Short Track
Intake: September
Master of Science (MSc)
The MSc programme in Data Analytics for Business is a five-year programme that trains future managers specialising in the management and deployment of data projects and the digital transformation of companies. This specialised programme offers a comprehensive vision of the new disciplines linked to data and artificial intelligence as business accelerators.
SEMESTER 1
BOOTCAMP CODE
- Algorithmic
- Python
DIGITALISATION OF COMPANIES
- Digital strategies
- Digitalisation of business processes
- Management styles
- Inbound marketing
- Digital marketing
DATA ORGANISATION
- Data formats
- Data modelling
- Queries
- NoSQL/Hadoop/MapReduce
IMPACTS OF ARTIFICIAL INTELLIGENCE
- Ethics and artificial intelligence
- Legal dimensions of artificial intelligence
- Social impacts
- Economy of artificial intelligence
DATA VISUALISATION
- Descriptive statistics
- Multivariate analysis
- Dashboards
- Time series analysis
MACHINE LEARNING I (FUNDAMENTALS)
- Supervised learning (categorization and regression trees)
- Unsupervised learning (clustering)
- Reinforcement learning (Q-learning)
- Model validation
- Over- and under-fitting
- Cross validation
CONFERENCES, SEMINARS, SOFT SKILLS
SEMESTER 2
AGILE PROJECT MANAGEMENT
- Agile methods
- Management of project teams
- Specific methods and tools
- Customer relations
MACHINE LEARNING II (ADVANCED TECHNIQUES)
- Neural network
- Deep learning
- Random forest and xg boost
- Natural language processing
BUSINESS ANALYTICS
- Linear programming
- Non-linear programming
STOCHASTIC PROCESSES
- Time series and forecasting
- Simulation
BUSINESS AND AI
- AI and new business models
- Starting a new AI business
- Marketing of AI
IMPLEMENTING MACHINE LEARNING IN COMPANIES
- The different types of actors involved
- Information systems aspects
- Setting up a pipeline
- Maintenance and updates of models
CONFERENCES, SEMINARS, SOFT SKILLS
SUPPLEMENTARY PROJECTS
INTERNATIONAL INTERNSHIP
This internship allows students to convert their knowledge in data analytics during a 6-month internship in a multinational company.
FINAL DISSERTATION
Students select a relevant research question and write an academic assignment. They work closely with an academic supervisor who guides them through the various steps of their research.
Long Track
Intake: September
Pathway Course
SEMESTER 1
Manager Skills
- Introduction to business management (Fast Track for Non-Managers)
- Strategic management
- Financial performance management
- Business & Sustainability
- Public speaking
- Visual communication & Design
- Data management
- Hard skills
- TOSA Excel
- TOEIC
- Orthodidacte
- French/English as a second language
Expertise skills
- Marketing studies
- Data visualisation
- Digital experience
SEMESTER 2
INNOVATIVE & ENTREPRENEURIAL SKILLS
-Innovation management and entrepreneurship
-Complexity Management
-Coding skills
-Corporate project
CORPORATE PROJECT
A consultancy mission on a real and current issue of a company. Corporate projects enable participants to gain valuable hands-on experience of business conduct and strategy and deal with the practical complexities. Students are guided by their KEDGE tutor
throughout the project. The assignment is done in groups of 4 to 5 students. A combination of collective and individual work. Final presentation in front of the corporate and KEDGE tutors.
POSSIBILITY TO COMPLETE ONE SEMESTER ABROAD AT A PARTNER UNIVERSITY
Your choice of experience
- INTERNSHIP in France or abroad
- PROJECT: Associative, entrepreneurship...
- LEARNING TRIP ABROAD: You can find a job or take some language courses
- OTHER CORPORATE PROJECT
Master of Science (MSc)
Semester 1
BOOTCAMP CODE
- Algorithmic
- Python
DIGITALISATION OF COMPANIES
- Digital strategies
- Digitalisation of business processes
- Management styles
- Inbound marketing
- Digital marketing
MANAGEMENT STYLES
- Inbound marketing
- Digital marketing
DATA ORGANISATION
- Data formats
- Data modelling
- Queries
- NoSQL/Hadoop/MapReduce
IMPACTS OF ARTIFICIAL INTELLIGENCE
- Ethics and artificial intelligence
- Legal dimensions of artificial intelligence
- Social impacts
- Economy of artificial intelligence
DATA VISUALISATION
- Descriptive statistics
- Multivariate analysis
- Dashboards
- Time series analysis
MACHINE LEARNING I (FUNDAMENTALS)
- Supervised learning (categorization and regression trees)
- Unsupervised learning (clustering)
- Reinforcement learning (Q-learning)
- Model validation
- Over- and under-fitting
- Cross validation
CONFERENCES, SEMINARS, SOFT SKILLS
Semester 2
AGILE PROJECT MANAGEMENT
- Agile methods
- Management of project teams
- Specific methods and tools
- Customer relations
MACHINE LEARNING II (ADVANCED TECHNIQUES)
- Neural network
- Deep learning
- Random forest and xg boost
- Natural language processing
BUSINESS ANALYTICS
- Linear programming
- Non-linear programming
STOCHASTIC PROCESSES
- Time series and forecasting
- Simulation
BUSINESS AND AI
- AI and new business models
- Starting a new AI business
- Marketing of AI
IMPLEMENTING MACHINE LEARNING IN COMPANIES
- The different types of actors involved
- Information systems aspects
- Setting up a pipeline
- Maintenance and updates of models
CONFERENCES, SEMINARS, SOFT SKILLS
Supplementary contents
INTERNATIONAL INTERNSHIP
Enables students to apply data analytics that they have learnt during a six month assignment within a multinational company.
FINAL DISSERTATION
Students select a relevant research question and write an academic assignment. They work closely with an academic supervisor who guides them through the various steps of their research.
