Data Science in der Praxis

The ideal introduction to Data Science for practitioners! Whether you have a background in mathematics or not, you will gain a comprehensive overview necessary for your projects. This way, you can unlock the treasure that data can represent when queried correctly. You will learn relevant analytical methods, receive an introduction to the R programming language, and discover how to implement machine learning. This includes essential tools like notebooks that make Data Science programming accessible today. And because technology alone is not enough, the book also addresses project execution challenges, explores various application fields, and does not overlook ethical considerations. It is filled with examples, troubleshooting tips, decision-making aids, and additional practical advice.

Contents include:

Getting started with R and RStudio
Basic concepts of statistics
Preparation: cleaning and transforming data
k-Means clustering
Linear and nonlinear regression
Predictions, clustering, classification
Tips and tools for all project phases
Deploying your application as a REST API
Utilizing AI and machine learning
Anomaly detection, market basket analysis, and many other use cases
Machine Learning: training models correctly.