Image created by author, based on images by Neven Krcmarek on Unsplash and Andyone on Unsplash


Learn Which Strategies and Factors Highly Influence Innovation Adoption


Had you a…

Image created by author; based on work from Pixabay Close-up of Wooden Plank and Photo by Frank Luca on Unsplash and Photo by Anshu A on Unsplash

Hands-on Tutorials, TUTORIAL — R — OKTOBERFEST

A Step-By-Step Tutorial on How to Analyze and Visualize Oktoberfest Data Using R and ggplot2 and How to Predict Price Information

My running journey; image by author


A Step by Step Tutorial to Create Beautiful Art using Data viz tools and Affinity Designer

Hi, I am Gregor, a Data Scientist, and a passionate non-competitive runner. I also enjoy the art of photography, while — with everything — I am still learning.

1. Introduction

One of my strategies to be creative next to a full-time job is to write about Data Science. I recently wrote an article about how to extract and visualize data collected by my smartwatch. It is most technical and about knowledge that I liked to share. But then, most recently, I read an article from Chesca Kirkland where she described how she collected personal data during the lockdown phase and how she…

Photo by Bruno Nascimento on Unsplash; slightly altered by author


A Step-by-Step Guide to Retrieve and to Visualize your Running Data with Python and Altair

1. Introduction

Hi, I am Gregor, a Data Scientist, and a passionate non-competitive runner. I just realized that I started to use a running app on my phone ten years ago. Back then, I just recorded GPS, start and end-times. I had no means to record cadence, heart rate, elevation, and the like. I remember to be a bad runner — slow and easily out of breath. I just finished my Ph.D. and worked way too much at my desk. So I decided to start this journey as a runner.

Ten years is quite a long time and I am wondering what…


A step-by-step guide to understand, install, use, and enjoy the standard logging package in Python

Photo by Jaymantri on Pexels

Gregor is a data scientist who loves to solve data riddles. One day, he is given a new project and wants to jump into this new task immediately. He is prepared and a master of his favorite coding tool of choice, knows which packages to use, and already he forms an idea of how to structure the data project. Logging is not on his mind. Not at the start, that is. It dawns on him when most of the code is written and there is only one bug that he tries to pinpoint and find in his code. Once he…


And how you can use a free database tool to start right now to build your own Article Pipeline — from idea generation and planning until review

Image by author; based on work from RetroSupply and Jordan Wozniak on Unsplash, inspired by Michal Malewicz’s article

Hi, I am Gregor, a researcher, a writer, a data scientist, and a consultant. And I like all of it, but these passions compete for the same amount of available time and energy. At any given moment new ideas emerge that compete with old ideas that I am working on at the moment. Maybe you can relate to this scenario. Wouldn’t it be great to have something to put your ideas into and that allows you to work on them gradually as well as take a step back and sort your ideas? …

Tips and Tricks

In this article you will learn how to use a great concept in Python and Pandas to make your code more efficient and better to read (even for your future self)

Photo by JJ Ying on Unsplash


By trade I am an R person. Especially the Tidyverse is such a powerful, clean, easy-to-understand and well documented data science platform. I highly recommend to every beginner the free online book R for Data Science.

However, my team’s programming language of choice is Python/ Pandas — which is also a wonderful data science platform. One of the major differences (to me, at least) is how we write Python code, which is very different to R code — that has nothing to do with the syntax in itself.

One of R’s elegances is using the pipe functionality programming metaphor. This…

You will learn how to analyse your LinkedIn connection data using R, ggplot2, and dpylr.


Photo by inlytics | LinkedIn Analytics Tool on Unsplash

March 15th, 2021 marks my ninth year on LinkedIn. I joined LinkedIn not at the beginning of my professional life, but nine years represent the better part of me working. I was a researcher with Siemens CT before I went into the consulting business in 2011, where I am still active today. Looking back, my consisting topics are process management and data science — topics I really enjoy. Since joining LinkedIn in 2012 I made 720 virtual connections 😯.

Some weeks ago I was reading Richard Cornelius Suwandi’s article about analyzing his own LinkedIn data. And of course it made…


Boxplots are extremely useful to learn more about any given dataset. Basically, it allows you to compare a continuous and a categorical variable, that includes information about distribution and statistics, such as the median. As an example, let us explore the Iris dataset.

Let’s say you want to know more about the variable Sepal.Length. One way to do this would be to look at its statistics.

summary(iris$Sepal.Length)##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 4.300 5.100 5.800 5.843 6.400 7.900

If you want to look at the variable Sepal.Length and differentiate by another variable - let's say…


A step-by-step tutorial how create a basic chart up to a publication ready dataviz chart

Photo by faaiq ackmerd from Pexels

If you love plotting your data with R’s ggplot2 but you are bound to use Python, the plotnine package is worth to look into as an alternative to matplotlib. In this post I show you how to get started with plotnine for productive output.

If you want to follow along please find the whole script on GitHub:


Dr. Gregor Scheithauer

Gregor Scheithauer is a consultant, data scientist, and researcher. He is specialized in Process Mining, Process Management, and Data Analytics.

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