Recent Posts

• December 23, 2016podcast

  Episode 16: 2017 Predictions in Data Science

  We strongly believe 2017 will be a very interesting year for data science and artificial intelligence. Let me tell you what I expect and why.

• December 5, 2016podcast

  Episode 15: Statistical analysis of phenomena that smell like chaos

  Is the market really predictable? How do stock prices increase? What is their dynamics? Here is what I think about the magics and the reality of predictions applied to markets and the stock exchange.

• September 27, 2016podcast

  Episode 14: The minimum required by a data scientist

  Why the job of the data scientist can disappear soon. What is required by a data scientist to survive inflation.

• September 6, 2016podcast

  Episode 13: Data Science and Fraud Detection at iZettle

  Data science is making the difference also in fraud detection. In this episode I have a conversation with an expert in the field, Engineer Eyad Sibai, who works at iZettle, a fraud detection company

• July 26, 2016podcast

  Episode 12: EU Regulations and the rise of Data Hijackers

  Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the information hidden behind complexity. Here are some methods that yo...

• May 3, 2016podcast

  Episode 11: Representative Subsets For Big Data Learning

  How would you perform accurate classification on a very large dataset by just looking at a sample of it

• March 14, 2016podcast

  Episode 10: History and applications of Deep Learning

  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, to scene desc...

• March 2, 2016podcast

  Episode 9: Markov Chain Montecarlo with full conditionals

  At some point, statistical problems need sampling. Sampling consists in generating observations from a specific distribution.

• February 15, 2016podcast

  Episode 7: 30 min with data scientist Sebastian Raschka

  In this show I interview Sebastian Raschka, data scientist and author of Python Machine Learning.In addition to the fun we had offline, there are great elements about machine learning, data science, current and future trends, to keep an ear on. Moreover,...

• February 15, 2016podcast

  Episode 8: Frequentists and Bayesians

  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, who’s goin...