Data Science, Statistical Learning, Helsinki–Milan
Text and photo: Teemu Roos
Data Science is a young discipline. Using data in innovative ways has of course been practiced, well, forever, but “data scientist” has become the sexiest job only in the 21st century. Our own data science Master’s programme started in 2017.
About a year ago, I met Professor Guido Consonni from the Università Cattolica del Sacro Cuorein Milan, Italy. Guido was in Helsinki to act as the opponent at a statistics PhD defense, and he gave a talk at our Machine Learning Coffee Seminar. (By the way, he was very curious about our habit of serving porridge at the Monday morning seminars. Not curious enough to dare a taste, but he took photos to show to his colleagues in Italy.)
We talked about joint research interests – we both work on probabilistic graphical models – but he also mentioned that he was in charge of a closely related Master’s programme in Data Analytics for Business and Economics in Milan that had also started the same year as ours. What a coincidence!
To learn more about how they teach data science in Milan, I ended up teaching machine learning for three weeks in Guido’s programme.
What did I learn here:
- The challenge to accommodate students from diverse (statistics, economics, computing, …) backgrounds is very hard. How to harmonize the starting level without spending half a year in preparatory courses?
- The main tools are the same: for example, python (Jupyter, pandas, numpy, scipy), R, Google and Amazon cloud platforms, …
- Bayes is best!
- The students in Helsinki have very strong computing skills compared to many other programmes, which is natural since we have many students who did a major in computing.
- Conversely, economics students are very good at envisioning practical data science projects: from stock market predictionto earthquake monitoring.
- Italians can be shy at first too!
I still have two more lectures but I can also say that the trip was worthwhile. I hope we’ll be able to support both teacher and student exchange also in the future. Thus, we can learn from each other and provide better education. After all, we are one team.