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

Email: m.sethi@icmacentre.ac.uk

Type of module:

Summary module description:

In this module you will learn howÌýbig dataÌýtechniques can be used to solve problems in finance. We will firstÌýexploreÌýissues related to the collection, organisation and visualisation of large sets of structuredÌýand unstructured data.ÌýWe 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).Ìý

Aims:

The module focuses on (1)Ìýissues facing big data handlingÌý(2) retrieval, organisation and cleaning of structured and unstructured dataÌý(3)ÌýaÌýhigh level description ofÌýa system for theÌýdistributed storage and processing of big data (Hadoop)Ìý(4)Ìýcloud computing with a focus on the Google Cloud PlatformÌý(5) finance applications.Ìý

Assessable learning outcomes:

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

• Understand howÌýtheÌýbig dataÌýrevolution isÌýchangingÌýour lives and creating businessÌýopportunities;Ìý


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


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


• Understand th eÌýadvantages and disadvantages of using a cloud computing platform;Ìý


• Understand how big dataÌýtechniquesÌýcan be used to solveÌýold and new problems inÌýfinanceÌýÌý

Additional outcomes:

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

Outline content:

1. Big data – a global multi-sector viewÌý


2. Structured and unstructured data collection, organisation, storageÌýand cleaningÌý


3. Visualisation of datasetsÌýÌý


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


5. Cloud computing platformsÌý


6. Big data case studiesÌý

Global context:

The module covers industry standardÌýbig dataÌýtechniques. The concepts are applied in investment banks, central banks, hedge funds and asset management firms worldwide.Ìý

Brief description of teaching and learning methods:

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%)Ìýin week 11 of the spring term.Ìý

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 individualÌýreportÌý

Additional Costs (specified where applicable):

Last updated: 4 April 2020