Looking for interesting data sets to train your models on or create hypothesis?
Here some well organized lists of free (most of them) and...
What does a Data Scientist do? - Facts
From the post about the CRISP-DM we know what phases a data science project consist of. This post analyzes the time spend on the different...
Association Analysis
The typical question behind Association Analysis or often also called Basket Analysis is:Which products are bought together? This question...
Anomaly detection
A classical Data Science problem is to identify outliers, meaning anomalous behaviours or unexpected high or low values. These unusual...
Principal Component Analysis
Typical Data Science problems have a huge amout of input features, they are often deciding factors for model performance and make it difficult...
Data Scientists - Skills
It is obvious that a data scientist should have an interest in data, a strong analytical, mathematical background and a good intuition on...
The Data Mining Process
The Cross Industry Standard Process for Data Mining (CRISP-DM) is a process model that describes the different steps data scientists use...
Neural Networks - Basics
What are neural networks?
Neural networks are algorithms that get input data and adapt parameters on an internal (unvisible) model so it works...
k-Means - how to choose k
After you understood how the basics of the k-Means-Algorithm works, you will be wondering:
Into how much clusters should I devide the...
k-Means - How to initialize
When you study the k-Means-Algorithm and understand, how it works, a natural question that ariese is:
How can you choose the starting points...
The k-Means Algorithm - Basics
Unsupervised Learning tries to find structures in datasets, one method to do so is by clustering the data. The most popular and widely used...
Machine Learning
What does "machine learning" mean, how can a computer "learn"?
The term "learning" is used in this context to refer to the fact...
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