Program Overview

---

MSc Statistics & Data Science

The MSc Statistics & Data Science is designed for students with a background in a broad range of disciplines. It provides an opening to a career as a data scientist or applied statistician, without having previous study in the field.

Key Facts

• Start date: September
• Study mode and duration: On-campus, 12 months full-time

Course Description

This course is a conversion course that allows you to develop skills in statistics and data science without previous study in the area. You'll be supported by experts who work directly with industry and the public sector, to develop analytical skills which are relevant to the varied industries which make use of data science, including:

• processing and analysis of complex data
• the use of statistical programming for data analysis, machine learning and prediction modelling
• problem solving
• effective communication
• conducting research in the data science field

What You'll Study

There are a range of optional modules in addition to compulsory ones, so you can tailor the course to your career interests.

Semester 1

Modules in Semester 1 focus on the foundations of statistics and data science. You’ll learn about statistical analysis, processing of big data and basic prediction and classification methods as well as developing programming skills in both R and Python.

Semester 2

Modules in Semester 2 build on the foundational concepts from Semester 1. These will focus on developing a deep understanding of machine learning techniques and creating interactive dashboards to visualise data.

Optional modules allow for learning in varied areas of statistical modelling applications such as spatial statistics and risk analysis.

Semester 3

In Semester 3, you'll undertake a research project in which you'll work on a data science problem, putting the practical skills you have learned into practice.

Compulsory Modules

• Foundations of Statistics (10 credits)
• Data Analytics in R (20 credits)
• Big Data Fundamentals (10 credits)
• Big Data Tools & Techniques (10 credits)
• Multivariate Analysis (10 credits)
• Data dashboards with RShiny (10 credits)
• Statistical Machine Learning (10 credits)

Optional Modules

Choose 40 credits from the list below:

• Deep Learning (10 credits)
• Medical Statistics (20 credits)
• Effective Statistical Consultancy (10 credits)
• Survey Design & Analysis (10 credits)
• Bayesian Spatial Statistics (10 credits)
• Quantitative Risk Analysis (10 credits)
• Database Fundamentals (10 credits)

Research Project

You'll undertake a research project in which you'll work on a data science problem or analysis of a real-world data set, putting the theoretical skills you have learned into practice.

Learning & Teaching

Modules are delivered by several teaching methods:

• lectures (using a variety of media including presentations and computer demonstrations)
• tutorials
• computer laboratories
• coursework
• projects

Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.

Assessment

The form of assessment varies across modules. For most modules, the assessment involves both coursework and practical computer-based or written examinations.

The assessment will require you to demonstrate your statistical and data science knowledge by analysing, creating predictive models, and visualising data to interpret the results in the context of the research question.

Projects will involve writing code, interpreting outputs, and producing a report, interactive visualisation or presentation outlining the findings from your analysis.

Group work may be undertaken in some modules.

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.
• Mathematical knowledge: Applicants are required to have some prior mathematical knowledge, for example, 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).

Fees & Funding

Fees may be subject to updates to maintain accuracy. Tuition fees will be notified in your offer letter.

• Scotland: £11,900
• England, Wales & Northern Ireland: £11,900
• Republic of Ireland: If you are an Irish citizen and have been ordinary resident in the Republic of Ireland for the three years prior to the relevant date, and will be coming to Scotland for Educational purposes only, you will meet the criteria of England, Wales & Northern Ireland fee status.
• International: £25,500

Careers

Our MSc Statistics & Data Science will provide graduates with skills in the statistical analysis of big data. These skills are required by many employers in sectors such as:

• investment companies
• financial institutions
• pharmaceutical industry
• medical research
• government organisations
• retailers
• internet information providers

Typical job roles include:

• statistician
• data analyst
• software developer or engineer
• statistical programmer
• data scientist

Related Programmes

We've a range of Masters courses similar to this one which may also be of interest:

• MSc Applied Statistics (on-campus)
• MSc Applied Statistics (online)
• MSc Applied Statistics in Finance (on-campus)
• MSc Applied Statistics in Finance (online)
• MSc Applied Statistics in Health Sciences (on-campus)
• MSc Applied Statistics in Health Sciences (online)
• MSc Applied Statistics with Data Science (online)
• MSc Advanced Computational Mathematics
• MSc Advanced Mathematical Modelling
• MSc Actuarial Science
• MSc Advanced Computer Science with Data Science
• MSc Advanced Data Science
• MSc Data Analytics
• MSc Data Science for Politics & Policymaking
• MSc Quantitative Finance
• MSc Sport Data Analytics