Assessment methods

  • Case Study: Based on a company case study detailing its industry and future challenges, the candidate will supplement the industry analysis using external sources and propose various strategic options for the use of AI and big data within the company.
  • Practical Case Study: Based on an AI project currently being rolled out within a real or fictional company, the candidate presents a policy for addressing the human, organizational, and environmental impacts of AI use and proposes solutions to mitigate them.
  • Simulated professional scenario (developing a business model): Using a set of raw data from various sources, the candidate must analyze the different components of the organization’s data value chain by employing a range of technologies and methodologies to convert raw data into actionable insights, and propose various options for leveraging this data to benefit the company.
  • Practical Case Study: Based on a real-world AI project, the candidate must produce a report providing an in-depth analysis of the challenges and constraints associated with data usage, as well as the ethical implications of using AI.

Teaching methods

The program utilizes a variety of teaching methods designed to promote active, engaging, and stimulating learning for students. These teaching methods include, but are not limited to:

  • Conceptual and theoretical foundations: Classroom lectures and presentations enable students to acquire fundamental theoretical knowledge. These courses are taught by experts in the field and are based on concrete examples and case studies.
  • Group work: Group work allows students to develop their collaboration, communication, and problem-solving skills. It also promotes the exchange of ideas and peer learning.
  • Professional scenarios (AI Clinic): Simulations, role-playing, and real-world projects allow students to put their knowledge and skills into practice in realistic professional contexts. This helps them develop their analytical skills, creativity, and decision-making abilities.
  • Flipped classrooms: In flipped classrooms, students prepare assignments, case studies, and research ahead of class. In class, they discuss, share their ideas, and work together to deepen their knowledge and solve problems. This approach promotes active, student-centered learning.

Other teaching methods

In addition to the teaching methods mentioned above, other approaches may be used depending on the specific needs of the program and learning objectives. These approaches may include guest lectures, workshops, company visits, and research projects.

Choice of teaching methods

The choice of teaching methods will be guided by the program's educational objectives, the characteristics of the learners, and the resources available. The goal is to create a dynamic and stimulating learning environment that promotes the acquisition of the skills and knowledge students need to succeed in the field of AI/Data.

Importance of diversity in teaching methods

Using a variety of teaching methods allows you to cater to students' different learning styles and keep them engaged in their learning. It also creates a richer and more stimulating learning environment that encourages critical thinking, creativity, and problem solving.