University of Strathclyde: Faculty of Science

MSc Applied Statistics with Data Science (online)

Why this course?

Our online MSc in Applied Statistics with Data Science is a conversion course, that offers the opportunity to develop skills in statistics and data analysis even if you've never studied statistics before. You'll be supported through this part-time program by members of staff who work directly with industry to develop skills that are relevant to current areas of research including population health and medicine, animal and plant health, finance, and business. You'll gain skills in:

• problem-solving
• big data technologies
• the use of statistical software for data analysis and reporting
• Python and R programming for data analysis
• cloud storage systems

The course is entirely delivered online. The course is ideally suited to those working full-time or with other commitments. You can study and complete the modules when it’s most convenient for you – you don’t need to be online at specific times.

The course has been designed by academics who also work as statisticians in the public sector. They are experts in understanding real-life statistical problems, data, and relating theory to practice.

The skills set provided will also equip you with the necessary training to work as an applied statistician or data analyst/scientist in a broad range of areas including health, insurance, finance, and social sciences.

Program skillset

On the online Applied Statistics with Data Science MSc program you'll have the opportunity to acquire:

• in-depth knowledge of modern statistical methods used to analyze and visualize real-life data sets, and the experience of how to apply these methods in a professional setting
• skills in using statistical software packages used in government, industry, and commerce
• the ability to interpret the output from statistical tests and data analyses, and communicate your findings to a variety of audiences including health professionals, scientists, government officials, managers, and stakeholders who may have an interest in the problem
• problem-solving and high numeracy skills are widely sought after in the commercial sector
• practical experience in statistical consultancy and how to interact with professionals who require statistical analyses of their data
• skills in working with big data technologies including programming in Python and R
• knowledge of cloud-based storage for large data sets

Entry requirements

Academic requirements/experience

• Minimum second-class (2:2) Honours degree or overseas equivalent.
• Mathematical training to A Level or equivalent standard.
• Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply.
• For Australia and Canada, normal degrees in relevant disciplines are accepted.

Mathematical knowledge

Applicants are required to have some prior mathematical knowledge, such as A Level or equivalent in:

• calculus
• linear algebra
• differential equations

English language requirements

You must have an English language minimum score of IELTS 6.0 (with no component below 5.5).

We offer comprehensive English language courses for students whose IELTS scores are below 6.0. Please see ELTD for full details.

As a university, we now accept many more English language tests in addition to IELTS for overseas applicants, for example, TOEFL and PTE Cambridge.

Please contact the university for more information.

Course content

Compulsory classes

• Foundations of Probability & Statistics (20 credits)
• Data Analytics in R (20 credits)
• Statistical Modelling & Analysis (20 credits)
• Big Data Fundamentals (10 credits)
• Big Data Tools & Techniques (10 credits)
• Research project (60 credits)

Elective classes

• Quantitative Risk Analysis (10 credits)
• Survey Design & Analysis (10 credits)
• Financial Econometrics (10 credits)
• Financial Stochastic Processes (10 credits)
• Medical Statistics (20 credits)
• Effective Statistical Consultancy (10 credits)
• Bayesian Spatial Statistics (10 credits)
• Machine Learning for Data Analytics (20 Credits)