Practical Statistics For Data Scientists 50 Essential Concepts 2nd Edition
Download Practical Statistics For Data Scientists 50 Essential Concepts 2nd Edition PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Practical Statistics For Data Scientists 50 Essential Concepts 2nd Edition book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Practical Statistics for Data Scientists
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
Practical Statistics for Data Scientists
Author: Peter Bruce
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-05-10
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various 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 programming language, 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
The Revolution Of AI Agents
" Artificial intelligence is radically transforming the global labor landscape. The Revolution of AI Agents and Their Impact on Global Employment explores how automation and machine learning are reshaping entire industries, from customer service to manufacturing, healthcare, and finance. With a detailed approach, the book examines different types of AI agents, their historical evolution, and their ability to process real-time data, enabling businesses to optimize operations with unprecedented precision. As AI agents become more sophisticated, the automation of routine tasks creates both opportunities and uncertainty. This book addresses the growing concern over the disappearance of traditional jobs while also highlighting new opportunities in technology and data analysis. Through practical examples and case studies, it explores business adaptation strategies, the ethical implications of AI, and the public policies needed to mitigate its negative societal effects. More than a technical analysis, this book invites deep reflection on AI's role in redefining work and the need for a proactive approach to ensure an inclusive future. How can we prepare for a world where human-machine collaboration becomes the norm? The Revolution of AI Agents and Their Impact on Global Employment provides an essential guide to understanding the changes already taking place and the challenges ahead. "