Daniëlle adds: “Until now, part of the analysis—besides Excel—mainly existed in my head.”
The students immediately gave their advice. Lola: “Python offers a solution. It’s a programming language that Charissa and I worked with a lot during our AI studies and still use extensively during our master’s in Data Science. Together with Daniëlle, we documented the Strategic Workforce Planning analysis in more detail, making the entire analysis programmable. It was something completely new for Daniëlle, but she became incredibly enthusiastic about it! She is now taking a basic programming course.”
Favorite language
Charissa: “Python is my favorite language. I’m still learning a lot from it myself: code can always be cleaner. But Python generally works easily, and even less elegant code still works.”
Next, Lola and Charissa wrote an algorithm in Python. They regularly visited the office to present their progress. Lola explains: “The staff at Achterhoek VO were so enthusiastic that, after delivering the Python code, we were also asked to deliver an app. With an app, Daniëlle can present the strategic workforce plan more effectively to colleagues.”
Dashboard app
The students have plenty of experience with Python, but building an app was something entirely new to them. They are now putting the finishing touches on it, and the dashboard app—which provides insights at foundation level and per school—is almost complete. Charissa says: “That involved other languages, such as JavaScript. Lola and I had to do a lot of research, and fortunately we had each other as sparring partners during this process.”
Daniëlle: “The app makes our process much more efficient. Automating the analyses not only saves time, but also ensures that the results are produced in a uniform and reliable way every time.”
Monic adds: “What surprised us most was how quickly Lola and Charissa mastered this subject matter and translated it into an automated solution.”
New experience
For Lola and Charissa, this case at an SME was a new experience. Lola says: “We had both previously, separately, done an assignment within MKB Datalab-Oost. Back then, the focus was mainly on analyzing the business process and mapping out opportunities for AI within the company.”
Charissa adds: “At university, we receive a largely theoretical education with some practical elements. But thanks to MKB Datalab-Oost, you learn in a different way. You build something that will actually be used in the business world.”
Lola: “This assignment confirmed for me that after graduating, I want to apply my AI knowledge in the social sector.”
Monic Schijvenaars on LinkedIn: “We want to have a clear picture of which teachers are retiring, how many people per subject and per school leave on average, and how this relates to declining student numbers now (and in the future). This allows us to predict, steer, and anticipate in a timely manner. Thanks to Lola and Charissa, we now have an almost completed dashboard app—scalable to other school boards and education regions, I think. Superb work by Lola and Charissa!”
What is MKB Datalab-Oost?
MKB Datalab-Oost introduces entrepreneurs to AI and data-driven entrepreneurship in a low-threshold way. It does so through inspiration sessions and masterclasses, and by engaging in small-scale AI projects that are custom-designed in close collaboration with regional entrepreneurs at an attractive rate. MKB Datalab-Oost also offers, in the form of a Data Buddy, students who inventory the possibilities within a company to get started with an AI project.
The initiative is a collaboration between Radboud University and the University of Twente, inviting entrepreneurs in the eastern part of the Netherlands to learn more about AI and the opportunities it can offer them. The assignments are carried out by AI and Data Science students from Radboud University, who in turn are supported by lecturers.
