Course summary

Developed in collaboration with the School of Mathematics and the Leeds Institute for Data Analytics, our online Data Science (Statistics) masters degree offers you the opportunity to learn in-demand data skills such as data acquisition, data preparation, data wrangling, modelling and analysis, and how to deal with missing data. Whether you have an undergraduate degree in a quantitative subject with substantial elements of mathematics and statistics or you’re already working in a data-driven STEM field, you’ll be ready for business-critical senior roles in areas such as healthcare or environmental science. The MSc Data Science (Statistics) offers a comprehensive curriculum that spans from foundational data science courses to specialised statistics courses. You'll also learn industry best practices and study widely used methods to understand and interpret data in a range of contexts. Because employers are looking for job candidates who can tell compelling stories with data, your projects in this programme will give you opportunities to combine different methods of presentation. Using research from Leeds Institute of Data Analytics, and others, you’ll work on projects in innovative areas such as AI, health informatics, urban analytics, statistical and mathematical methods, and visualisation and immersive technologies. Experience in these areas will help you prepare for the future of data science. As a graduate of this programme, you'll be able to:

• Illustrate a comprehensive understanding of key statistical methods and their practical application.

• Demonstrate thorough knowledge in various specialised topics within statistics such as Baysian modelling, Monte Carlo estimation and dimension reduction.

• Select and apply tools and techniques for using statistical methods on context.

• Acquire transferable skills and the ability to work independently through the completion of a practical data analysis project.

• Build proficiency in key programming languages and techniques for data analysis.

• Develop effective analysis strategies for traditional “simple random sample” and a “big data” (population) datasets differ.

• Analyse large datasets (including ones with more variables than observations).

• Describe issues of data ethics and governance, as well as evaluate the impact of these issues on data gathering and analysis.