Rehman

The partnership of Strathclyde with different companies, including Scottish Power and NHS, gave us an opportunity to provide solutions to the problems of these big companies by working on the real massive datasets which actually prepared us for what we will be working on after our degree ends.
Nicolas Kirsch

Nicolas Kirsch

My classmates and I agreed that the amount of material learned and the progress made over a short time is impressive and very rewarding, going from first steps in coding in September, to building deep neural networks with advanced architectures by April.
Divjot Kaur Narula

Divjot Kaur Narula

The Data Analytics in Practice class in particular taught me the necessary soft skills and technical knowledge of learning how to use really huge datasets, to provide solutions to real-world problems. It offered a glimpse of what we will be working on after graduation.
Scott Docherty

Scott Docherty

I would absolutely recommend to anyone interested in a career which is remotely related to numbers/programming/analysis to sign up for this course. I feel much more confident when applying for jobs thanks to the skills I learned over the last year and my CV has improved significantly by being able to say I can use R, Python, SQL, MATAB and others.
Matthew Shedden

Matthew Shedden

The ‘Data Analytics in Practice’ module and summer project provide an environment exclusively where skills can be reapplied to real-world problems. Personal highlights from this module were predicting Glasgow traffic activity and building ML models to predict (somewhat successfully) the price of cryptocurrencies.
Vlad Cherman

Vlad Cherman

The module that really brings everything together is ‘Data Analytics in Practice’. This module is excellent at giving students a taste of the type of issues faced in real settings, and is especially great if the goal is to prepare yourself for what will come in the future.
Andrea Oteo Valmaseda

Andrea Oteo Valmaseda

‘Data Analytics in Practice’ is one of the classes that caught my attention in this programme. It gives you the opportunity to make real contacts with workers from leading companies around the world and to apply the knowledge you learnt during the classes to real projects.
Picture of student

Sally Buchanan

The course has refined and improved my analytical and problem-solving skills whilst expanding my knowledge and confidence to analyse large datasets using a range of techniques.
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Entry requirements

Academic requirements

MSc: Minimum second class Honours degree, or overseas equivalent (see our country pages for further information) in:

  • mathematics
  • the natural sciences
  • engineering
  • economics/finance

Applications from those with other degrees are also encouraged if you have demonstrated a good grasp of numerical/quantitative subjects.

There will be significant programming elements to the programme; many modules will require the student to use software packages such as Python and R, along with others. Students should have experience of working with programming languages, and a willingness to learn new ones.


English language requirements

Students whose first language is not English must have a minimum of 6.5 IELTS score, with no individual score lower than 5.5. Get more information about the English language requirements for studying at Strathclyde.

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.

Please note: Previous Maths & English qualifications and your undergraduate degree must meet GTCS minimum entry requirements as well as the pre-Masters course and an interview will be conducted before an offer can be made.

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.

Visit our international students' section

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Fees & funding

All fees quoted are for full-time courses and per academic year unless stated otherwise.

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 tuition fees are revised annually and may increase in subsequent years of study. Annual increases will generally reflect UK inflation rates and increases to programme delivery costs.

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England, Wales & Northern Ireland




Additional costs
Course materials

Class materials comprise textbooks and course handbooks.  All of the compulsory handbooks are available to students free on the VLE.  Some classes may have a recommended core textbook which you may wish to purchase but copies will be available in the University Library.

Placements & field trips

May incur travel costs depending on clients and project placement. Will be confirmed with students before commencement of the placement.

International students

International students may have associated visa and immigration costs. Please see student visa guidance for more information.

Other costs

Students are required to submit two hard copy dissertations. An average cost will be £10-15 including delivery to the department.

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?

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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.

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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.

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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.

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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.

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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.

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The aim of the course is to develop graduates who can use data analytics technology, understand the statistical principles behind the technologies and understand how to apply these technologies to solve business problems.

Graduates will be able to bridge the various knowledge domains that are relevant for tackling data analytics problems as well as being able to identify emerging themes and directions within data analytics.

Graduates will display abilities across the three component disciplines. Examples of graduate employers and job roles include; Software Development Engineer - Machine Learning at RBS, Junior Data Scientist at V.Group, Data Scientist at Solita Scandinavia, Business Analyst at Scottish Power, IT Graduate at Scottish Power.

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For information and guidance on the application process, take a look at our How to Apply web page.

Start date: Sep 2024

Data Analytics

Start date: Sep 2024

Start date: Sep 2024

Data Analytics

Start date: Sep 2024

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Contact us

SBS Postgraduate Admissions

Telephone: +44 (0)141 553 6105 / +44 (0)141 553 6116


Strathclyde Business School, University of Strathclyde
199 Cathedral Street
G4 0QU

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