000			03611nam a22003857a 4500
003			OSt
005			20260630141226.0
008			260630s20252026-uka|||er|||| 001 0 eng d
020			_a9781836831440 [hardbound]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
100			_aRivers, Julian, _eauthor.
245			_aStatistics for data science / _cby Julian Rivers.
250			_aFirst edition.
260			_aUnited Kingdom : _bVintage Press Ltd., _cc2025.
300			_av, 264 pages : _bcolor illustrations ; _c24 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505			_aContents: 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.
520			_a"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
521			_aAdult
541			_aPurchased _xOrtega, Eric _yCollege of Computer Studies _zComputer Science
546			_aText in English
650			_aMathematical statistics.
650			_aData science.
650			_aBig data.
650			_2Information visualization.
650			_aElectronic data processing.
650			_aMathematical statistics _xData processing.
650			_aMathematical statistics _vTextbooks.
942			_2ddc _cBK
998			_cJanna [new] _d06/30/2026
999			_c15442 _d15442