Duration
30h Th
Number of credits
| Master in business engineering, professional focus in digital business | 5 crédits |
Lecturer
Substitute(s)
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
Objectives
The aim of this course is to introduce advanced, novel technologies and the state of the art in related research to masters students. These technologies and research are poised to play an increasingly important role in the digital age.
The main themes will revolve around:
- Distributed systems & NoSQL
- Big data computing
- Ethics, Regulations and Legal Aspects
The topics to be covered are listed below.
Necessary support and guidance (incl. consultation will be provided).
Topics
NoSQL
- Motivation for NoSQL
- Comparison with SQL type databases
- Replication & sharing strategies
- Introduction to NoSQL databases (column-family, XML-based, ...-
- MongoDB (including practical)
Infrastructure
- Containers
- Kubernetes
- Introduction to language models
- Open-source LLMs
NLP-Topic Models
- LDA
- Co2 emissions
- LLMs and energy consumption
Practicals
- NoSQL: Mongodb
- Web crawling
- Topic Modeling with LDA
- LLMs installation and configuration
Practical sessions will be organised.
Course participants will present their practical projects.
Learning outcomes of the learning unit
- Understand the underlying principles and algebraic formulations of advanced methods, algorithms and technologies in digital business.
- Ability to apply these models in various managerial contexts.
- Synthesize various principles and algorithms introduced in the course and to develop a full-fledge applications/solutions (as part of the course project)
- Implement at least one of the technology (smart contract project)
- Formulate a strategy based on the skills to optimize the value of an organization
- Ability to perform research on and understand advanced topics in the field and to be informed on recent developments to adapt easily to changing requirements
- Communicate appropriately about text analytics projects/applications to various stakeholders
Prerequisite knowledge and skills
- Reasonable skills (masters level) in mathematics & programming.
Planned learning activities and teaching methods
The course carries 5 credits and therefore requires 150 hours of work (1 credit = 30 hours).
Theory lectures = 18-22 hours
- Self-study for exam = approx. 70 hours
- Practical lectures = 9-12 hours
- Working on practical exercises and projects = approx. 80 hours
- Total = 150 hours (5 credits)
Mode of delivery (face to face, distance learning, hybrid learning)
- Lectures (face-to-face)
- Practical (during lectures and as homework)
Course materials and recommended or required readings
- Scientific articles, to be communicated during the lectures.These will be provided on Lol@, the online learning platform.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Continuous assessment
Further information:
Continuous Evaluation based on projects and presentations.
Work placement(s)
Organisational remarks and main changes to the course
Materials will be made available on Lol@, the online learning platform.
Contacts
Ashwin Ittoo, ashwin.ittoo@uliege.be