In this episode I speak to Alexandr Honchar, data scientist and owner of blog https://medium.com/@alexrachnogAlexandr has written very interesting posts about time series analysis for financial data. His blog is in my personal list of best tutorial blogs.

In this episode I have a wonderful conversation with Chris Skinner. Chris and I recently got in touch at The banking scene 2019, fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he define.

From a recent publication titled “EU regulations on algorithmic decision-making and a right to explanation”  about EU regulations to be applied to machine learning algorithms and data science in the near future,

In this episode I meet Dr. Eliseo Ferrante, formerly at the University of Leuven, currently researcher at the Université de Technologie de Compiègne, who studies self-organization and evolution. His academic page here.

Have you ever thought to get a Big Data infrastructure on your desk? That’s right! On your desk. In this episode I met Wim Van Leuven (Twitter, LinkedIn), software guy (and much more), who is operating in the data science arena for a while.

In this episode I give an introduction about networks and the theory behind graph databases. I hereby revisit a lecture I gave at the University of Liege, Belgium, the slides of which are available here. An Introduction to Networks from Francesco Gadaleta

How would you perform accurate classification on a very large dataset, by just looking at a sample of it? In this episode I interview friend and colleague Rocco Langone, Machine Learning Researcher at the University of Leuven, Belgium.

What is deep learning? If you have no patience, deep learning is the result of training many layers of non-linear processing units for feature extraction and data transformation e.g. from pixel, to edges, to shapes, to object classification,

Markov Chain MonteCarlo with full conditional calculations At some point, statistical problems need sampling. Sampling consists in generating observations from a specific distribution. Prior knowledge and likelihood are the essential components of baye...

There are statisticians and data scientists… Among statisticians, there are some who just count. Some others who… think differently. In this show we explore the old time dilemma between frequentists and bayesians. Given a statistical problem,