Today the biggest book fair of the world starts again in Frankfurt, Germany. I thought this might be a good opportunity to do you some good!
Springer is one of the most renowned scientific publishing companies in the world. Normally, their books are quite expensive but also in the publishing business Open Access is a megatrend.
If you want to use R in a little fun project to find the latest additions of open access books to their program read on!
Continue reading “Finding free Science Books from Springer”
The two most disruptive political events of the last few years are undoubtedly the Brexit referendum to leave the European Union and the election of Donald Trump. Both are commonly associated with the political consulting firm Cambridge Analytica and a technique known as Microtargeting.
If you want to understand the data science behind the Cambridge Analytica/Facebook data scandal and Microtargeting (i.e. LASSO regression) by building a toy example in R read on!
Continue reading “Cambridge Analytica: Microtargeting or How to catch voters with the LASSO”
A few month ago I posted about market basket analysis (see Customers who bought…), in this post we will see another form of it, done with Logistic Regression, so read on…
Continue reading “Learning Data Science: The Supermarket knows you are pregnant before your Dad does”
A few months ago I published a quite popular post on Clustering the Bible… one well known clustering algorithm is k-means. If you want to learn how k-means works and how to apply it in a real-world example, read on…
Continue reading “Learning Data Science: Understanding and Using k-means Clustering”
One of the most notoriously difficult subjects in statistics is the concept of statistical tests. We will explain the ideas behind it step by step to give you some intuition on how to use (and misuse) it, so read on…
Continue reading “From Coin Tosses to p-Hacking: Make Statistics Significant Again!”
The area of combinatorics, the art of systematic counting, is dreaded territory for many people, so let us bring some light into the matter: in this post we will explain the difference between permutations and combinations, with and without repetitions (also called replacements), will calculate the number of possibilities and present efficient R code to enumerate all of them, so read on…
Continue reading “Learning R: Permutations and Combinations with Base R”
A few months ago I published a post on recursion: To understand Recursion you have to understand Recursion…. In this post we will see how to use recursion to fill free areas of an image with colour, the caveats of recursion and how to transform a recursive algorithm into a loop-based version using a queue – so read on…
Continue reading “Learning R: Painting with Fire”
There are a million reasons to learn R (see e.g. Why R for Data Science – and not Python?), but where to start? I present to you the ultimate introduction to bring you up to speed! So read on…
Continue reading “Learning R: The Ultimate Introduction (incl. Machine Learning!)”
Bavaria is known for its famous Oktoberfest… and within Germany also for its presumably difficult Abitur, a qualification granted by university-preparatory schools in Germany.
A mandatory part for all students is maths. This year many students protested that the maths part was way too hard, they even started an online petition with more than seventy thousand supporters at this time of writing!
It is not clear yet whether their marks will be adjusted upwards, the ministry of education is investigating the case. As a professor in Bavaria who also teaches statistics I will take the opportunity to share with you an actual question from the original examination with solution, so read on…
Continue reading “Was the Bavarian Abitur too hard this time?”
One of the problems of navigating an autonomous car through a city is to extract robust signals in the face of all the noise that is present in the different sensors. Just taking something like an arithmetic mean of all the data points could possibly end in a catastrophe: if a part of a wall looks similar to the street and the algorithm calculates an average trajectory of the two this would end in leaving the road and possibly crashing into pedestrians. So we need some robust algorithm to get rid of the noise. The area of statistics that especially deals with such problems is called robust statistics and the methods used therein robust estimation.
Continue reading “Separating the Signal from the Noise: Robust Statistics for Pedestrians”