Name: Axel Ullern, President & Professor
Organization: Axel ULLERN Consulting
I teach as an independent teacher (external) at some of the most prestigious high schools and universities in France, including: École Centrale Supélec Université Paris-Dauphine PSL, ESCP Europe, Grenoble école de management (GEM), EPITA (Ecole pour les technologies avancées), ISG (institut supérieur de gestion), MBA-ESG Le Cnam (Conservatoire National des Arts et Métiers), and EM-Normandie.
After a long career at HP, in 2012 I decided to create my own company, Axel ULLERN Consulting, to deliver consulting services in IT management and teach at high schools and universities in France. I've specialized in data science for five years now, and it was during this time I discovered the Dataiku DSS platform.
For over three years I've been training approximately 300 students per year in data science at some of the most prestigious French schools and universities. My course is 100% based on the Dataiku platform, and I've trained around 1,000 students so far on this tool.
The students who take my course come from a mix of technical and non-technical backgrounds, falling into one of two camps:
- Business-oriented majors: Students following a two-year study at the master's level in an international business program. Most of the content in this program is about preparing them to be business leaders in large International companies and to occupy functions in HR, finance, and marketing. Some of them will also start their careers in manufacturing (supply chain). These students are beginners in data science when they start my class. Typical programs and schools include the Master of International Business at Paris-Dauphine, the Master in Digital Management at GEM, and the Master in Sales at Centrale Supelec.
- Technically-oriented majors: Students specializing in data science, who are preparing to occupy positions in IT including data scientist and data analyst. Typical schools would include EPITA and MBA ESG.
It's important to note that most of my students are doing apprenticeships, which means they're partially in school and partially working at a company throughout the duration of the program. This means I have an eye on what they're doing at their respective companies during their time in my class, and as a result, I can adapt my teaching to meet the real needs of these businesses.
As mentioned above, my students are all at the master's level, and in a few years, I believe they will occupy managerial positions in large-scale French or international companies, as well as in small and medium-sized companies, and startups. They will remember their academic training and likely become prescribers or ambassadors of Dataiku. As soon as they have data-related problems to solve, they will have the reflex to think of this platform.
As a result of my training, I contribute to raising the level of knowledge of Dataiku within large French and international companies and feel I indirectly participate in its development. I'm convinced that the Dataiku platform is brilliant, and perfectly matches the needs of business entities and companies. It allows non-specialists with business-related problems to collaborate on the same project with data analyst scientists and business managers.
This is why I decided to base my teaching on this product: My students are future business leaders, and when they will start their careers, they'll need an intuitive tool to help them solve complex business issues without being dependent on a data scientist. I believe that most companies and businesses need to become more and more comfortable with extracting value from data, and Dataiku is a perfect fit for this task.
I've developed a particularly effective teaching method that has proven to be successful. In the space of a few tens of hours, my students are able to produce good predictive analysis with limited guidance from me using Dataiku.
They learn by individually practicing with the Dataiku platform, and when they feel they've mastered it, I advise them to work in small teams under my supervision. They'll then work collaboratively on projects similar to the kind of data projects they'll have to deal with when they begin their careers.
For my teaching, I rely on all of the materials available via the Dataiku Academy, including use cases and tutorials, as well as data sets available on the Kaggle site. At the end of my course, I can check that my students are able to analyze and make predictions using Dataiku on almost any data set, and solve complex problems without necessarily needing to call on a data scientist or data analyst. What's more, I teach them to make recommendations to management based on their analyses, as if they were consultants.
At the end of my course, my students have provided positive feedback and are satisfied with the framework, with many choosing to go on to pass their certification with Dataiku. They've explained to me that the course has finally allowed them to develop a concrete understanding of complex concepts such as machine learning, AI, and data science.
Some examples of student feedback:
- "I'm personally interested in business analytics and data science. I didn't have the chance to learn about it before but always wanted to. Thanks to your class, I was able to learn through different tools and experience it! It made me want to learn more about the topic, and is definitely useful."
- "Your course aroused my interest in data analysis, and I would like to know more about the data analyst role and the skills required to do this job."
Value Brought by Dataiku:
Dataiku is an excellent platform for non-specialists to learn about data science and AI and help improve their analytical skills. In my class, I use some of the examples found on the Dataiku Academy, such as the bank fraud detection use case, which helps them a lot in understanding the most important aspects and functionalities of Dataiku.
During each step of going through the use case, I pay a lot of attention to ensuring that each of my students understands the "why" (why each step is necessary in the use case) and not only the "what" (what needs to be done). When I see they've clearly understood, I provide them with a new dataset and business problem to solve, and they have to do the complete analysis. For example, why customers have churned and what the company should do to reduce churn.