You will study 180 or 185 credits in total during your Data Science and Analytics MSc. These are the modules studied in 2021/22 and will give you a flavour of the modules you are likely to study in 2022/23. All modules are subject to change.
Dissertation in Data Science and Analytics - 60 credits
This module will prepare students for data science project assignment and dissertation writing. This module brings together all the skills and knowledge that the students have gained in the MSc Data Science and Analytics taught programme.
Learning Skills through Case Studies - 15 credits
This module will develop skills which will be useful preparation for the dissertation, potential further research, and in employment. This will include presentation skills and teamwork, some of which will be developed through real-life case studies.
Data Science - 15 credits
The aim of the module is for students to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.
Optional modules include
Artificial Intelligence – 15 credits
The module introduces the field of Artificial Intelligence (AI), taking a strongly integrative and state of the art approach based on deep neural networks. In line with the use of AI in key sectors (e.g. finance, health, law), there is an emphasis on the combination of multiple input modalities – specifically, combining images, text and structured data. Students gain hands-on experience in developing AI systems to address real-world problems, providing the knowledge and skills necessary to develop an AI system as part of an MSc project.
Statistical Theory and Methods - 15 credits
This module gives an introduction to the basics of statistics, aimed at students who did not take any statistics modules during their undergraduate degree. This module is to give a general unified theory and method of estimation and hypotheses testing, and to introduce Bayesian inference and the comparison with classical inference.
Statistical Learning - 15 credits
Statistical learning is a way to rigorously identify patterns in data and to make quantitative predictions. It is how we translate data into knowledge. In this module the fundamental concepts of statistical learning are introduced and the student will learn to use several key statistical models widely employed in science and industry.
Business Analytics and Decision Science - 15 credits
This module aims to introduce students to key concepts in business analytics, with a special emphasis on common areas of application. It also explores the links between the behavioural (decision science) perspective on decision support and the management science/business analytics perspective. Students will explore and comment on the emerging role of ‘big data’ and learn to display familiarity with major areas of application of business analytics.