- 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
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:
- Department of Management Science
- Department of Mathematics & Statistics
- Department of Computer & Information Sciences
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.
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 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 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.
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.
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
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.
- Data Analytics in R (Semester 1)
- Business and Decision Modelling (Semester 1)
- Data Analytics in Practice (Semesters 1 & 2)*
- Two elective classes (Semester 2)
- Big Data Tools and Techniques (Semester 1)
- Big Data Fundamentals (Semester 1)
- Optimisation for Analytics (Semester 2)
- Two elective classes (Semester 2)
*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
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
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
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.
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.
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.
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
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.
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.
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.
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
‘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.
MSc: Minimum second class Honours degree, or overseas equivalent (see our country pages for further information) in:
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 EU/UK) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.
Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.
We've a thriving international community with students coming here to study from over 100 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
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 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.
|England, Wales & Northern Ireland|
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 may have associated visa and immigration costs. Please see student visa guidance for more information.
Students are required to submit two hard copy dissertations. An average cost will be £10-15 including delivery to the department.
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.
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.
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.
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.
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.
Glasgow is Scotland's biggest & most cosmopolitan city
Our campus is based right in the very heart of Glasgow. We're in the city centre, next to the Merchant City, both of which are great locations for sightseeing, shopping and socialising alongside your studies.Life in Glasgow
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