Data Analytics for Business

Build bridges between Data Science and Business

EdUniversal ranking 2023-Innovation Award

Curriculum "Data Analytics for Business"

Admission level

Degree

Short Track

Admission level:

Bachelor's degree - 4 years
Validation of Personal and Professional Experience (VAPP)

 
Master of Science (MSc)

17,400€ (*) - 1 year

Degree:

MSc, Master of Science
Diploma recognised by the French Ministry of Higher Education

Long Track

Admission level:

Bachelor's degree
Validation of Personal and Professional Experience (VAPP)

Pathway Course

9,500€ (*) - 1 year

Master of Science (MSc)

17,400€ (*) - 1 year

Degree:

MSc, Master of Science
Diploma recognised by the French Ministry of Higher Education

The fees are provided for reference purposes only. They might be subject to indexation for multi-year programmes (see general conditions)

Short Track

Intake: September

Admission level :
Bachelor's degree - 4 years Validation of Personal and Professional Experience (VAPP)
Duration :
1 year
Degree :
MSc, Master of Science Diploma recognised by the French Ministry of Higher Education
Campus :
Bordeaux
Format :
Full Time
Language :
English

Master of Science (MSc)

Duration :
1 year

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

Admission level :
Bachelor's degree Validation of Personal and Professional Experience (VAPP)
Duration :
2 years
Degree :
MSc, Master of Science Diploma recognised by the French Ministry of Higher Education
Campus :
Bordeaux
Format :
Full Time

Pathway Course

Format :
Full Time
Duration :
1 year
Language :
English
Campus :
Bordeaux
Admission level :
Bachelor's degree
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)

Format :
Full Time
Duration :
1 year
Language :
English
Campus :
Bordeaux
Admission level :
Bachelor's degree - 4 years
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.