Hamlet: Do you see yonder cloud that’s almost in shape of a camel?
Polonius: By the mass, and ’tis like a camel, indeed.
Hamlet: Methinks it is like a weasel.
from Hamlet by William Shakespeare
One of the classic examples in data science (called data mining at the time) is the beer and diapers example: when a big supermarket chain started analyzing their sales data they encountered not only trivial patterns, like toothbrushes and toothpaste being bought together but also quite strange combinations like beer and diapers. Now, the trivial ones are reassuring that the method works but what about the more extravagant ones? Does it mean that young parents are alcoholics? Or that instead of breastfeeding they give their babies beer? Obviously, they had to get to the bottom of this.
Continue reading “Customers who bought…”
One of the topics that is totally hyped at the moment is obviously Artificial Intelligence or AI for short. There are many self-proclaimed experts running around trying to sell you the stuff they have been doing all along under this new label.
When you ask them what AI means you will normally get some convoluted explanations (which is a good sign that they don’t get it themselves) and some “success stories”. The truth is that many of those talking heads don’t really know what they are talking about, yet happen to have a friend who knows somebody who picked up a book at the local station bookshop… ok, that was nasty but unfortunately often not too far away from the truth.
So, what is AI really? This post tries to give some guidance, so read on!
Continue reading “So, what is AI really?“
Data Science is all about building good models, so let us start by building a very simple model: we want to predict monthly income from age (in a later post we will see that age is indeed a good predictor for income).
Continue reading “Learning Data Science: Modelling Basics”
Everything “neural” is (again) the latest craze in machine learning and artificial intelligence. Now what is the magic of artificial neural networks (ANNs)?
Continue reading “Understanding the Magic of Neural Networks”
During this time of year, there is obviously a lot of talk about the Bible. As most people know the New Testament comprises four different Gospels written by anonymous authors 40 to 70 years after Jesus’ supposed crucifixion.
Unfortunately, we have lost all of the originals but only retained copies of copies of copies (and so on) which date back hundreds of years after they were written in all kinds of different versions (renowned Biblical scholar Professor Bart Ehrmann states that there are more versions of the New Testament than there are words in the New Testament). Just as a fun fact: there are many more Gospels but only those four were included in the “official” Bible.
Continue reading “Clustering the Bible”
Principal Component Analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.
PCA is a concept that is traditionally hard to grasp so instead of giving you the n’th mathematical derivation I will provide you with some intuition.
Continue reading “Intuition for Principal Component Analysis (PCA)”
There are literally hundreds of programming languages out there, e.g. the whole alphabet of one letter programming languages is taken. In the area of data science, there are two big contenders: R and Python. Now, why is this blog about R and not Python?
Continue reading “Why R for Data Science – and Not Python!”
We already saw the power of the OneR package in the preceding post, One Rule (OneR) Machine Learning Classification in under One Minute. Here we want to give some more examples to gain some fascinating, often counter-intuitive, insights.
Continue reading “OneR – Fascinating Insights through Simple Rules”
Here I give a very short introduction on how to use the
OneR Machine Learning package for the hurried, so buckle up!
Continue reading “One Rule (OneR) Machine Learning Classification in under One Minute”