2024-2025 / INFO2048-1

Business Analytics

Duration

30h Th

Number of credits

 Master in management engineering, professional focus in digital business5 crédits 
 Master in business engineering, professional focus in Financial Engineering5 crédits 
 Master in business engineering, professional focus in Financial Engineering (Digital Business)5 crédits 
 Master in business engineering, professional focus in Intrapreneurship and Management of Innovation Projects5 crédits 
 Master in management engineering, professional focus in management and technologies (Industrial Business Engineering)5 crédits 
 Master in management engineering, professional focus in sustainable performance management5 crédits 
 Master in business engineering, professional focus in Supply Chain Management and Business Analytics5 crédits 
 Master in business engineering, professional focus in Supply Chain Management and Business Analytics (Digital Business)5 crédits 
 Master in business engineering, professional focus in science and technology5 crédits 
 Master in mathematics, research focus5 crédits 
 Master in mathematics, teaching focus5 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 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.

Attendance to the lectures is compulsory to be allowed to ask questions.

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

Face-to-face course


Further information:

The students who want to participate need a computer.

Course materials and recommended or required readings

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

Exam(s) in session

Any session

- In-person

written exam

Written work / report


Additional information:

Final mark:

Project on PBI: 20%
Written exam on Excel and Machine Learning : 80%

! 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)

Organisational remarks and main changes to the course

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).

All the Erasmus and Exchange students must contact the professor before registration to the course.

Contacts

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

Association of one or more MOOCs