INFO9026-1

Duration

30h Th

Number of credits

	Master in management, teaching focus	5 crédits
	Master in management, professional focus in Banking and Asset Management	5 crédits
	Master in management, professional focus in Financial Analysis and Audit	5 crédits
	Master in management, professional focus in global supply chain management	5 crédits
	Master in management, professional focus in Intrapreneurship and Management of Innovation Projects	5 crédits
	Master in management, professional focus in international strategic marketing	5 crédits
	Master in management, professional focus in social and sustainable enterprise management	5 crédits
	Master en sciences de gestion, à finalité spécialisée en management des organisations et dynamiques sociales	5 crédits
	Master in management, professional focus in strategy and human resource management	5 crédits
	Master en sciences de gestion, à finalité spécialisée en management des entreprises sociales et Transition	5 crédits
	Master in management (60 ECTS)	5 crédits

Lecturer

Siamak Khayyati

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

Introduction to the subject of management analytics and the process of supporting managerial decisions with data and analytical solutions.

The course focuses on the following subjects

Basics of data visualization
Machine learning methods including Linear and logistic regression, KNN, Decision trees and Random Forests, and K-means clustering

A large part of the course is dedicated to practice through examples and project(s).

Learning outcomes of the learning unit

Upon completion of this unit, the student will be able to:

Use data visualization tools to generate visualizations that communicate the information in a dataset
Explain and understand the fundamental concepts of machine learning
Think and propose machine learning solutions for concrete business problems
Understand the risks and challenges a machine learning project
Select a machine learning method adapted to the context, the problem and the dataset
Interpret the results of machine learning methods

Prerequisite knowledge and skills

Basic computer skills.

Basic statistics (descriptive statistics, and elements of probability)

Planned learning activities and teaching methods

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

Face-to-face course

Further information:

All lectures require the use of a computer.

Jupyter notebooks and Excel will be used in the class. However, prior knowledge of programming with Python is not required.

Course materials and recommended or required readings

Platform(s) used for course materials:
- LOL@

Further information:

All required documents will be published on Lol@.

Exam(s) in session

Any session

- In-person

written exam ( multiple-choice questionnaire, open-ended questions )

Further information:

Group project.

Work placement(s)

Organisational remarks and main changes to the course

Contacts

Siamak Khayyati: s.khayyati@uliege.be

Management Analytics