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Using AWS Lambda and Slack to have fun while saving on EMR costs

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We all have these times where we hack a piece of code together in 5 minutes. Usually, these pieces of code are not hidden gems, they tend to do simple stuff. Every once in a while though, you will find yourself writing a simple script which gives you a big smile afterwards. In this post, I will discuss one of »

Connecting offline sales to online campaign sources with Google Analytics - Part 2

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Connecting offline to online is a challenge, but this week we did it. We’ve measured our first offline sales in Google Analytics, and we can directly attribute these to online campaign sources! .... This post describes the general system. The second post will discuss the actual code used in the system. The text mentioned above is a recap of the »

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

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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 »

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

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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)

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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 »