2021-2022 / INFO2048-1

Business Analytics

Duration

30h Th

Number of credits

 Master in business engineering (120 ECTS)5 crédits 
 Master in business engineering (120 ECTS) (Digital Business)5 crédits 
 Master in business engineering (120 ECTS) (Industrial Business Engineering)5 crédits 

Lecturer

Michael Schyns

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

No company can survive without a good management information system.  Nowadays, a company must be able to collect, analyze and handle huge volume of data in order to answer managerial questions and/or offer new top value services.  Amazon, Google, Facebook  are obvious successful stories confirming the importance of data management. 
Three keywords to define the course:

  • Management field: Decision Making
  • Approach: Data analysis (Analytics)
  • Theory (basics) and applications
In this course, the main question is:  How to transform raw "stupid" data into valuable "actionable" information?  Common applications in finance are fraud detection, credit risk analysis, risk profile, pricing...  Common applications in eCommerce are recommendation tools, converting clicks into customers...  Other common applications in Supply Chain Management are revenue management (e.g. airline ticketing), capacity management, diagnosis of production faults, forecasting demand, predictive modeling, advanced reporting based on ERP systems...  Common applications in marketing and sales are the analysis of customer loyalty, the analysis of the market basket, the identification of prospective customers)...
This course covers techniques such as:
  • advanced reporting with Excel (DB functions, filters, pivot table...)
  • reporting with advanced visual tools like "Tableau" or Power BI
  • some of these data mining tools: - data preparation - decision trees - neural networks - linear and logistic regression - market basket analysis  - clustering - model analysis

Learning outcomes of the learning unit

  • Gaining the knowledge and understanding of the chosen concentration field.
  • Understanding and being able of using modelization methods.
  • Capacity to research autonomously and methodically the information needed to solve a complex, transversal management problem.
  • Integrate autonomously researched information, tools, knowledge and context to build and propose original, creative and viable solutions to concrete complex management problems whether real or simulated.
  • Providing concrete solutions to a management problem, integrating modelization methods and/or a dimension of technology, innovation or production.
  • Developing a critical sense (arguing).
  • Professional capacity for written communication.

Prerequisite knowledge and skills

Computer skills Programming language skills Statistical knowledge

Planned learning activities and teaching methods

Theory is immediately illustrated by small examples.  Students perform exercises on computer during the lectures.  Real cases are considered (e.g. revenue management, bankruptcy detection, customer choice...)
They will acquire a basic theoretical knowledge but also learn to use professional tools like Excel for basic reporting, Tableau or Power BI for  advanced visual reporting and advanced tools for machine learning.  A programming language (probably python) will be used for the machine learning part.
The students have also to complete an in-depth visual analysis on a provided dataset as a group project.  
The project and the preparations are compulsory.  A student will not be allowed to sit the exam (failure mark) while he has not completed all his/her assignments

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course


Additional information:

All lectures are given in a computer room. The lectures are recorded (if no technical problem)

Recommended or required readings

Required: 
Slides on Lol@
Recommended:
Introduction to Data Mining, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson Inertnational.

Assessment methods and criteria

Exam(s) in session

Any session

- In-person

written exam

Written work / report


Additional information:

Final mark:
Project (with oral defense): 2/3 Written exam : 1/3
! Peer evaluation taken into account for the projects Group project but each student must work on the project and be able to explain/modify every part.

Work placement(s)

Organizational remarks

The examinations are organized twice a year during the official sessions.  All the students sit the exam in Liège.  Due to the nature of this course, we have never accepted to organize any additional exam at a different date and/or at a different location (even for students from abroad).

Contacts

M. Schyns, HEC-Liege, N1 M.Schyns@uliege.be