AI-Assisted Statistics for Data Scientists: 50+ Essential Concepts Using R and Python - Couverture souple

Bruce, Peter; Bruce, Andrew; Gedeck, Peter

Couverture souple

ISBN 13 : 9798341666283

Editeur : O'Reilly Media, 2026

Synopsis

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 third edition of this popular guide expands its practical foundations in R and Python into the modern AI toolkit, with new chapters on neural networks, deep learning, and large language models. Generative AI is integrated throughout, showing how tools such as ChatGPT, Claude, and Gemini work, and how they can support real-world statistical workflows.

This book highlights concepts that matter most when working with data, building predictive models, and deploying AI responsibly. If you're comfortable with R or Python and have had some exposure to basic statistics, this concise reference will boost your statistical literacy, your understanding of how AI works, and your confidence in real-world data science and AI projects.

• Conduct exploratory analysis of data to improve quality and model outcomes
• Apply sampling and experimental design to reduce bias and answer questions with clarity
• Use regression to understand data-generating processes and detect anomalies
• Build predictive models using classification, clustering, and unsupervised learning with unbalanced data

A propos du livre

À propos de l'auteur

Andrew Bruce, Principal Research Scientist at Amazon, has over 30 years of experience in statistics and data science in academia, government and business. The co-author of Applied Wavelet Analysis with S-PLUS, he earned his bachelor's degree at Princeton, and PhD in statistics at the University of Washington. Peter Gedeck, Senior Data Scientist at Collaborative Drug Discovery and Lecturer at the School of Data Science, University of Virginia, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Co-author of several books in statistics and analytics, he earned a PhD in Chemistry from the University of Erlangen-Nürnberg in Germany and studied Mathematics at FernUniversität Hagen, Germany

Détails de l'édition

• Éditeur: O'Reilly Media
• Date d'édition: 2026
• Langue: anglais
• ISBN 13: 9798341666283
• Reliure: Broché
• Numéro d'édition: 3
• Nombre de pages: 400
• Coordonnées du fabricant: non disponible
• Personne responsable: GPSR Kontakt; gpsr@libri.de; ; Europaallee 1; Bad Hersfeld; 36244; Allemagne