1-Year MSc in Data Science for Marketing

Prepare to perform functions related to data analysis and marketing decision making.

1-Year MSc in Data Science for Marketing

Applications are now closed for the start of the 2024 academic year. This MSc remains available in a 2-year format.

Discover our program

A professional approach that is highly sought after by employers! The MSc in Data Science in Marketing is based on a teaching approach that balances theoretical knowledge and practical skills.

The courses are structured around three dimensions:

- Methodologies: machine learning, deep learning, psychometrics, etc.

- Practical skills: programming in R and Python, use of XLstat, SPSS, etc.

- Applications: recommendation systems, development of appetence models, automated customer segmentation, etc.

Throughout the course, you will attend a wide range of talks given by experts, learn more about the concepts involved in real-life case studies using databases supplied by the School's partners, and take part in data mining hackathons.

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Summary
  • Rhythm:
    Full time
  • Total duration:
    14 months maximum
  • Degree:
    Master of science - Grade Master, Visé BAC+5
  • Language(s):
    English
  • Format:
    In person
  • Campus:
    Paris Campus - Paris
  • Start Date:
    2024-09-02
  • Number of places:
    30
  • VAE accessible:
    No
  • Entry level:
    BAC +4
  • Duration of internship / Work experience:
    4-6 months
  • Training schedule:
    The 1-year MSc program offers a total of 408 hours of training

Program Benefits

On completion of the programme students can expect to achieve the following Key Learning Objectives:

Understand industry 4.0 trends and resolve modern day marketing problems by applying the principles of data science and algorithms.

Develop a thorough knowledge and understanding of structured and unstructured marketing data, identify important KPIs and explore their value proposition towards digital transformation and overall corporate strategy.

Some of the key topics covered will include descriptive analytics, predictive analytics, prescriptive analytics, consumer experimentation and psychometry, MIS, machine learning, big data and AI for marketing. 

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