PYTHON PROGRAMMING IN FINTECH - 2021/2
Module code: MANM491
The aim of this course is to provide students with an opportunity to learn programming skills that are valuable in a Financial Technology (FinTech) environment. To understand how technology is being used in financial services and regulation, we will apply a variety of tools to analyze real world examples and data. We will be using Python. Programming knowledge is not a prerequisite but a desire to acquire that skill is a prerequisite for the course.
Surrey Business School
WANG Shuhui (SBS)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 95
Lecture Hours: 22
Laboratory Hours: 20
Guided Learning: 2
Captured Content: 11
Prerequisites / Co-requisites
Indicative content includes:
- Traditional statistical techniques (eg regression)
- The fundamentals of Python and the key libraries relevant to financial data analysis:
- Python data structures (NumPy)
- Data analysis (Pandas)
- Data visualisation (Matplotlib)
- Python scientific manipulation (SciPy)
- Applying numerical analysis and programming in Python to solve a range of finance and policy problems (eg what affects the valuation of FinTech products/ FinTech firms)
|Assessment type||Unit of assessment||Weighting|
|Coursework||Individual programming problem set 1||25|
|Coursework||Individual programming problem set 2||25|
|Coursework||Individual programming problem set 3||25|
|Coursework||Individual programming problem set 4||25|
The assessment strategy is designed to provide students with the opportunity to demonstrate Python programming skills as applied to FinTech problems.
Thus, the summative assessment for this module consists of four individual programming problem sets, each of which will constitute 25% of the module assessment.
Formative assessment and feedback
Students will receive formative assessment/feedback in the following ways:
- By means of unassessed problem sheets
- During supervised computer laboratory sessions
- Via assessed coursework
- Provide students with an understanding of how technology is being used in financial services and regulation.
- Enable students to apply Python programming skills to real world examples and data.
- Expose students to the challenges of programmatic data analysis in financial services and regulation
|001||Critically apply a structured approach to problem solving using Python||CKPT|
|002||Write syntactically correct Python code||CKP|
|003||Understand the basic concepts of Python||CK|
|004||Understand the importance of constructing maintainable code by using good design and code conventions||CP|
|005||Analyze typical financial data using Python||CKPT|
|006||Understand the basic statistical techniques relevant to finance||CKPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy follows a mix of didactic and problem-based learning in a practical(computer laboratory) environment.
The learning and teaching methods would typically comprise of 11 weeks of 3-hour computer laboratories, supplemented by guided learning outside of classroom.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MANM491
Programmes this module appears in
|FinTech and Policy MSc||1||Optional||A weighted aggregate mark of 50% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2021/2 academic year.