ICM323-Big Data in Finance

Module Provider: ICMA Centre
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Mr Mininder Sethi

Email: m.sethi@icmacentre.ac.uk

Type of module:

Summary module description:

In this module you will learn how big data techniques can be used to solve problems in finance. We will first explore issues related to the collection, organisation and visualisation of large sets of structured and unstructured data. We will then look at methods for storage and computation of big data sets by distributed computing (Hadoop). The module will also explore the use of cloud computing platforms with a focus on the Google Cloud Platform (GCP). 


Aims:

The module focuses on (1) issues facing big data handling (2) retrieval, organisation and cleaning of structured and unstructured data (3) a high level description of a system for the distributed storage and processing of big data (Hadoop) (4) cloud computing with a focus on the Google Cloud Platform (5) finance applications. 


Assessable learning outcomes:

By the end of the module it is expected that students will: 




  • Understand how the big data revolution is changing our lives and creating business opportunities; 

  • Understand the basic techniques for the collection and cleaning of large structured and unstructured data; 

  • Be familiar with the main issues in distributed storage and processing of big data; 

  • Understand th e advantages and disadvantages of using a cloud computing platform; 

  • Understand how big data techniques can be used to solve old and new problems in finance  


Additional outcomes:

The module will provide an overview of the Google Cloud Platform and how it can be used to solve real problems in finance. 


Outline content:


  1. Big data – a global multi-sector view 

  2. Structured and unstructured data collection, organisation, storage and cleaning 

  3. Visualisation of datasets  

  4. Distributed storage and processing of big data (Hadoop) 

  5. Cloud computing platforms 

  6. Big data case studies 


Global context:

The module covers industry standard big data techniques. The concepts are applied in investment banks, central banks, hedge funds and asset management firms worldwide. 


Brief description of teaching and learning methods:

Contact hours:
  Autumn Spring Summer
Lectures 10
Seminars 5
Guided independent study:      
    Wider reading (independent) 25
    Wider reading (directed) 10
    Preparation for seminars 10
    Revision and preparation 15
    Essay preparation 15
    Reflection 10
       
Total hours by term 0 0
       
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Report 40
Class test administered by School 60

Summative assessment- Examinations:

Students will be asked to complete a report (40%) in week 2 of the summer term and in class multiple choice tests (60%) in week 11 of the spring term. 


Summative assessment- Coursework and in-class tests:

Formative assessment methods:

Penalties for late submission:

Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx


Assessment requirements for a pass:

50% weighted average mark


Reassessment arrangements:

Re assessment of individual report 


Additional Costs (specified where applicable):

Last updated: 4 April 2020

THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.

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