College of Business
Students at the College of Business are required to gain a minimum of 30 credits from attending taught modules, which are chosen in consultation with their Supervisor and their Research Studies Panel (which is a panel of two to four doctoral studies advisers that support and advise the student and the Supervisor(s) throughout the doctoral programme).
In addition, Research Integrity Training is a compulsory element of all PhD programmes from 2019.
There are three module sets:
- Set 1 includes modules in research methods, which provide training in methodological issues and philosophy of the social sciences.
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Set 2 includes Academic/Subject-specific modules, which serve to broaden and deepen knowledge of one's discipline, informing the development of the theoretical framework for doctoral research.
Note: Students are required to secure permission to attend Academic/Subject specific modules from their Supervisor and the Module Coordinator or Programme Manager of the module prior to registration.
- Set 3 includes Transferable skills modules, sometimes referred to as generic or soft skills, provide training in the types of skills necessary for professional development. These skills should enable PhD students to complete their doctoral research on time and enhance their professional attractiveness for potential employers.
PhD students, in consultation with their Supervisor and Research Studies Panel, can choose any module across the Business School and/or university, which will be useful in advancing their research capability.
Research modules include but are not limited to:
- (opens in a new window)BMGT50040 - Research Design and Measurement
- (opens in a new window)GSBL50050 - Approaches and Techniques in Qualitative Research
- POL50220 - Social Science Methodology
- SCI50020 - Research Integrity Online
- UTL40230 - Intro to Univ T&L for Tutors
Academic/Subject-specific modules include but are not limited to:
- COMP30870 - Graph Algorithms
- COMP47470 - Big Data Programming
- COMP47590 - Advanced Machine Learning
- COMP47750 - Machine Learning with Python
- ECON41820 - Econometrics
- ECON42630 - Decision Theory
- ECON42710 - Advanced Econometrics: Time Series
- ECON50570 - PhD Microeconomics 1
- ECON50710 - PhD Econometrics 1
- ECON50580 - PhD Econometrics 2
- ECON 50740 - PhD Macroeconomics 1
- EEEN40080 - Power System Operation
- EEEN40580 - Optimisation Techniques for Engineers
- ENVP50030 - Behavioural Public Policy
- FIN40430 - Strategic Finance
- FIN41360 - Portfolio & Risk Mgt
- FIN41660 - Financial Econometrics
- FIN42100 - Machine Learning for Finance
- FIN50040 - Portfolio Theory & Asset Pricing
- MATH40690 - Stochastic Calculus
- MATH40740 - Advanced Financial Models
- MIS41160 - Optimisation in Business
- NMHS43800 - Introduction to Systematic Literature Reviews
- STAT40800 - Data Prog with Python (online)
- STAT40860 - Time Series (online)