Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you’ll learn:

• Why exploratory data analysis is a key preliminary step in data science
• How random sampling can reduce bias and yield a higher-quality dataset, even with big data
• How the principles of experimental design yield definitive answers to questions
• How to use regression to estimate outcomes and detect anomalies
• Key classification techniques for predicting which categories a record belongs to
• Statistical machine learning methods that "learn" from data
• Unsupervised learning methods for extracting meaning from unlabeled data

Statistische Konzepte aus der Perspektive von Data Scientists erläutert

• Das Buch stellt die Verbindung zwischen nützlichen statistischen Prinzipien und der heutigen Datenanalyse-Praxis her


• Ermöglicht Data Scientists, ihr Wissen über Statistik auf ein neues Level zu bringen


• Übersetzung der 2. Auflage des US-Bestsellers mit Beispielen in Python und R

Statistische Methoden sind ein zentraler Bestandteil der Arbeit mit Daten, doch nur wenige Data Scientists haben eine formale statistische Ausbildung. In Kursen und Büchern über die Grundlagen der Statistik wird das Thema aber selten aus der Sicht von Data Scientists behandelt. Viele stellen daher fest, dass ihnen eine tiefere statistische Perspektive auf ihre Daten fehlt.
Dieses praxisorientierte Handbuch mit zahlreichen Beispielen in Python und R erklärt Ihnen, wie Sie verschiedene statistische Methoden speziell in den Datenwissenschaften anwenden. Es zeigt Ihnen auch, wie Sie den falschen Gebrauch von statistischen Methoden vermeiden können, und gibt Ratschläge, welche statistischen Konzepte für die Datenwissenschaften besonders relevant sind. Wenn Sie mit R oder Python vertraut sind, ermöglicht diese zugängliche, gut lesbare Referenz es Ihnen, Ihr statistisches Wissen für die Praxis deutlich auszubauen.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.

This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

• A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
• A new section on ethical issues in data mining
• Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
• More than a dozen case studies demonstrating applications for the data mining techniques described
• End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
• A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

“This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.”

—Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R