- 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
PgDip: 9 months full-time
Study with us
- 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.
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
This class aims to endow students with an understanding of the new challenges posed by the advent for big data, as they refer to its modelling, storage, and access, along with an understanding of the key algorithms and techniques which are embodied in data analytics solutions.
Big Data Tools & Techniques
The aim of this class is to endow students with an understanding of the new challenges posed by the advent for big data, as they refer to its modelling storage, and access, and to expose them to a number of different big data technologies and techniques, showing how they can achieve efficiency and scalability, while also addressing design trade-offs and their impacts.
Data Analytics in R
This class will introduce the R computing environment and enable you to import data and perform statistical tests. The class will then focus on the understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures.
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.
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
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.
Bayesian Spatial Statistics
This class will introduce you to Bayesian statistics and the modern Bayesian methods that are used in health care research. Again, the focus is on real-life data and using statistical software packages for analysis.
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.
MSc: Minimum second class Honours degree, or overseas equivalent - see our country pages for further information - in mathematics, the natural sciences, engineering, or economics/finance. Applications from those with other degrees are also encouraged if you have demonstrated a good grasp of numerical/quantitative subjects.
PgDip: Minimum of a Pass degree or equivalent in an appropriate subject. Subject to performance, Diploma students may transfer from to the MSc.
|English language requirements|
If you’re a national of an English speaking country recognised by UK Visas and Immigration (please check most up-to-date list) or you have successfully completed an academic qualification (at least equivalent to a UK bachelor's degree) in any of these countries, then you do not need to present any additional evidence.
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
If you're from a country not recognised as an English speaking country by UK Visas and Immigration, please check English requirements before making your application.
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 will 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.
|Rest of UK|
iMPM students have the option of taking MBA elective classes overseas. Students cover travel and accommodation themselves, but no costs associated with teaching.
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 and non-UK EU postgraduate students
Scottish and non-UK EU 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.
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 in the very heart of Glasgow, Scotland's largest city. National Geographic named Glasgow as one of its 'Best of the World' destinations, while Rough Guide readers have voted Glasgow the world’s friendliest city! And Time Out named Glasgow in the top ten best cities in the world - we couldn't agree more!
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.
Find out what some of our students think about studying in Glasgow!Find out all about life in Glasgow
Start Date: Sep 2020
Mode of Delivery: full-time
Start Date: Sep 2020
Mode of Delivery: part-time
Have you considered?
We've a range of postgraduate taught and Masters courses similar to this on which may also be of interest.