Thoughts, lessons, and the occasional rabbit hole
Practical data work, analytics, visualization, databases, and machine learning.
11 articles
Transitioning to another SQL database? This blog post is for you. Shifting from one SQL dialect to another can be a journey full of surprises. While the basic syntax (SELECT FROM WHERE) is similar, there are important differences, that will make your queries slow, fast, fail or worse: fail silently!
Most machine learning models are only as good as the data they learn from. That sounds obvious, but it is surprisingly easy to forget once you have millions of data points and a model that performs well on your offline metrics.
Search engines rely on models, which rank the matching results for a given user query. These models optimize the order of items. They learn how to rank items in a result list, therefore the name Learning-to-Rank (LTR) models.
In many scenarios, such as a google search or a product recommendation in an online shop, we have tons of data and limited space to display it. We cannot show all the products of an online shop to the user as a possible next best offer. Neither would a user want to scroll through all the pages indexed by a search engine to find the most relevant page that matches his search keywords. The most relevant content should be on top. Learning to rank (LTR) models are supervised machine learning models that attempt to optimize the order of items. So compared to classification or regression models, they do not care about exact scores or predictions, but the relative order. LTR models are typically applied in search engines, but gained popularity in other fields such as product recommendations as well.
In this blogpost I will share some tips for working with Jupyter Notebooks. Those tips greatly improved my productivity when working with Jupyter Notebooks and I wish someone would have told me earlier. The two main topics of this post are extensions and magic commands.
The basic idea of complex datatypes is to store multiple values in a single column. So if you are working with a Hive database and you query a column, but then you notice “This value I need is trapped in a column among other values…” you just came across a complex a.k.a. nested datatype.
Seaborn is a python library for creating plots. It is based on matplotlib and provides a high-level interface for drawing statistical graphics.
When I started working with pandas I noticed that there were so many ways how to subset, filter and join data with pandas. But I was lacking a systematic overview. How do the different approaches differ and when to use which?
This post is about how to change your R working directory. You might be wondering:
In this post, I will show you some of my best practises for formatting tables in R Markdown. We will cover
I write this blogpost for someone, who has never worked with R Markdown. After you read this post, you will