MSc Operational Research with Data Science
Why Operational Research with Data Science?
With the explosion of data available to analyse a vast range of activities, there is a growing demand for novel techniques and the ability to handle ever larger data sets. Many existing techniques lie naturally within areas of computational optimization and operational research.
The Operational Research with Data Science programme gives an opportunity to study these areas via the fundamentals of optimization and operational research and a range of optional courses in optimization, statistics, and data science.
The School's Optimization and OR group has worked on data science applications for many years and this is expected to develop with the School's role in the Alan Turing Institute and the increasingly data-driven world at large. Students of the OR with Data Science degree will benefit from being part of this rich environment.
Structure and course options for the Operational Research with Data Science MSc programme
You will take 120 credits of courses in total during Semesters 1 and 2, followed by a 60 credit dissertation which you complete over the summer. The courses you take will be dependent on the availability of courses each year which may be subject to change as the curriculum develops to reflect a modern degree programme.
Compulsory courses cover the core skills of operational research, with all compulsory courses being studied in Semester 1. All courses are worth 10 credits, unless otherwise indicated.
Semester 1 compulsory courses have previously included:
- Fundamentals of Operational Research
- Fundamentals of Optimization
- Methodology, Modelling and Consulting Skills
For your MSc to have a specialization in Data Science, you must also study between 30-40 credits of the following Semester 1 courses:
- Statistical Methodology
- Incomplete Data Analysis
- Introduction to Practical Programming with Objects
- Statistical Programming
- Python Programming
- Stochastic Modelling
- Machine Learning and Pattern Recognition (20 credits)
- Introductory Applied Machine Learning (20 credits)
You will have the opportunity to tailor your degree by selecting from a broad range of optional courses. All courses are worth 10 credits, unless otherwise indicated.
Semester 2 optional courses have previously included:
- Machine Learning in Python
- Algorithmic Game Theory and its Applications
- Time Series
- Large Scale Optimization for Data Science
- Biomedical Data Science
- Generalised Regression Models
- Integer and Combinatorial Optimization
- Credit Scoring
- Optimization Methods in Finance
- Topics in Applied Operational Research
- Operational Research in the Energy Industry
- Data Mining and Exploration
- Reinforcement Learning
- Computational Cognitive Neuroscience
The project gives you the opportunity to apply skills developed earlier to real operational research problems. Projects often take the form of a consultancy exercise for a sponsoring organisation. Projects usually involve modelling the problem and applying existing packages and/or developing a computer program for a new application of operational research. It is also possible to have an academic project without a direct link to an external organisation.
Academic projects are defined and supervised inside the department. External projects are defined by some external organisation, which may be an industrial or commercial company, a government body or research lab. There will be one supervisor in the University and one in the outside organisation. In an external organisation, you may work as part of a team on a project, but the work you do must be sufficiently self-contained that it can be written up into a coherent dissertation.
The purpose of the external placement is to enhance your experience through working on a practical project, and to allow you to apply and extend the knowledge and skills that you have already developed as part of your MSc programme. You will also develop your communication and other transferable skills, and interact with others on a more open-ended problem than you will have experienced in the taught part of the MSc.