MSc Applied Statistics

Key facts

  • Start date: September
  • Study mode and duration: MSc: 12 months full-time, 24 months part-time
  • Accreditation: Awaiting accreditation from The Royal Statistical Society

Study with us

  • conversion course for those with a background in a broad range of disciplines 
  • gain skills in problem-solving, big data and use of statistical software packages
  • learn to interpret and report the result from data analyses
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Why this course?

This is a conversion course, designed for candidates from a broad background of disciplines. Students will gain skills in:

  • problem solving
  • big data
  • the use of statistical software for data analysis and reporting

Programme skillset

On the Applied Statistcs MSc programme you'll have the opportunity to acquire:

  • an in-depth knowledge of modern statistical methods used to analyse and visualise 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 widely sought after in the commercial sector
  • practical experience of statistical consultancy and how to interact with professionals who require statistical analyses of their data

Statistical information on laptop screen

What you’ll study

All the classes are elective, meaning you can tailor the course in line with your career interests.

Semester 1
You'll take modules to equip you with fundamental statistical and data analysis skills.

Semester 2
Classes focus on a different applied element of being a statistician/analyst.

Semester 3
You'll undertake a research project in which you'll work on a real-life data set, putting the theoretical skills you have learned into practice.

Learning & teaching

Classes are delivered by a number of teaching methods:

  • lectures (using a variety of media including electronic 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 from class to class. For most classes the assessment involves both coursework and examinations.

Facilities

The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and access to University computing laboratories, with all necessary software available.

You'll also have access to a common room facility which gives you a modern and flexible area for individual and group study work, and is also a relaxing social space.  

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Course content

You are required to take 120 credits of optional classes from those listed below in Semesters 1 and 2.

Foundations of Probability & Statistics

The course and thus this introductory class is aimed at graduates who have not previously studied statistics at university level. The class will provide the foundation elements of probability and statistics that are required for the more advanced classes studied later on.

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.

Applied Statistical Modelling

This class will cover the fundamental statistical methods necessary for the design and analysis of scientific experiments. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation.

Statistical Inference

This class will provide students with statistical methodology and the R-codes necessary to conduct statistical inferences and tests. Gain insight into approaches to parameter estimation, focusing on maximum likelihood estimation, bootstrap estimation, and properties of estimators. This course will also present hypothesis testing procedures, including classical likelihood ratio tests and goodness-of-fit tests.

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.

Medical Statistics

This class will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for health care research. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation. Software packages such as Minitab will be introduced.

Quantitative Risk Analysis
This class will cover the theory of assessing risks under uncertainty. It will focus on the practical assessment of risk using simulation methods such as Monte Carlo simulation. You'll develop skills in communicating risk to risk managers as well as formulating practical risk questions that can influence policy decisions.
Survey Design & Analysis

Surveys are an important way of collecting data. This class will introduce you to the methods that are commonly used in health care to design questionnaires and analyse data resulting from these questionnaires.

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.

Effective Statistical Consultancy

This cover all aspects of statistical consultancy skills necessary for being a successful statistician working in any research environment. You'll work on real-life problems in small groups and have the opportunity to interact with health care researchers to formulate hypotheses.

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.

Financial Stochastic Processes

The class aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.

Business Analytics

Every two days, we generate as much data as the data generated in all human history up to 2003. From online data on every click of the mouse on the internet through the huge upsurge in manufacturing companies’ use of sensors to sports organisations collecting in game data. With these increased quantities of data comes an increased need for tools to make sense of the main messages coming from these data.

The class will build on the fundamental multivariate statistics by developing both visualisation and advanced analysis techniques relevant in the area of big data. The focus will be on application and interpretation of techniques and there will be an investigation of what makes good data. The class will develop both new theoretical knowledge in the form of analytics techniques as well as new software skills in relevant analytics software.

Research project
You'll undertake a research project in which you'll work on a real-life data set, putting the theoretical skills you have learned into practice.
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Entry requirements

Academic requirements/experience

Minimum second-class 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, eg A Level or equivalent in:

  • calculus
  • linear algebra
  • differential equations

If you have any questions, email (science-masters@strath.ac.uk).

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. View the full list of accepted English language tests here.

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.

International students

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.

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Fees & funding

2020/21

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

Scotland/EU

£8,100

Rest of UK

£8,100

International

£18,100

Available scholarships

Take a look at our scholarships search for funding opportunities.

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?

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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.

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

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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.

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

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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.

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

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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.

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

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International students

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.

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Careers

The MSc in Applied Statistics will provide graduates with skills in the statistical analysis of data from a wide range of disciplines. 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 such as Google or Bing

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!

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Apply

Applied Statistics

Qualification: MSc
Start Date: Sep 2020
Mode of Delivery: full-time

Applied Statistics

Qualification: MSc
Start Date: Sep 2020
Mode of Delivery: part-time

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Contact us

PGT Admissions Team

Telephone: +44 (0)141 574 5147

Email: science-masters@strath.ac.uk

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