Data Science Book: Everybody Lies

Seth Stephens-Davidowitz has written a very entertaining book on big data and how it can be used to understand Humankind. The main idea of Seth is that Google searches is the most powerful source of information to understand what people really think about. Seth argues that the main advantage of Big Data is our ability to zoom in the data. When referring to Big Data, Seth is only dealing with the notion of Volume (not the other Vs). He defines Big Data by providing use cases with new kinds of data in large volume that were not available before.

On Facebook and other social media sites, people tend to lie and try to show a nice story about themselves. They want to look good and to be seen as happy individuals. When using Google search engine, the story is different. The anonymous nature of the tool makes people ask about their real concerns. I would have appreciated a chapter on the limitations of such approaches. Mainly, not everyone is using Google search and the ones using it represent a biased sample of the world population.

While plenty of other topics are covered in the book (e.g. A/B tests), Seth focus primarily on examples related to sexuality. According to him, this is the most interesting subject for which people are more likely to lie. Seth provides plenty of meaningful examples of how people lie.

The book often mentions Data Science and Machine Learning. Seth is mainly performing data analysis using huge volume of data. Everybody Lies is definitely an interesting reading, with the same kind of freshness as Weapons of Math Destruction, which I also recommend.


Data Science Book: Profit Driven Business Analytics

Verbeke, Baesens and Bravo have written a data science book focusing on profit. Instead of the typical statistical or programming point of view, Profit Driven Business Analytics has a self-proclaimed value-centric perspective.

This means the book approaches each topic with a focus on profit, costs and ROI. Each data science subject is briefly explained and illustrated with business cases. The authors… Continue reading...


Data Science Workshop, EPFL, June 4-6th

What do Swisscom, Expedia, Cisco, Google, Frontiers and Bühler have in common?

They will present use cases at the Data Science Workshop, EPFL, June 4-6th.

“The 2018 IEEE Data Science Workshop is a new workshop that aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science theory and applications. In particular, the event will gather researchers… Continue reading...


Data Analytics for Internal Audit

This is a guest post from Marcel Baumgartner, Data Analytics Expert at Nestlé S.A.


Large publicly listed companies not only have external auditors who check the books, but often also a large community of internal auditors. These collaborators provide the company with a sufficient level of assurance in terms of adherence to internal and external rules and guidelines. This covers financial… Continue reading...


The academic tip: What is Deep Learning?

This is a guest post from Jacques Zuber, Data Science Teacher at HEIG-VD.

The commonly called deep learning or hierarchical learning is now a popular trend in machine learning. Recently during the Swiss Analytics Meeting Prof. Dr. Sven F. Crone presented how we can use deep learning in the industry in a forecasting perspective (beer forecasting for manufacturing, lettuce forecasting in… Continue reading...


Interview of Jerome Berthier, Head of BI and Big Data at ELCA

Data Mining Research (DMR): Can you tell us who you are and how you came to the field of Data Science?

Jerome Berthier (JB): My name is Jerome Berthier, I am an engineer in Computer Science and I have an MBA in management. After 10 years working in different roles for an IT provider (developer, sales representative, managing director), I joined… Continue reading...