Program Overview

Program Overview

The MSc Applied Statistics with Data Science is a part-time online program designed for those with a background in a broad range of disciplines. The program aims to provide students with skills in problem-solving, manipulation, and interrogation of big data sets and the use of programming languages commonly used in statistics and data science.

Key Facts

• Start date: September or January
• Accreditation: Royal Statistical Society: MSc graduates may qualify for GradStat status
• Study mode and duration: online over 2 or 3 years, part-time. Standalone modules can also be taken for CPD purposes or working towards an MSc over a maximum of 5 years.

Course Content

• Throughout the studies, students will take 90 credits of compulsory taught classes, 30 credits of elective taught classes, and in the final year, they will also undertake their MSc Project (60 credits)
• September start program terms are as follows:
  • Term 1: September to December
  • Term 2: January to April
  • Term 3: April to July
• January start program terms are as follows:
  • Term 1: January to April
  • Term 2: April to July
  • Term 3: September to December

Compulsory Classes

• Foundations of Probability & Statistics: 20 credits
  • Introduction to probability distributions
  • Introductory hypothesis testing
  • Non-parametric hypothesis testing
  • Linear regression
  • Introductory power and sample size calculations
• Data Analytics in R: 20 credits
  • Use of functions and packages in R
  • Use of the tidyverse for data manipulation
  • Data visualization using both base R and ggplot2
  • Multiple linear regression
  • Using variable selection techniques to cope with large data sets
  • More general model comparison
• Statistical Modelling & Analysis: 20 credits
  • Fundamental principles of statistical modeling through experimental design and multivariate analysis
  • Statistical models used in the analysis of balanced experimental designs
  • Concepts of data reduction, clustering, and classification
• Big Data Tools & Techniques: 10 credits
  • Design and implementation of cloud NoSQL systems
  • Addressing design trade-offs and their impact
  • The Map-Reduce programming paradigm
• Big Data Fundamentals: 10 credits
  • Fundamentals of Python for use in big data technologies
  • Classical statistical techniques applied in modern data analysis
  • Limitations of various data analysis tools in a variety of contexts
• Data Dashboards with RShiny: 10 credits
  • Creating a data dashboard in RStudio
  • User interface design with respect to accessibility
  • Creating interactive data visualizations
  • Reactive programming in RStudio
  • Static programming in R

Elective Classes

Students are required to take at least 10 credits from List A and the remaining 20 credits can be from List A and/or List B modules.

List A

• Quantitative Risk Analysis: 10 credits
  • Uncertainty and variability
  • Bootstrapping
  • Monte Carlo Simulation
  • Selecting appropriate probability distributions based on given scenarios
• Bayesian Spatial Statistics: 10 credits
  • Visualizing spatial data
  • Geospatial data, including methods for prediction
  • Bayesian modeling using software to implement Markov Chain Monte Carlo
  • Areal unit modeling

List B

• Survey Design & Analysis: 10 credits
  • Designing appropriate survey questions
  • Various sampling methods
  • Analyzing data for different sampling methods
• Effective Statistical Consultancy: 10 credits
  • Engaging with professionals working in business, industry, and the public sector
  • Applying statistical knowledge in different situations
  • Effectively communicating statistical results to non-statisticians
• Medical Statistics: 20 credits
  • Fundamental statistical methods necessary for the application of classical statistical methods to data collected for healthcare research
  • Emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation
• Financial Econometrics: 10 credits
  • Basic statistics in finance
  • Time Series modeling
  • Financial volatility modeling
  • Forecasting
• Financial Stochastic Processes: 10 credits
  • Stochastic models arising in finance
  • Financial options
  • The Black-Scholes equation
  • Simulation of financial mathematical models
• Machine Learning for Data Analytics: 20 credits
  • Principles of Machine Learning
  • Core machine learning algorithms
  • Understanding when to apply which algorithm
  • Deep learning
  • Artificial neural networks

Learning & Teaching

• Classes are delivered using the MyPlace online teaching environment hosted by the University of Strathclyde.
• Students learn through video lectures, interactive sessions, independent reading of articles and texts, and discussion forums.
• On average, students study five hours of online material per module per week, plus additional self-study.
• Regular assistance from dedicated tutors who interact and communicate with students through online forums and email.

Assessment

• All assessment will be undertaken online.
• The assessment will take the form of large-scale projects where students will be asked to demonstrate their knowledge on a real-world data set.
• Projects will involve writing code, interpreting statistical outputs, and producing a report or presentation outlining the findings from the analysis.
• Group work may be undertaken in some classes.

Entry Requirements

• Academic requirements/experience: Minimum second-class (2:2) Honours degree or overseas equivalent. Mathematical training to A Level or equivalent standard.
• Mathematical knowledge: Applicants are required to have some prior mathematical knowledge, such as A Level or equivalent in calculus, linear algebra, and differential equations.
• English language requirements: Students must have an English language minimum score of IELTS 6.0 (with no component below 5.5).

Fees & Funding

• Tuition fees may be subject to updates to maintain accuracy.
• Annual revision of fees: Students on programs of study of more than one year should be aware that the majority of fees will increase annually.
• 2025/26:
  • 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.
  • Tuition fees:
    • £5,600 (3-year program, price per year)
    • £8,400 (2-year program, price per year)
  • Additional costs: International students may have associated visa and immigration costs.
  • Available scholarships: Scholarships of £1,800 are available to new students joining for January entry of one of the online programs in the 2025/26 academic year.

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 organizations, retailers, and internet information providers.
• Typical job roles include:
  • Statistician
  • Data analyst
  • Software developer or engineer
  • Statistical programmer
  • Data scientist

Teaching Staff

The following staff are involved in the teaching and research project supervision:

• Dr. Bingzhang Chen: An ecologist focusing on marine plankton with experience in employing various statistical techniques.
• Dr. Tunde Csoban: Teaching Associate with research interests in women’s health, mental health, equity, diversity, and inclusion.
• Dr. Alison Gray: Research interests center on applications of statistics in honeybee research.
• Dr. Helen He: Lecturer in Medical Statistics and a Real-World Evidence (RWE) pharmacoepidemiologist.
• Dr. David Hodge: Teaching Associate with particular interests in probability and applications of probability and statistics to decision making under uncertainty.
• Dr. Kim Kavanagh: Statistical expertise in the analysis and modeling of large observational health datasets.
• Dr. Louise Kelly: Senior Teaching Fellow with a general interest in quantitative risk assessment and epidemiology.
• Prof. Adam Kleczkowski: Works on modeling of disease systems at the interface of epidemiology, socio-economics, and policy.
• Dr. Ainsley Miller: Teaching Fellow with a focus on mathematics and statistical pedagogy.
• Dr. Jiazhu Pan: Main research interests include Time Series Analysis and Econometrics with applications in modeling complex spatio-temporal data.
• Prof. Chris Robertson: Professor of Public Health Epidemiology and Statistical Advisor at Public Health Scotland.
• Dr. Ryan Stewart: Teaching Associate with interest in oral health and statistical pedagogical research.
• Dr. Florence Tydeman: Research Associate in Statistics and Knowledge Exchange.
• Dr. David Young: Part-time Senior Consultant Statistician for NHS Scotland.
• Connor Watret: Teaching Associate with an interest in disease modeling in UK forests.
• Dr. Suzy Whoriskey: Director of Knowledge Exchange in Mathematics & Statistics.
• Dr. Yue Wu: PhD in Stochastic Analysis from Loughborough University.