What are driving factors behind the gas price? With freely accessible data we are goging to find out if the brand, the location and more have any impact on the price!
What the MAPE is FALSELY blamed for, its TRUE weaknesses and BETTER alternatives!
In time series context, one of most the commonly used measures is the MAPE. In this blog post, I evaluate critical arguments and weaknesses concerning the MAPE and demonstrate alternative measures.
Monotoniebedingungen in Machine Learning Modellen mit R
Monotoniebedingungen können helfen den Sachverhalt besser durch Modelle darstellen zu lassen. In diesem Beitrag wird erklärt wir man solche Monotoniebedingungen in R umsetzt.
Simulating the bias-variance tradeoff in R
In my last blog post, I have elaborated on the Bagging algorithm and showed its prediction performance via simulation. Here, I want to go into the details on how to simulate the bias and variance of a nonparametric regression fitting method using R. These kinds of questions arise here at STATWORX when developing, for example, new machine learning algorithms or …
Optimising your R code – a guided example
Do you want to optimise your code but don’t know where to start? In this post I guide you through my thought process when I optimised my code.
Data University 2019 – Get your Data Degree
BARC und STATWORX präsentieren vom 09. – 10. Oktober 2019 die Data University, in Fankfurt am Main. Die Data University ist ein interaktives Workshop- und Trainings-Event mit dem Fokus auf alle wichtigen Themen rund um Daten.
6 myths about refuelling – tackled with statistics
Is it cheaper to fill up your gas tank in the evening? Many car drivers have their own theories and myths about refuelling. Our colleague Jakob tackled 6 of these myths using statistics in his latest blog post.
Roses Are Red, Violets Are Blue, Statistics Can Be Romantic Too!
It’s Valentine’s day, making this the most romantic time of the year. But actually, already 2018 was a year full of love here at STATWORX: many of my STATWORX colleagues got engaged. And so we began to wonder – some fearful, some hopeful – who will be next? Therefore, today we’re going to tackle this question in the only true way: with data science!
Plotly – An interactive Charting library
In this blog we will explore the plotly library for python and R. We show how plotly is structured and use the LA Metro Bike dataset as an example to create interactive plots.
Dreaming of a white Christmas – with ggmap in R
With the holidays approaching, one of the most discussed questions at STATWORX was whether we’ll have a white Christmas or not. And what better way to get our hopes up, than by taking a look at the DWD Climate Data Center’s historic data on the snow depth on the past ten Christmas Eves? But how to best visualize spatial data? …