Data Science course details
For those wanting some more detailed information about the structure of the Data Science degree course, this is the place to find it!
First year - Second year - Third Year - Administration
The first year of the course provides the background knowledge and fundamental skills required to develop expertise in Data Science. Students cover topics in university-level mathematics and statistics, such as formal proof, mathematical reasoning and probability theory, and also take a range of modules in computer programming, algorithms and the design of information structures. In particular, students encounter programming in both Java and R. The core first-year modules (126 CATS) are:
CS118 | Programming for Computer Scientists | 15 CATS |
CS126 | Design of Information Structures | 15 CATS |
IB104 | Mathematical Programming I | 12 CATS |
MA106 | Linear Algebra | 12 CATS |
MA137 | Mathematical Analysis | 24 CATS |
MA138 | Sets and Numbers | 12 CATS |
ST104 | Statistical Laboratory | 12 CATS |
ST115 | Introduction to Probability | 12 CATS |
ST116 | Mathematical Techniques | 12 CATS |
In the second year, students further integrate the skills gained in their first year, developing their expertise in statistical inference, algorithm design and software engineering, whilst also building their communication and team working skills – thereby allowing them to operate effectively as data scientists. Core modules from Statistics and Computer Science account for 93 CATS, with students selecting at least 27 CATS of optional modules. A group project gives students practical experience of industrial software engineering. The core second-year modules are:
CS258 | Database Systems | 15 CATS |
CS260 | Algorithms | 15 CATS |
CS261 | Software Engineering | 15 CATS |
ST202 | Stochastic Processes | 12 CATS |
ST208 | Mathematical Methods | 12 CATS |
ST218 | Mathematical Statistics A | 12 CATS |
ST219 | Mathematical Statistics B | 12 CATS |
A non-exhaustive list of optional modules is:
CS249 | Digital Communications and Signal Processing | 15 CATS |
CS255 | Artificial Intelligence | 15 CATS |
IB207 | Mathematical Programming II | 12 CATS |
ST221 | Linear Statistical Modelling | 12 CATS |
(ST = Statistics, CS = Computer Science, IB = Warwick Business School)
In their final year of study, students focus on applying the skills gained in a variety of problem domains, including in an individual project involving an extended analysis of a substantial data set (worth 30 CATS). They also develop a greater understanding of cutting-edge topics and issues in Data Science. In particular, students take at least 90 CATS of specialist optional modules. At least 60 CATS of these optional modules must come from Option List A, with at least 30 CATS from each of Computer Science and Statistics. The modules in Option List A are:
CS301 | Complexity of Algorithms | 15 CATS |
CS331 | Neural Computing |
15 CATS |
CS341 | Advanced Topics in Algorithms | 15 CATS |
CS342 | Machine Learning | 15 CATS |
CS346 | Advanced Databases | 15 CATS |
ST301 | Bayesian Decision Theory | 15 CATS |
ST323 | Multivariate Statistics | 15 CATS |
ST333 | Applied Stochastic Processes | 15 CATS |
ST337 | Bayesian Forecasting | 15 CATS |
ST340 | Programming for Data Science | 15 CATS |
ST343 | Topics in Data Science | 15 CATS |
(ST = Statistics, CS = Computer Science)
The course is administered through the Statistics department which has a long track record of running the successful interdisciplinary MORSE degree. Students are assigned personal tutors from within the Statistics department. The new course is represented on both the Statistics and Computer Science departmental Student-Staff Liaison Committees.