How can you frame a data science question according to your client’s needs? In this blog post, our colleague Dominique explains how important it is to think about the business question in a different way – the data science way.
To increase revenue, customers should be offered products they may need or films they might like. In this blog post, our colleague Andreas explains how to train your own movie recommender with R and provides it in a Shiny App.
As data scientists, getting our hands on the data we need is often the most challenging part of a project. In practice, we tend to make life hard on ourselves because we don’t use the best tools for the job. Well no longer! Read on to learn how can you can harness Airflow to orchestrate your own ETL processes like a pro!
Have a look at what my team and I worked on during the Permafrost Hackathon in Zurich. The goal was to detect movements from multitemporal images. Since the images didn’t have any labels, we used unsupervised learning methods. Check it out, yo!
For all those, who are struggling with the (kind of weird) Johns Hopkins University COVID-19 case data CSV files, we’ve created a free API that makes it easy to integrate the latest worldwide COVID-19 data into your application.
This blog is a hands-on experience in Dash, presenting core components, how to display figures with callbacks, supplying you with a working web application to play with, and the resources to build your own. Dash is a powerful tool for Python developers. Developed by the team behind Plotly, Dash is an open-source framework built on top of Flask, Plotly.js, and React.js.
Function names should correspond to what they do. But have you ever wondered why whole packages are called what they are called? In this blog post Matthias leads you through the mysterious world of R package names.
In this blog post, Matthias shows you how to write and structure code even faster and more efficiently. Learn how to define keyboard shortcuts in RStudio with his step-by-step tutorial.
The number of different products and customers in any business area are practically infinite.
But how can you find interactions between them like joint purchases and define groups? One solution is the so-called Community Detection. In this blog post, I want to show you the magic behind Community Detection and give you a theoretical introduction into the Louvain and Infomap algorithm.
Newman and Postman form a great team to test your REST API. I will give you a quick roundtrip through both tools and their interplay: define requests and tests, export them, and let them run with CLI and within Jenkins.