Financial Data Analytics
(MSc) Financial Data Analytics
Entry for 2023
FHEQ level
This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.
Course Aims
The aim of this course is to combine mathematics, finance and data analytics to teach students how to solve real-world problems with an emphasis on financial and data science sector. All core financial modules showcase how mathematical tools solve quantitative problems in the financial industry. The data science modules teach master level techniques of data science and machine learning, which are currently driving innovation in a wide variety of businesses. Combining those two fields provides a unique opportunity to develop a cohort of highly trained professionals in both finance and data science.
The course will provide students with tools and techniques for analysis of financial data, financial decision making, analytic thinking and systematic reasoning. It is fully in line with the School of Mathematical and Physical Sciences’ long-term strategy to provide high-quality teaching and increase student numbers in areas that of high importance and demand, and will, as such, allow for an enhanced student mobility.
The course has been designed to align it with the current needs of potential employers. The employment opportunities for graduates with Data Science and Finance degrees in the UK and worldwide are excellent, and demand for such graduates is high and increasing.
The curriculum design allows students to study under the supervision, independently, in groups, as well as to demonstrate transferrable skills through their Dissertation project. The course is in agreement with the generic QAA Benchmark Statement (Masters).
The structure of the course is such that in the first semester the students are taught elements of Financial Mathematics, programming and data analysis methods in core modules and are offered a range of optional modules in statistics and data analysis. The second term comprises of core modules teaching students elements of financial portfolio analysis, analysis of various financial models with applications in finance and the wider industry, and machine learning. The number of optional modules alllows students to choose between statistics-oriented modules and financial modules. The options in the course structure enable the students to choose their own area of expertise in Finance, Data Analytics, Programming or Statistics.
The course has an integrated career session jointly with the Career and Employability Centre and will signpost Sussex services such as CV360. Students will be advised about virtual internship programmes such as Riipen and Forage, which will raise business awareness and guide them in the strategic choice of their dissertation topic.
Course learning outcomes
Apply a comprehensive understanding of leading principles of quantitative finance and their mathematical underpinnings to a range of problems in financial analytics
Systematically understand and apply foundations of data science and machine learning to analyse financial data
Apply critical thinking and systematic reasoning in understanding financial trends
Apply advanced research skills to a wide range of real-world financial and business applications
Independently apply analytical and numerical methods to solve problems in financial data analysis
Demonstrate proficiency in computer programming in order to understand and analyse large data sets
Synthesise and apply mathematical and data science techniques to analyse large financial datasets
Critically review literature, identify knowledge-furthering opportunities and innovative ways of formulating relevant questions, building arguments and summarising findings in written reports
Full-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Computing for Data Analytics and Finance (L7) (854G1) | 15 | 7 |
| Core | Data Science Research Methods (970G1) | 15 | 7 | ||
| Core | Financial Mathematics (L.7) (G5078) | 15 | 7 | ||
| Option | Algorithmic Data Science (969G5) | 15 | 7 | ||
| Data Analysis Techniques (890F3) | 15 | 7 | |||
| Linear Statistical Models (L7) (971G1) | 15 | 7 | |||
| Probability Models (L7) (973G1) | 15 | 7 | |||
| Spring Semester | Core | Financial Portfolio Analysis (849G1) | 15 | 7 | |
| Core | Machine Learning (934G5) | 15 | 7 | ||
| Core | Mathematical Models in Finance and Industry (832G1) | 15 | 7 | ||
| Option | Financial Invest & Corp Risk Analysis (861G1) | 15 | 7 | ||
| Monte Carlo Simulations (L7) (865G1) | 15 | 7 | |||
| Numerical Solution of Partial Differential Equations (L.7) (845G1) | 15 | 7 | |||
| Random processes (L.7) (862G1) | 15 | 7 | |||
| Statistical Inference (L.7) (867G1) | 15 | 7 |
Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.
The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.