| Unit name | Data Science for Finance |
|---|---|
| Unit code | ACFIM0054 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Shin |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Accounting and Finance - Business School |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
Data Science for Finance bridges the gap between theory and practice, equipping you with the knowledge and tools that are needed to solve real-world problems that are faced by investment banks, fund management companies and other financial institutions. The unit introduces you to the general principles of financial modelling, the use of financial data, as well as methodological tools that are useful for implementing a financial model, such as matrix operations, numerical optimisation, sensitivity analysis and simulation. These methods are applied to a wide range of applications in finance drawn from investment analysis, corporate finance, fixed income analysis, risk management and financial econometrics.
How does this unit fit into your programme of study?
The Finance pathway in the programme MSc Data Science for Business provides you with the analytical tools to implement theories and concepts that are covered in finance-relevant decision-making in a real-world setting through the extensive use of practical case studies. It directly aligns with the programme’s aim of equipping you with the skills to apply data science techniques in solving complex business and financial problems. This unit provides you with hands-on experience in building and applying financial models, using real-world case studies to bridge the gap between theoretical knowledge and practical application in the finance industry. This unit complements core units such as Statistical Computing, Empirical Methods, and Software Development for Data Science, reinforcing technical skills while specializing in finance. It also aligns with optional units like Financial Technology and Entrepreneurial Finance, extending concepts to advanced financial models and methodologies commonly used in the industry. By offering you exposure to practical financial challenges, the unit prepares you to excel in data-intensive finance roles, contributing to the overarching goal of developing industry-ready data science professionals.
An overview of content
The unit covers both the general principles of financial modelling and the specific tools that prove to be useful to implement a financial model in practice. These techniques are illustrated with a wide range of applications in finance drawn from investment analysis, corporate finance, fixed income analysis, risk management and financial econometrics, including the estimation of the required returns on debt and equity, construction of optimal passive and active investment portfolios, forecasting the volatility of financial asset returns and the estimation of portfolio risk. The emphasis of the unit is on practical application of the theory, with lectures on each topic followed by in-depth practical classes in which you work through comprehensive realistic case studies using Excel, VBA and, optionally, Python.
How will students, personally, be different as a result of the unit
The unit develops in you the ability to solve some of the practical challenges that you are likely to encounter in a career in the finance industry. In so doing, you will acquire not only an understanding of the principles and practice of data science in financial modelling but also valuable transferable analytical skills such as the ability to analyse relevant data and write a professional report, as well as proficiency in Excel, VBA and Python.
Learning Outcomes
By the end of the unit, you should be able to:
1. Design and implement a financial model to address a specific problem in finance using appropriate data and modelling techniques.
2. To critically assess the robustness of a financial model with respect to sources of uncertainty, and hence establish the reliability and usefulness of the conclusions drawn.
3. Present the results and conclusions of a financial model in a clear and precise way.
Teaching will be delivered through a combination of lectures, exercise lectures and online clinics. Lectures will cover the principles and methods of financial modelling, illustrated with extensive practical examples. In the exercise lectures, you will work through practical case studies that allow you to implement, under guidance, what you have learned in the lectures. The online clinics give you the opportunity to raise and discuss questions related to any aspect of the unit, including the lecture material, practical case studies and the formative and summative assessment.
Tasks which help you learn and prepare you for summative tasks (formative):
You will undertake a group assignment of similar form to that of the individual summative assessment (see below), with written feedback provided on the content and presentation of the submitted report.
Tasks which count towards your unit mark (summative):
The unit is assessed by a single individual assignment (2,500 words) in which you will use the financial modelling techniques that you have learned to solve a comprehensive practical case study and write up the details of the financial model together with your results and conclusions into a professionally presented report. The assignment tests ILO1, ILO2 and ILO3.
When assessment does not go to plan
The re-assessment will take the same form as the original assessment and create a new piece of work.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ACFIM0054).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
