PYTHON PROGRAMMING IN FINTECH - 2021/2

Module code: MANM491

Module Overview

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.

Module provider

Surrey Business School

Module Leader

LINDSAY Ira (Schl of Law)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

JACs code:

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

None.

Module content

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 pattern

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

Alternative Assessment

None

Assessment Strategy

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


Module aims

  • 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

Learning outcomes

Attributes Developed
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

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Overall student workload

Independent Study Hours: 117

Laboratory Hours: 33

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.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM491

Other information

N/A

Programmes this module appears in

Programme Semester Classification Qualifying conditions
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.