Go Data Driven BLOG!

Welcome to the Go Data Driven BLOG.

This is the place where we share our knowledge and opinions. We will try to post new content regularly.
Enjoy, the GoDataDriven team.

Are sklearn defaults wrong?

03 Sep

There was some uprising on Twitter recently about the default behavior of sklearn LogisticRegression:


Improved wireless coverage using an old router

28 Aug

Using an old router (WRT54GL) to improve the WiFi coverage.


Data Driven Board Game Design

23 Aug

Using a simulation to design a board game


Real time analytics: Divolte + Kafka + Druid + Superset

22 Aug

Divolte is GoDataDriven's open source click stream collector which enables you to collect events. These insights are easily visualized using Apache Superset as an interactive slice and dice tool, utilizing Apache Druid as a scalable backend.


DeepCS - Berlin Buzzwords 2019

26 Jul

I gave a talk on DeepCS at Berlin Buzzwords 2019. This blog post explains how DeepCS works and what it implies for the future of information retrieval systems.


Fairness in AI - Dutch Data Science Week 2019

23 Jul

At the Dutch Data Science Week I gave a talk about Fairness in AI. The impact of AI on society gets bigger and bigger - and it is not all good. We as Data Scientists have to really put in work to not end up in ML hell!


How to build your first image classifier using PyTorch

18 Jul

Step by step tutorial on training an image classifier using PyTorch


Data Science Podcast Recommendations

12 Jul

At GoDataDriven we like to listen to podcasts related to our field. We share the ones we like amongst ourselves and now share them with you as well!


The Analytics Translator Part 3: Characteristics of an Analytics Translator

10 Jul

The Analytics Translator is the liaison between senior management, the business, and data experts. What characteristics make them suited for this role?


The Analytics Translator Part 2: The Problems an Analytics Translator Solves

03 Jul

An Analytics Translator enables the execution of your company’s AI strategy. Data engineers are good at developing robust applications. Data scientists are good at distilling intelligence from data. Business teams know their specific processes, habits, and work-arounds like no other. Still, there is a gap between data experts and business. Translators bridge it by solving three business problems for you.