Duration
30h Th
Number of credits
| Master in management (120 ECTS) | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Did you know that... at the beginning of 2020, the digital universe was estimated to consist of 44 zettabytes of data. The main question is: How to transform raw "stupid" data into valuable "actionable" information to help decision making? The answer is : « By using Marketing Analytics techniques ! »
Marketing analytics involves the creation and use of quantitative data to derive consumer insights and make decisions. It is often heralded as a critical resource necessary for effective marketing.
Common applications in marketing and sales are the analysis of customer loyalty, the analysis of the market basket, the identification of prospective customers.. Common applications in eCommerce are recommendation tools, converting clicks into customers...
In this course, you will learn :
Good practices in data analysis
Descriptive analytics tools:
- Advanced reporting and visualization tools,
- Association analysis
- Clustering/Segmentation
- kNN
- Regression (linear and logistic) and Artificial Neural Networks
- Decision trees and Random forests
Learning outcomes of the learning unit
This course will help students to :
- Gain the attitudes, skills, knowledge and understanding specific to the chosen concentration field.
- Establish a strategy to optimize the value chain of a company.
- Use modelization methods efficiently.
- Develop scientific precision and a critical sense (arguing).
- Take advantage of data digitalization.
- Acquire the 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.
- Provide concrete solutions to a management problem, integrating modelization methods and/or a dimension of technology, innovation or production.
- aqcuire a professional capacity for written communication.
Prerequisite knowledge and skills
Basic computer skills.
Basic statistics (descriptive statistics, and elements of probability)
Planned learning activities and teaching methods
Emphasis will be placed on the understanding and real-life application of the analytics methods. To this end, all the theoretical concepts will be directly illustrated on small examples and students will practice on real-data examples. They will acquire a basic theoretical knowledge but also learn to use professional analytics tools.
The students will have to complete two group projects on a provided dataset. Students will also have the opportunity to replace one part of the exam and the predictive analysis project with a an official certification exam in Machine Learning.
A flipped classroom approach is used for some chapters: the students have to prepare some topics before the lecture thanks to an online tutorial and/or slides. The lecture will focus on the difficulties encountered during the preparation, as well as on the interpretation of the results.
The project and the preparations are compulsory. If a student has not completed all his/her assignments, he/she will not be allowed to sit the exam (failure mark).
Mode of delivery (face to face, distance learning, hybrid learning)
All lectures require the use of a computer.
Organisational adjustments related to the current health context
Recommended or required readings
All required documents will be published on Lol@.
Assessment methods and criteria
Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.
Any session :
- In-person
written exam ( open-ended questions )
- Remote
written exam ( open-ended questions ) AND written work
- If evaluation in "hybrid"
preferred in-person
Additional information:
Option 1 : No certification
- Project 1 : Data visualization
- Project 2 : Machine Learning
- Exam Part 1: Understanding of the methods
- Exam Part 2 : Practice Data visualization
- Exam Part 3 : Practice Machine Learning
- Understanding of the methods (Exam Part 1) : 1/3
- Practice Data visualization (Project 1 + Exam Part 2) : 1/3
- Practice Machine Learning (Project 2 + Exam Part 3) : 1/3
- Project 1 : Data visualization
- Exam Part 1 : Understanding of the methods
- Exam Part 2 : Practice Data visualization
- You prepare (e-learning) for an official international certification delivered by SAS
- Understanding of the methods (Exam Part 1) : 1/3
- Practice Data visualization (Project 1 + Exam Part 2) : 1/3
- SAS Certification* : 1/3
--- The project and the preparations are compulsory. If a student has not completed all his/her assignments (first and second sessions), he/she will obtain a failure mark and will not be allowed to sit the exam. In second session, group projects are transformed into individual projects.
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 do not accept to organize any additional exam at a different date and/or at a different location (even for students from abroad).
Contacts
S. Aerts, HEC-ULg, N1 (334) Stephanie.Aerts@uliege.be