PYTHON PROGRAMMING IN FINTECH - 2022/3

Module code: MANM491

Module Overview

This course introduces modern programming concepts and practice for students with little or no background in computing using the computer language Python.


The course will start with a presentation of basic programming concepts, including data types and structures as they exist in Python. Loops and conditional statements will then be introduced, as well as custom functions, along with a wider discussion of structured programming and ways to reuse code.


Students will then consider practical applications of programming. They will learn to work with data input and output in different formats, use suitable libraries for scientific computing and data analysis, and create plots and visualizations to display results.


Throughout the course, students will engage with professional programming practices and tools (test-driven development, version control, code reviewing, debugging), and will have the opportunity to collaborate with peers to develop their skills.

Module provider

Surrey Business School

Module Leader

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: 11

Laboratory Hours: 22

Guided Learning: 11

Captured Content: 11

Module Availability

Semester 1

Prerequisites / Co-requisites

None.

Module content

Introduction to Python:


  • Variables, Loops

  • Operators, Data type

  • list, tuple, dictionary

  • Function

  • Data structures



Object Oriented programming (OOP) in Python:


  • Introduce the OOP and understand class, attribute, objects and method

  • Features of OOP: Encapsulation; Inheritance; Polymorphism



Matplotlib, Pandas and Numpy in Python :


  • Various application methods: plot the figures

  • Understand the Pandas: powerful data structure

  • Introduce the Numpy: another data structure



Web crawling data: read data from a Web source

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test Individual programming problem set 1 25
School-timetabled exam/test Individual programming problem set 2 25
Examination Individual programming problem set 3 50

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 the conceptual foundations for understanding, analysing and interpreting python programming language
  • Enable students to apply Python programming to the business world
  • Enable students to have ability to develop their own coding

Learning outcomes

Attributes Developed
001 Apply Python programming skills to real world examples and data. KCP
002 Create various programmatic data analysis in financial services and regulation KCPT
003 Apply the python programming skills in order to develop their own codings KCP
004 Evaluate different codings and have the ability to distinguish the optimized methodologies KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to:


The teaching and learning methods include the use of weekly lecture and labs to illustrate the theory and allow the student to practice the practical application of such theory with a range of weekly lab questions.  Lecture material will be supported by directed reading and weekly homework exercises will be set to test students’ understanding on an on-going basis.  Surrey – Learn will be used as an information portal and will contain lecture notes, practical exercises and model answers plus past exam papers and model answers.


The learning and teaching methods include:


  • Lectures 

  • Lab sessions

  • Captured contents

  • Guided learning


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 2022/3 academic year.