Curriculum "Data Analytics for Business"

FUNDAMENTALS OF MANAGEMENT

• Innovative strategies for sustainable entrepreneurship
• Financial performance management
• Business & sustainability: transition & durability
• Public speaking, visual communication & design
• Data management
• Marketing reconsidered
• Team management & inclusivity
• Ecological challenges & organisational management

EXPERT SKILLS

• 2 pre-specialisation courses in international business

LEARNING BY DOING

• Impact project: Transition, sustainability or inclusivity

REAL-WORLD SCENARIOS

• Case studies: Decarbonation & business methodology
• 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.

ADDITIONAL COURSES

• Languages:
  -English / FLE (French as a Foreign Language)
• Hard Skills:
  -TOSA Excel & coding skills
  -TOEIC preparation
  -Orthodidacte
  -Option to spend a semester abroad at a partner university
• Choose your experience:
  -Internship in the UK or abroad
  -Undertake another corporate project

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

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

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.