Data Science MSc
An overview of the degree and advice on selecting your courses.
Overview of the degree
Data science is the study of the computational principles, methods and systems for extracting and structuring knowledge from data. In this degree, you will take courses from several areas that focus on different parts of this problem from both a theoretical and applied perspective: machine learning focuses on finding patterns and making predictions from data; ideas from algorithms and databases are required to build systems that scale to big data streams; and separate research areas have grown around different types of unstructured data such as text, images, sensor data, video and speech.
Full-time students on this degree must be registered for exactly 180 credit points at all times. Part-time students have the same course requirements, but spread out over two or three years.
The following courses are compulsory:
- Informatics Research Review (IRR) — 10 credits of coursework in Semester 1.
- Informatics Project Proposal (IPP) — 10 credits of coursework in Semester 2.
- MSc Dissertation — 60 credits in the Summer.
In addition, you must register for another 100 credits of optional taught courses, usually split evenly across two semesters. At least 80 credits must be chosen from one of three areas of Data Science, and you must take at least 10 credits in each area:
- Machine learning, Statistics, and Optimization
- Databases and Data Management Systems
- Unstructured Data and Applications
This degree offers considerable flexibility in your taught course choices. Specific course options and requirements are listed in your Degree Programme Table, and are built into the Path programme builder, which you can use to help you put together a set of courses that works for you.
Course topics and advice on choosing courses
Course topics are groups of related courses to help you navigate our course options. The topics relevant to this degree are described below.
Please consider the prerequisites for each course before signing up. Not all students will be prepared for all available courses, but you should be able to find a selection that works for you. Courses in some topics tend to require more mathematics, while others may require more programming or other skills. The individual topic pages often provide some general guidance about this, but check the course descriptors if you're unsure.
Before finalizing your other courses, please check the advice on the Programming Courses topic page to see whether you would benefit from taking one of these courses to improve your programming skills. These courses do not count as one of your Data Science courses and must be taken using some of your 20 non-Data Science credits.
Machine Learning, Statistics, and Optimization course topics
Most of the Machine Learning courses available on this degree are taught in the School of Informatics and information about them can be found on the Machine Learning topic page below.
Most of the Statistics and Optimization courses available on this degree are taught in the School of Mathematics. Further information about them can be found in the individual course descriptors, linked from Path.
Databases and Data Management course topics
The courses in this part of your Degree Programme Table are described on the following page.
Unstructured Data and Applications course topics
Most of the courses in this part of your Degree Programme Table fall into one of the three topics below. Note that not all courses in each topic are available on this degree, so check your DPT or Path for availability. For options oustide these topics, such as Data Visualization, see the course descriptor for details.
You may use some of your credits to choose from other courses in Informatics or other Schools. Most courses taken by our students (including some popular outside courses) are listed in the Topic pages. The complete list of courses offered by the School is in the Sortable Course List.
Full list of Course Topics (information about most MSc courses, according to topic)
Informatics Sortable Course List (a compact list of all courses offered this year, sortable by semester, level, credits, etc.)