Latest post

Helping our new Data Scientists start in Python: A guide to learning by doing

Author image

The Data Science team at Greenhouse Group is steadily growing and continuously changing. This also implies new Data Scientists and interns starting regularly. Each new Data Scientist we hire is unique and has a different set of skills. What they all have in common though is a strong analytical background and the practical ability to apply this on real business »

Calculation of confidence intervals for ratios

Author image

The science of campaign optimization was among the very first things I was introduced to when first coming to work in programmatic media at Bannerconnect, and it was with great interest that I learned the particular steps our campaign managers regularly take to ensure campaigns run in the most effective way for their advertiser. Statistically sound optimization From the data »

Send Slack notifications whenever a Pokémon spawns nearby using a Pokémon GO SlackBot

Author image

CURRENT STATUS (04/08/2016): Niantic seems to have made some big changes to the API last night, so currently all API scripts (including PoGoMap and bots) seem to be down. Feel free to share any fixes if you come across anything that could be of help. Meanwhile, I hope that the post is still a good read about how »

Upload your local Spark script to an AWS EMR cluster using a simple Python script

Author image

Apache Spark is definitely one of the hottest topics in the Data Science community at the moment. Last month when we visited PyData Amsterdam 2016 we witnessed a great example of Spark's immense popularity. The speakers at PyData talking about Spark had the largest crowds after all. Sometimes we see that these popular topics are slowly transforming in buzzwords that »

A recommendation system for blogs: Content-based similarity (part 2)

Author image

In this second post in a series of posts about a content recommendation system for The Marketing Technologist (TMT) website we are going to elaborate on the concept of content-based recommendation systems. In the first post we described the benefits of recommendation systems and we roughly divided them in two different types of recommenders: content-based and collaborative filtering. The first »