MSc by Research Course Requirements
First Year Requirements & Registration Information.
Core courses
The following core courses are required as part of the first year of the CDT in Data Science:
- Introduction to Research in Data Science (IRDS): 20 points, Semester 1
- Optional Taught Courses: 70 points, Semesters 1 & 2
- Please see "Course Options" below for a list of suggested courses. Most courses are 10 points each. Most of the courses that you will take will be Level 11 (MSc- level) courses. You may take a limited number of Level 9 and 10 courses (30 points maximum, although in most cases we expect less than that).
- MSc Research Thesis (Data Science): 90 points, Semester 2 & Summer
Optional taught courses
You are required to complete 70 points of taught courses. Almost all of the courses that you will want to take will be Level 11 (MSc level). You are allowed to take Level 9 and Level 10 courses (UG3 and UG4) if you need to, up to an absolute maximum of 30 points Levels 9-10 (although, it would be extremely unlikely that you would need to take so many).
You have substantial flexibility in choosing your classes. There is a breadth requirement: you must take at least one course each in (A) Machine Learning and Statistics, (B) Databases and Data Management and (C) Applications of Data Science.
If you are considering taking courses in Optimization, here are more details on the optimization courses and courses on optimization for finance.
Optional Courses: Optimization
The courses that you are most likely to want to take are:
A. Machine Learning and Statistics
- Bayesian Data Analysis
- Bayesian Theory
- Biomedical Data Science
- Combinatorial Optimization
- Fundamentals of Optimization
- Introductory Applied Machine Learning --- Course Materials
- Large Scale Optimization for Data Science
- Likelihood and Generalized Linear Models
- Machine Learning and Pattern Recognition --- Course Materials
- Modern Optimization Methods for Big Data Problems
- Nonlinear Optimization
- Probabilistic Modelling and Reasoning --- Course Materials
- Reinforcement Learning --- Course Materials
- Statistical Programming
- Statistical Regression Models
- Statistical Theory
- Stochastic Optimization
- The Analysis of Survival Data
- Data Mining and Exploration --- Course Materials
- Information Theory --- Course Materials (not offered in 2016/17)
B. Databases and Data Management
- Advanced Databases --- Course Materials
- Advanced Topics in Foundations of Databases --- Course Materials
- Applied Databases --- Course Materials
- Databases Systems --- Course Materials
- Extreme Computing --- Course Materials
C. Applications of Data Science
- Accelerated Natural Language Processing --- Course Materials
- Advanced Vision --- Course Materials
- Automatic Speech Recognition --- Course Materials
- Bioinformatics 1 --- Course Materials
- Bioinformatics 2 --- Course Materials
- Foundations of Natural Language Processing --- Course Materials
- Machine Translation --- Course Materials
- Natural Language Understanding --- Course Materials
- Neural Information Processing --- Course Materials
- Optimization Methods in Finance
- Speech Synthesis
- Social and Technological Networks --- Course Materials
- Text Technologies for Data Science --- Course Materials
- Natural Language Generation --- Course Materials (not offered in 2016/17)
Alternative options
The courses listed above are those that we think are most likely to be suitable. However, you are allowed to pick from any Level 11 courses in the university (subject to prerequisites, resourcing constraints, etc.), and if you have interdisciplinary interests, you may choose to take courses outside of Informatics and Maths. However, if you intend to take a course that is not included in the list above, you should discuss this with your Personal Tutor prior to registration to make sure that your choices make sense.
The School's sortable course list gives a full list of the courses delivered in the School of Informatics (Note: this does not include couses in the School of Mathematics):
Students with a previous MSc
If you have done previous MSc level work in data science, you might find that your previous work overlaps with the classes here. In this case, you can consider an alternative option in which: (A) you take only 30 points of Level 11 courses instead of 60 points and (B) you take a correspondingly larger MSc project, i.e. starting in October. If you are considering this option, you must speak to your Personal Tutor by the end of Week 1.
Course registration
The first year of the CDT in Data Science is governed by the Degree Programme Table (DPT). These pages guide students through the range of possible course choices, helping ensure that you graduate with a set of courses that are relevant to your chosen research field. The following external links provide additional information above course registration: