Data Analytics (MSc)

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. 

Why this course?

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

• 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.

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.

Course content
Core Classes

• Big Data Fundamentals
• Foundations of Statistics (10 Credits)
• Data Analytics in R
• Business & Decision Modelling
• Optimisation for Analytics
• Data Analytics in Practice

Elective 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
• Evolutionary Computation for Finance 1
• Evolutionary Computation for Finance 2
• Legal, Ethical & Professional Issues for the Information Society
• Fundamentals of Machine Learning for Data Analytics
• Machine Learning for Data Analytics

Department of Mathematics & Statistics

• Financial Econometrics
• Bayesian Spatial Statistics (20 credits)
• Statistical Machine Learning (10 Credits)
• Data Dashboards with R Shiny (10 Credits)

Department of Management Science

• Stochastic Modelling for Analytics
• Business Simulation Modelling
• Risk Analysis & Management
• Business Information Systems

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

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.

Assessment

• 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.

Facilities

• 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.

Careers

• 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