Learning Journeys for University Lecturers

Objectives of the Learning Program

The University created a list of requirements for this learning program. Most important objectives were:

1. The participants should be engaged with AI
2. The content of the course contains the latest insights from real-world practices
3. The content of the full study program should be flexibly set-up

To be quite honest, not many companies are able to comply with all three requirements. Moreover, we were looking for a more structural collaboration rather than a one-off. In Xebia, we found a partner that has the authority in AI in its veins. With full confidence we can put their trainers in front of this difficult audience. But Xebia not only appeared to be a source of insights: they also helped us with the design of the program.

Program Design

The program was designed to empower experienced lecturers to teach students what is important when working on data & AI use cases. The program starts with an analytics translator course. It is then followed by an intense tour of Python for Data Analysis — especially pandas and various plotting libraries in the ecosystem. It continues with Python for Machine Learning sessions, with an emphasis on scikit-learn.

After mastering the tools, the program continues with an in-depth series of lectures. They cover linear programming, time series analysis and forecasting, A/B testing, and Bayesian modeling.

At the end of this three months program, the lecturers are ready to engage with the students. The hands-on knowledge gained allows them to be confident in front of the most challenging classes.

Continued Collaboration

The University sees Xebia as a sparring partner for future projects as well. An example is a subsidiy program for data science projects in the industrial sector.