MSc Applied Statistics with Data Science (online)

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Key facts

  • Start date: Sep 2021
  • Accreditation: On successful completion of the MSc, students may be eligible for GradStat status.
  • Study mode and duration: Part-time online course over 36 months

Study with us

  • A conversion course, designed for those with a background in a broad range of disciplines

  • Gain skills in problem-solving, manipulation and interrogation of big data sets and use of programming languages commonly used in statistics and data science

  • Learn to interpret and report the result from data analyses

How could the Covid-19 pandemic affect my studies?

Covid-19: information & FAQs
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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 technologies
  • the use of statistical software for data analysis and reporting
  • Python and R programming for data analysis
  • cloud storage systems

Programme skillset

On the online Applied Statistics with Data Science 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, including working with big data sets
  • 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
  • skills in working with big data technologies including programming in Python and R
  • knowledge of cloud-based storage for large data sets

Abstract data concept.

THE Awards 2019: UK University of the Year Winner

What you'll study

In addition to compulsory classes, there are a range of elective classes, meaning you can tailor the course in line with your career interests.

Year 1

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

Year 2

Compulsory modules with a focus on big data are undertaken, as well as elective modules, which allows you to tailor the course to your own interest areas.

Year 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.

Accreditation

On successful completion of the MSc, students may be eligible for GradStat status. This may be awarded by submitting a transcript to the Royal Statistical Society as part of the evidence of meeting RSS GradStat criteria.

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Course content

  • In Year 1 all classes are compulsory, this amounts to 60 credits
  • In Year 2 you are required to take 20 credits of compulsory classes and 40 credits of optional classes
  • In Year 3 you'll undertake your MSc Project
  • With the approval of the Course Director, students may substitute other appropriate classes offered by the University for one or more of the optional classes listed below

Compulsory modules

The following modules are worth 20 credits each:

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.

Statistical Modelling & Analysis

This class provides students with the fundamental principles of statistical modelling through experimental design and multivariate analysis. The statistical models used in the analysis of balanced experimental designs are derived and used in the analysis of data sets. The fundamental principles of balance, replication, blocking, interactions, nesting and repeated measures are all covered. In the multivariate part of the class, the concepts of data reduction, clustering and classification are discussed.

Compulsory modules

The following modules are worth 10 credits each:

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.

Optional modules

The following modules are worth 10 credits each:

Survey Design & Analysis

Surveys are an important way of collecting data. This class will introduce you to the methods that are commonly used 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 a variety applications. Like with other classes, the focus is on real-life data and using statistical software packages for analysis.

Effective Statistical Consultancy

This class covers all aspects of statistical consultancy skills necessary for being a successful statistician working in any research or customer environment. You will work on real-life problems in small groups and have the opportunity to interact with stakeholders researchers to formulate hypotheses.

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.

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.

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.

The following modules are worth 20 credits:

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.

The following module is worth 60 credits:

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.

Learning & teaching

Classes are delivered using the MyPlace online teaching environment hosted by the University of Strathclyde.

You’ll learn through video lectures, interactive sessions, independent reading of articles and texts and discussion forums.

On average you'll study five hours of online material per week plus additional self-study. You’ll also have regular assistance from dedicated tutors who'll interact and communicate with you through online forums and email.

You'll be part of a community of students working collaboratively to share and enhance learning.

Assessment

  • The form of assessment varies for each class and are all undertaken online
  • For most classes, the assessment involves both coursework and examinations
  • Coursework will typically involve a project where you'll analyse data, write code, interpret statistical outputs and produce a report
  • Group work may be undertaken in some classes
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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, such as 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.

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Fees & funding

The MSc in Applied Statistics with Data Science (online) consists of 180 credits studied over three years. Students will study 60 credits annually.

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Scotland

£3,750 (60 credits)

England, Wales & Northern Ireland

£3,750 (60 credits)

International

£3,750 (60 credits)

Take a look at our scholarships search for funding opportunities.

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Careers

The online MSc in Applied Statistics with 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

Graduate roles

Typical job roles include:

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

Please note there is no deadline for submitting applications.

Applied Statistics with Data Science (online)

Qualification: MSc
Start Date: Sep 2021
Mode of Attendance: part-time

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Contact us

PGT Admissions Team

Telephone: +44 (0)141 574 5147

Email: science-masters@strath.ac.uk