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Statistics for data science / by Julian Rivers.

By: Rivers, Julian [author. ]Material type: Text

TextPublisher: United Kingdom : Vintage Press Ltd., c2025Edition: First editionDescription: v, 264 pages : color illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volume ISBN: 9781836831440 [hardbound]Subject(s): Mathematical statistics | Data science | Big data | | Electronic data processing | Mathematical statistics -- Data processing | Mathematical statistics -- Textbooks

Contents:

Contents: 1. Introduction to statistics and data science -- 2. Descriptive statistics -- 3. Probability concepts -- 4. Sampling and sampling distributions -- 5. Estimation and confidence intervals -- 6. Hypothesis testing -- 7. Analysis of variance (ANOVA) -- 8. Statistical inference in data science -- 9. Multivariate statistics -- 10. Time series analysis.

Summary: "In recent years, data has emerged as one of the most valuable resources in the world, transforming industries, research fields, and the ways in which decisions are made. Data science has risen to the forefront of this transformation, combining statistical analysis, machine learning, and domain expertise to extract insights and drive innovation. In this book, Statistics for Data Science, the aim is to bridge the essential knowledge of statistics with the practical applications necessary for today's data-driven world. Statistics is the foundation upon which data science is built, offering tools to explore, analyze, and interpret data in ways that support accurate, meaningful, and actionable conclusions. The objective of this book is to introduce readers to fundamental statistical concepts, methodologies, and techniques, with a focus on their applications in data science. Topics include descriptive statistics, inferential statistics, probability, regression analysis, hypothesis testing, and more advanced areas such as time series analysis, multivariate statistics, and statistical quality control. Written with data science practitioners, students, and professionals in mind, this book combines theory with practical examples and exercises to reinforce understanding and encourage hands-on learning. Each chapter not only provides theoretical knowledge but also includes applied scenarios, demonstrating how statistical methods are used in real-world data science problems. Whether you are just beginning your journey in data science or seeking to deepen your statistical knowledge, this book is designed to be both a foundational guide and a practical resource. I hope that 'Statistics for Data Science serves as a valuable tool, inspiring readers to harness the power of statistics to make better, more informed decisions. Thank you for embarking on this journey, and I look forward to exploring the world of statistics with you." —Preface

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Item type	Current location	Call number	Status	Date due	Barcode
Subject Reference	College Library Subject Reference	519.502 R52 2025 (Browse shelf)	Available		3UCBL000029707

Includes bibliographical references and index.

"In recent years, data has emerged as one of the most valuable resources in the world, transforming industries, research fields, and the ways in which decisions are made. Data science has risen to the forefront of this transformation, combining statistical analysis, machine learning, and domain expertise to extract insights and drive innovation. In this book, Statistics for Data Science, the aim is to bridge the essential knowledge of statistics with the practical applications necessary for today's data-driven world.
Statistics is the foundation upon which data science is built, offering tools to explore, analyze, and interpret data in ways that support accurate, meaningful, and actionable conclusions. The objective of this book is to introduce readers to fundamental statistical concepts, methodologies, and techniques, with a focus on their applications in data science. Topics include descriptive statistics, inferential statistics, probability, regression analysis, hypothesis testing, and more advanced areas such as time series analysis, multivariate statistics, and statistical quality control.
Written with data science practitioners, students, and professionals in mind, this book combines theory with practical examples and exercises to reinforce understanding and encourage hands-on learning. Each chapter not only provides theoretical knowledge but also includes applied scenarios, demonstrating how statistical methods are used in real-world data science problems.
Whether you are just beginning your journey in data science or seeking to deepen your statistical knowledge, this book is designed to be both a foundational guide and a practical resource. I hope that 'Statistics for Data Science serves as a valuable tool, inspiring readers to harness the power of statistics to make better, more informed decisions. Thank you for embarking on this journey, and I look forward to exploring the world of statistics with you." —Preface