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MSc, PgDipData Analytics

Why this course?

Our MSc in data analytics is designed to create rounded data analytics problem-solvers.

This 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.

Strathclyde's MSc in data analytics is unique by bringing together essential skills from three departments, Management Science, Mathematics & Statistics, and Computer & Information Sciences (CIS), in order to address the needs of a fast-growing industry.

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

What you’ll study

The course will have a duration of 1 year, with two semesters of classes (120 credits in total) followed by an MSc dissertation project (60 credits) during the summer.

The class Data Analytics in Practice (20 credits) will be run over both semesters to provide you with a practical environment to apply methodological learnings from other classes into challenging projects from industry.

Semester 1

Semester 1 will additionally consist of five 10-credit core modules as listed under 'Course Content' which will provide the technical background to students. The contributions in Semester 1 will be split evenly between three departments.

This semester 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 will additionally consist of a 10-credit core module as well as 40 credits worth of elective modules. To ensure breadth of knowledge, you'll be required to choose electives from at least two departments. This semester 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.

The only technical core class will provide you with a thorough theoretical and practical understanding of optimisation techniques essential for data analytics, whereas each of the three departments will offer four to five elective courses, the majority of which are accessible to everyone on the course without any prerequisites. The final component of the MSc course will be a summer dissertation project, which can be completed either through a client-based project or a desk-based research project, depending on your interests. You will submit your dissertation in September to complete your  degree requirements (pending any resits).

Work placement

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 be covered by the host organisation if out of town.

Major projects

The taught modules on the programme introduce you to a variety of tools, techniques, methods and models. However, the practical reality of applying analytical methods in business is often far removed from the classroom. Working with decision-makers on real issues presents a variety of challenges.

For example, data may well be ambiguous and hard to come by, it may be far from obvious which data analytics methods can be applied and managers will need to be convinced of the business merits of any suggested solutions. While traditional teaching can alert students to such issues, understanding needs to be reinforced by experience.

This is primarily addressed by the core module ‘Data Analytics in Practice’, which takes place over both semesters. Every year, case studies and challenging projects are presented to our students by various organisations.

Facilities

Strathclyde Business School (SBS) is one of the 76 triple-accredited business schools in the world, and is one of the largest of its kind in Europe. SBS was also recently selected as the "Business School of the Year" in Times Higher Education (THE) Awards."

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.

Course content

Compulsory classes

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.

Dissertation in Data Analytics

The MSc project is the most substantial and independent piece of work you'll carry out during your course. This big project, either a desk-based research project or a client-based industrial project, will finalise your learning experience in the course, exposing you to challenging problems.

Optional classes

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.

Department of Mathematics & Statistics

Financial Econometrics

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.

Networks in Finance

This module will introduce you to a number of diverse topics in game theory and its applications to financial problems as well as giving a sound background on network theory at both theoretical and applied level.

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, modelling it, supporting and 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; i.e. all discussion follows the logic of the business processes. After having the business and IS context of the knowledge work introduced, the various types of IS, namely the databases, ERP systems, knowledge-based systems, corporate portals, collaboration support systems.

The course will provide you with conceptual knowledge introduced in the lectures, as well as hands-on experience gained in tutorials using appropriate packages of the various IS categories.

Learning & teaching

The course is delivered in various ways. While most classes have regular lectures, tutorials and hands-on software sessions, experiential learning is a crucial part of the course. This is delivered through projects and case studies with various external organisations, and MSc projects.

There are also guest lectures and recruitment events throughout the year, as well as a number of career support sessions that provide you with invaluable career information and generic job hunting skills such as CV writing and how to handle interviews.

Assessment

Every module has its own methods of assessment appropriate to the nature of the material. These include written assignments, exams, practical team projects, presentations and individual projects. Many modules involve more than one method of assessment to realise your potential.

Entry requirements

MSc

Second-class Honours degree, or equivalent, 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 the diploma course to the MSc.

English language requirements

If you’re a national of an English speaking country recognised by UK Border Agency (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.

For others, the department requires a minimum overall IELTS score of 6.5 (with no individual component below 5.5 (or equivalent)). Pre-sessional courses in English are available.

If you're from a country not recognised as an English speaking country by the United Kingdom Border Agency (UKBA), please check English requirements before making your application.

Pre-Masters preparation course

The Pre-Masters Programme is a preparation course for international students (non EU/UK) who do not meet the entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.

To find out more about the courses and opportunities on offer visit isc.strath.ac.uk or call today on +44 (0) 1273 339333 and discuss your education future. You can also complete the online application form. To ask a question please fill in the enquiry form and talk to one of our multi-lingual Student Enrolment Advisers today.

Fees & funding

How much will my course cost?

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

Scotland/EU

  • 2017/18 - £9,500

Rest of UK

  • 2017/18 - £9,500

International

  • 2017/18 - £18,000

How can I fund my course?

You may apply for a scholarship towards your tuition fees for the Data Analytics (MSc) 2017 entry (full-time only). Find out more about what is available and how you can apply for the Data Analytics Scholarship. 

Check our Scholarship Search for more help with fees and funding.

Scottish and non-UK EU postgraduate students

Scottish and non-UK EU postgraduate students starting in 2017 can 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 can 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 Wales

Postgraduate students starting in 2017 who are ordinarily resident in Wales can apply for 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. We are waiting on further information being released about this 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 starting in 2017 who are ordinarily resident in Northern Ireland can apply for support from Student Finance NI. The support is a tuition fee loan of up to £5,500. We are waiting on further information being released about this support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

International students

We have a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.

Please note

The fees shown are annual and may be subject to an increase each year. Find out more about fees.

Careers

The aim of the MSc in data analytics 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

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Data Analytics

Qualification: MSc, Start date: Sep 2017, Mode of delivery: attendance, full-time

Data Analytics

Qualification: MSc, Start date: Sep 2017, Mode of delivery: attendance, part-time

Data Analytics

Qualification: MSc, Start date: Sep 2018, Mode of delivery: attendance, full-time

Data Analytics

Qualification: MSc, Start date: Sep 2018, Mode of delivery: attendance, part-time

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