AI is THE topic of the future, but unfortunately there is still a lot of uncertainty. Here are five tips for CEOs on what to do now.
Forecasts for intermittent data are often difficult. Torben examines whether the Statsforecast package can be used and describes which evaluation methods are useful.
Testing in data science projects is often neglected. In this blog post, you can find out what the consequences are and how it can be done better.
Successful AI implementation depends to a large extent on the corporate culture.
This article sheds light on how fundamental values, norms and behaviors in the company support the introduction of AI.
In this blog post, Michael describes why data storytelling goes far beyond pure data quality and how it enables companies to realize the full potential of their data.
The term “preventive maintenance” is often used in connection with the optimization of maintenance plans.
What is information entropy? Let’s assume that in summer we want to send our friends a telegram from Hamburg to St. Petersburg every day about the weather conditions: sunny, slightly cloudy, overcast, rainy.
This post presents a modern approach to estimate an individual probability curve for the acceptance of an offer from historical accepted and rejected transaction data.
Publications are the basis of scientific work: scientists and practitioners all over the world use these results.
Frequent problems that we are confronted with can be traced back to a (multidimensional) regression or a classification. Starting from a set of characteristics, regression attempts to represent the dependency between the characteristics and a target variable as a function.
Based on Bayes’ theorem, Bayesian statistics has developed, which is used in the context of inductive statistics and machine learning to estimate parameters and test hypotheses.
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