Module Catalogues, Xi'an Jiaotong-Liverpool University   
 
Module Code: MAN432
Module Title: Big Data-Applications in Business
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM2
Originating Department: International Business School Suzhou
Pre-requisites: N/A
   
Aims
This module provides students with a thorough understanding of the applications and research of big data in the areas of finance, marketing and management. Students will learn to critically apply data mining and other relevant analytical techniques with IBM SPSS modeler.
Learning outcomes 
A. Demonstrate in-depth understanding and critical evaluation of big data analytical techniques.

B. critically apply data analysis techniques to solve business problems in the fields of finance, marketing and management.

C. demonstrate a critical ability to present and report complex analytical results to non-experts in written form.

D. demonstrate a critical ability to present and communicate complex statistical results in front of an audience of non-experts in spoken form.


Method of teaching and learning 
The module primarily involves instructor-led lectures, supported by occasional guest lectures and company visits. During the course of the module students will be expected to choose their project focus, i.e., big data applications in finance, marketing or management. Depending on their chosen project topic, students will gain an in-depth understanding of data analysis techniques in one or more of the following areas: (i) Predictive analytics for finance fundamentals (regression and classification, principal components analysis and dimension reduction, time series); (ii) Predictive analytics for finance applications (asset pricing, institution risk management and credit risk management); (iii) Marketing analytics (identify patterns and relationships with primary and secondary data); (iv) Marketing analytics (social media analytics, collecting, analyzing and deriving insights from social media chatter, techniques of sentiment analysis and text mining), and (v) Management analytics (operations and supply chain analytics, sourcing, inventory management, manufacturing, quality, sales and logistics).
Syllabus 
• Introduction to big data analytics and its applications in finance, marketing and management fields.

• Review of data mining techniques.

• Finance: time series analysis.

• Finance: credit risk management.

• Marketing: behavioural patterns and relationships identification.

• Marketing: social media analytics and text mining.

• Management: operations management.

• Management: supply chain management.

Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 32.5          117.5  150 

Assessment

Sequence Method % of Final Mark
1 Individual Report 70.00
2 Individual Presentation 30.00

Module Catalogue generated from SITS CUT-OFF: 6/6/2020 9:20:33 PM