Do you want to implement your Data Science solution in your company’s infrastructure and wonder which method is suitable? In this blog post, Jan Fischer explains why the use of containers combined with a microservices architecture may be the right way for you.
Auch unser STATWORX Team in der Schweiz ist diesen Monat in ein neues Büro umgezogen. Die neuen Räumlichkeiten im hippen Industrieviertel von Zürich bilden die Grundlage für den geplanten Ausbau des Standortes. Mehr zu den Zielen und Plänen des Teams Schweiz erzählt euch unsere Kollegin Livia in diesem Blogartikel.
Are you confused by Bayesian statistics? If you understand Ridge regression, one of the most common Bayesian models is within your reach! This post gives a brief intro to Bayesian thinking and shows you just how similar Ridge regression is to Bayesian linear regression by walking you through the math and exploring how coefficient estimates from both models compare.
In this blog post, Andre explains two approaches on how to secure a REST API: One works with nginx and the sub-request feature, the other implements the verification part in the API itself.
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.
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!
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.
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.
A new field of Machine Learning is born: Causal Machine Learning. Learn here about the Causal Forest, one of the most famous Causal Machine Learning algorithms for estimating heterogeneous treatment effects.