Duration
15h Th, 25h Mon. WS
Number of credits
| Master in data science (120 ECTS) | 5 crédits | |||
| Master in data science and engineering (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
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
This course covers machine learning techniques such as: - data preparation - decision trees - neural networks - linear and logistic regression - market basket analysis - clustering - model analysis
This course includes a SAS certification "Predictive Modeling Using SAS Enterprise Miner". A nice added value to your degree. See below for details:
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
Basic computer skills Basic statistical knowledge
Planned learning activities and teaching methods
This course is based on a SAS certification program. The students who obtain a mark larger than 72% also get the SAS external professional certification.
The online course provided by SAS is open at the beginning of the year and the exam will be held at the end of the second semester. You can work at your own pace, when you wish, and from where you want (even from your Erasmus location during the first semester while the offline course starts only in February). The SAS exam can be sit at the end of the year at HEC or before in a SAS certified center.
The off-line lectures are optional. During the lectures, we help you to start the training (connection and basics) and we are available to answer questions.
Mode of delivery (face-to-face ; distance-learning)
This is an online course with some otional offline lectures.
Recommended or required readings
SAS e-course: "Applied Analytics Using SAS Enterprise Miner"
(this is the support used for the offline course AND the online SAS version).
Online description: Applied Analytics Using SAS Enterprise Miner
SAS certification description: SAS certified predictive modeler using SAS EM
Details: http://www.sig.hec.ulg.ac.be/ulg/aaem.php
Assessment methods and criteria
First session
Exam on computer - theory and practice
Remark: the FIRST certification exam is free for my students.
Second session:
Exam on computer - theory and practice .
A fee may be required to sit a second (or more) time the certification exam.
If it is a problem, the student will be allowed to choose between the SAS certification or a local exam (free but without certification).
The final mark for the course is the result of the certification exam (or the local exam)
Work placement(s)
Organizational remarks
Contacts
M. Schyns, HEC-Liege, N1
M.Schyns@uliege.be
S. Aerts, HEC-Liege, N1
Stephanie.Aerts@uliege.be