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Module Catalogue

IB919-15 Advanced Analytics: Models and Applications

Department
Warwick Business School
Level
Taught Postgraduate Level
Module leader
Xuan Vinh Doan
Credit value
15
Module duration
9 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry

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Introductory description

To introduce advanced analytics using different optimisation models and demonstrate them with applications ranging from healthcare, sports, social network, to asset management and fraud detection.

Module web page

Module aims

To introduce advanced analytics using different optimisation models and demonstrate them with applications ranging from healthcare, sports, social network, to asset management and fraud detection. These applications will be presented using different case studies and examples.
The module will enable students to:

  1. Appreciate the power of analytics in different application domains ranging from healthcare, sports, social network, to asset management and fraud detection.
  2. Understand how optimisation models such as (integer) linear optimisation and combinatorial optimisation can be applied in analytics.
    This module will offer students another perspective on analytics. It will be based on the book titled The Edge of Analytics.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Proposed syllabus:
W1: Introduction: overview of analytics (models vs experts, analytical trend such as recommendation/personalised system)
W2: Linear and integer (linear) optimisation: a brief introduction
W3-5: Three different analytics applications that use linear optimisation such as analytics for kidney allocation, online advertising, and analytics in asset management
W6: Combinatorial optimisation and heuristics: a brief introduction
W7-9: Three different analytics applications that use combinatorial optimisation such as optimising sports league structures, fraud detection, and network science

Learning outcomes

By the end of the module, students should be able to:

  • Demonstrate a comprehensive understanding of the importance of optimisation models in different applications of analytics
  • Demonstrate a comprehensive understanding of simple optimisation models (linear, integer, and combinatorial optimisation)
  • Demonstrate a comprehensive understanding of how analytics and optimisation can be applied in different application domains of analytics
  • Critically analyse different case studies in analytics

Indicative reading list

  1. D. Bertsimas, A. O'Hair, and W. Pulleybank, The Analytics Edge, Dynamic Ideas, 2016 .
  2. D. Bertsimas and R. Freund, Data, Models, and Decisions, Athena, 2004 .
  3. D. Bertsimas and J. Tsitsiklis, Introduction to Linear Optimization, Athena, 1997 .
  4. C. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity, Dover Publications, 1998.

Subject specific skills

Evaluate and apply optimisation techniques in different applications of analytics.

Transferable skills

Written communication skills.
Numeracy.
Problem solving and modeling skills.
Teamwork skills.

Study time

Type Required
Other activity 27 hours (36%)
Private study 49 hours (64%)
Total 76 hours

Private study description

Self study to include pre-reading for lectures

Other activity description

This module will be split as two hours face-to-face workshops and one online lecture hour per week. The lecture hour may be live, or may be prerecorded, or as asynchronous tasks with either online or face-to-face support

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group D3
Weighting Study time Eligible for self-certification
Assessment component
Analytics Project 20% 15 hours No
Reassessment component is the same
Assessment component
In-person Examination 80% 59 hours No
  • Answerbook Pink (12 page)
  • Students may use a calculator
  • Graph paper
Reassessment component is the same
Assessment group S
Weighting Study time Eligible for self-certification
Assessment component
Analytics Project 20% 15 hours No
Reassessment component is the same
Assessment component
Individual Assignment 80% 59 hours No
Reassessment component is the same
Feedback on assessment

General question-by-question feedback for the whole cohort for the exam. For the group assessment, in addition to peer assessment, written feedback would be provided at the group level

Past exam papers for IB919

Courses

This module is Optional for:

  • Year 1 of TIBS-N1N3 Postgraduate Taught Business Analytics