Introduction To Probability For Data Science
DOWNLOAD
Download Introduction To Probability For Data Science PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Introduction To Probability For Data Science 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. If the content not found or just blank you must refresh this page
Introduction To Probability For Data Science
DOWNLOAD
Author : Stanley H. Chan
language : en
Publisher: Michigan Publishing Services
Release Date : 2021
Introduction To Probability For Data Science written by Stanley H. Chan and has been published by Michigan Publishing Services this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Computer science and applied mathematics categories.
"Probability is one of the most interesting subjects in electrical engineering and computer science. It bridges our favorite engineering principles to the practical reality, a world that is full of uncertainty. However, because probability is such a mature subject, the undergraduate textbooks alone might fill several rows of shelves in a library. When the literature is so rich, the challenge becomes how one can pierce through to the insight while diving into the details. For example, many of you have used a normal random variable before, but have you ever wondered where the 'bell shape' comes from? Every probability class will teach you about flipping a coin, but how can 'flipping a coin' ever be useful in machine learning today? Data scientists use the Poisson random variables to model the internet traffic, but where does the gorgeous Poisson equation come from? This book is designed to fill these gaps with knowledge that is essential to all data science students." -- Preface.
Statistics For Data Scientists
DOWNLOAD
Author : Maurits Kaptein
language : en
Publisher: Springer
Release Date : 2022-02-27
Statistics For Data Scientists written by Maurits Kaptein and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-27 with Computers categories.
This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.
Introduction To Probability And Statistics For Data Scientists With R
DOWNLOAD
Author : Ronald D. Fricker, Jr.
language : en
Publisher: CreateSpace
Release Date : 2014-05-25
Introduction To Probability And Statistics For Data Scientists With R written by Ronald D. Fricker, Jr. and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-25 with Mathematics categories.
This is the first three chapters of a textbook for data scientists who want to improve how they work with, analyze, and extract information from data. The focus of the textbook is how to appropriately apply statistical methods, both simple and sophisticated, to 21st century data and problems. This book contains the first three chapters: Introduction -- Data Science and Statistics, Descriptive Statistics, and Data Visualization -- as well as the book front matter. Subsequent chapters will be published in 3- to 5-chapter sets as they become available.The textbook is intended for current and future data scientists, and for anyone interested in deriving information from data. It requires some mathematical sophistication on the part of the reader, as well as comfort using computers and statistical software.Data science is a new field that has arisen to exploit the proliferation of data in the modern world. Mathematical statistics dates back to the mid-18th century, where the field began as the systematic collection of population and economic data by nations. The modern practice of statistics – which includes the collection, summarization, and analysis of data – dates to the early 20th century. Today statistical methods are widely used by governments, businesses and other organizations, as well as by all scientific disciplines.It has been said that a data scientist must have a better grasp of statistics than the average computer scientist and a better grasp of programming than the average statistician. This book will give data scientists a firm foundation in statistics.
Statistics For Data Scientists
DOWNLOAD
Author : Maurits Kaptein
language : en
Publisher: Springer Nature
Release Date : 2022-02-02
Statistics For Data Scientists written by Maurits Kaptein and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Computers categories.
This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.
Probability And Statistics For Data Science
DOWNLOAD
Author : Norman Matloff
language : en
Publisher: CRC Press
Release Date : 2019-06-21
Probability And Statistics For Data Science written by Norman Matloff and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-21 with Business & Economics categories.
Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
High Dimensional Probability
DOWNLOAD
Author : Roman Vershynin
language : en
Publisher: Cambridge University Press
Release Date : 2018-09-27
High Dimensional Probability written by Roman Vershynin and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-27 with Business & Economics categories.
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Probability For Data Scientists First Edition
DOWNLOAD
Author : Juana Sánchez
language : en
Publisher: Cognella Academic Publishing
Release Date : 2019-05-31
Probability For Data Scientists First Edition written by Juana Sánchez and has been published by Cognella Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-31 with Computer science categories.
Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to inferences about specific populations. The book is divided into two parts. Part I introduces readers to fundamental definitions, theorems, and methods within the context of discrete sample spaces. It addresses the origin of the mathematical study of probability, main concepts in modern probability theory, univariate and bivariate discrete probability models, and the multinomial distribution. Part II builds upon the knowledge imparted in Part I to present students with corresponding ideas in the context of continuous sample spaces. It examines models for single and multiple continuous random variables and the application of probability theorems in statistics. Probability for Data Scientists effectively introduces students to key concepts in probability and demonstrates how a small set of methodologies can be applied to a plethora of contextually unrelated problems. It is well suited for courses in statistics, data science, machine learning theory, or any course with an emphasis in probability. Numerous exercises, some of which provide R software code to conduct experiments that illustrate the laws of probability, are provided in each chapter.
Introduction To Probability And Statistics For Data Science
DOWNLOAD
Author : Steven E. Rigdon
language : en
Publisher: Cambridge University Press
Release Date : 2024-08-31
Introduction To Probability And Statistics For Data Science written by Steven E. Rigdon and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-31 with Computers categories.
Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises.
Probability For Data Scientists
DOWNLOAD
Author : Juana Sanchez
language : en
Publisher: Cognella Academic Publishing
Release Date : 2019-07
Probability For Data Scientists written by Juana Sanchez and has been published by Cognella Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07 with categories.
Probability for Data Scientists provides students with a mathematically sound yet accessible introduction to the theory and applications of probability. Students learn how probability theory supports statistics, data science, and machine learning theory by enabling scientists to move beyond mere descriptions of data to inferences about specific populations. The book is divided into two parts. Part I introduces readers to fundamental definitions, theorems, and methods within the context of discrete sample spaces. It addresses the origin of the mathematical study of probability, main concepts in modern probability theory, univariate and bivariate discrete probability models, and the multinomial distribution. Part II builds upon the knowledge imparted in Part I to present students with corresponding ideas in the context of continuous sample spaces. It examines models for single and multiple continuous random variables and the application of probability theorems in statistics. Probability for Data Scientists effectively introduces students to key concepts in probability and demonstrates how a small set of methodologies can be applied to a plethora of contextually unrelated problems. It is well suited for courses in statistics, data science, machine learning theory, or any course with an emphasis in probability. Numerous exercises, some of which provide R software code to conduct experiments that illustrate the laws of probability, are provided in each chapter.
Probability With R
DOWNLOAD
Author : Jane M. Horgan
language : en
Publisher: John Wiley & Sons
Release Date : 2019-12-04
Probability With R written by Jane M. Horgan and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-04 with Mathematics categories.
Provides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely down-loadable statistical programming language R is used throughout the text, not only as a tool for calculation and data analysis, but also to illustrate concepts of probability and to simulate distributions. The examples in Probability with R: An Introduction with Computer Science Applications, Second Edition cover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; estimating the reliability of components and systems; evaluating algorithms and queuing systems. Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. This second edition includes: improved R code throughout the text, as well as new procedures, packages and interfaces; updated and additional examples, exercises and projects covering recent developments of computing; an introduction to bivariate discrete distributions together with the R functions used to handle large matrices of conditional probabilities, which are often needed in machine translation; an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data; a new section on spam filtering using Bayes theorem to develop the filters; an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud; use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem. The book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book. Primarily addressed to students of computer science and related areas, Probability with R: An Introduction with Computer Science Applications, Second Edition is also an excellent text for students of engineering and the general sciences. Computing professionals who need to understand the relevance of probability in their areas of practice will find it useful.