Introduction To Probability For Data Science

Author : Stanley H. Chan
Publisher : Michigan Publishing Services
Page : 0 pages
File Size : 41,5 Mb
Release : 2021
Category : Computer science and applied mathematics
ISBN : 1607857464

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Introduction to Probability for Data Science by Stanley H. Chan Pdf

"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.

Author : Roman Vershynin
Publisher : Cambridge University Press
Page : 299 pages
File Size : 41,6 Mb
Release : 2018-09-27
Category : Business & Economics
ISBN : 9781108415194

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High-Dimensional Probability by Roman Vershynin Pdf

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Author : Maurits Kaptein,Edwin van den Heuvel
Publisher : Springer Nature
Page : 342 pages
File Size : 40,6 Mb
Release : 2022-02-02
Category : Computers
ISBN : 9783030105310

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Statistics for Data Scientists by Maurits Kaptein,Edwin van den Heuvel Pdf

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.

Author : Juana Sánchez
Publisher : Cognella Academic Publishing
Page : 341 pages
File Size : 49,6 Mb
Release : 2019-05-31
Category : Computer science
ISBN : 1516532708

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Probability for Data Scientists (First Edition) by Juana Sánchez Pdf

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.

Author : F.M. Dekking,C. Kraaikamp,H.P. Lopuhaä,L.E. Meester
Publisher : Springer Science & Business Media
Page : 488 pages
File Size : 44,9 Mb
Release : 2006-03-30
Category : Mathematics
ISBN : 9781846281686

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A Modern Introduction to Probability and Statistics by F.M. Dekking,C. Kraaikamp,H.P. Lopuhaä,L.E. Meester Pdf

Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books

Author : Charles Miller Grinstead,James Laurie Snell
Publisher : American Mathematical Soc.
Page : 536 pages
File Size : 41,7 Mb
Release : 1997
Category : Mathematics
ISBN : 0821807498

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Introduction to Probability by Charles Miller Grinstead,James Laurie Snell Pdf

This text is designed for an introductory probability course at the university level for undergraduates in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a firm understanding of the subject.

Author : Norman Matloff
Publisher : CRC Press
Page : 295 pages
File Size : 47,5 Mb
Release : 2019-06-21
Category : Business & Economics
ISBN : 9780429687112

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Probability and Statistics for Data Science by Norman Matloff Pdf

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.

Author : Stanley H. Chan
Publisher : Unknown
Page : 0 pages
File Size : 40,7 Mb
Release : 2021
Category : Electronic
ISBN : 1607857472

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Introduction to Probability for Data Science by Stanley H. Chan Pdf

Author : Sheldon M. Ross
Publisher : Unknown
Page : 532 pages
File Size : 55,6 Mb
Release : 1987
Category : Mathematics
ISBN : UOM:39015013026342

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Introduction to Probability and Statistics for Engineers and Scientists by Sheldon M. Ross Pdf

Elements of probability; Random variables and expectation; Special; random variables; Sampling; Parameter estimation; Hypothesis testing; Regression; Analysis of variance; Goodness of fit and nonparametric testing; Life testing; Quality control; Simulation.

Author : Maurits Kaptein,Edwin van den Heuvel
Publisher : Springer
Page : 321 pages
File Size : 47,8 Mb
Release : 2022-02-27
Category : Computers
ISBN : 303010530X

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Statistics for Data Scientists by Maurits Kaptein,Edwin van den Heuvel Pdf

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.

Author : Dimitri Bertsekas,John N. Tsitsiklis
Publisher : Athena Scientific
Page : 544 pages
File Size : 44,6 Mb
Release : 2008-07-01
Category : Mathematics
ISBN : 9781886529236

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Introduction to Probability by Dimitri Bertsekas,John N. Tsitsiklis Pdf

An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields. This is the currently used textbook for an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students, and for a leading online class on the subject. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains a number of more advanced topics, including transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes, Bayesian inference, and an introduction to classical statistics. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis is explained intuitively in the main text, and then developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.

Author : G. Jay Kerns
Publisher : Lulu.com
Page : 388 pages
File Size : 48,9 Mb
Release : 2010-01-10
Category : Education
ISBN : 9780557249794

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Introduction to Probability and Statistics Using R by G. Jay Kerns Pdf

This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.

Author : Joseph K. Blitzstein,Jessica Hwang
Publisher : CRC Press
Page : 620 pages
File Size : 53,6 Mb
Release : 2019-02-08
Category : Mathematics
ISBN : 9780429766749

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Introduction to Probability, Second Edition by Joseph K. Blitzstein,Jessica Hwang Pdf

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment. The second edition adds many new examples, exercises, and explanations, to deepen understanding of the ideas, clarify subtle concepts, and respond to feedback from many students and readers. New supplementary online resources have been developed, including animations and interactive visualizations, and the book has been updated to dovetail with these resources. Supplementary material is available on Joseph Blitzstein’s website www. stat110.net. The supplements include: Solutions to selected exercises Additional practice problems Handouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110. Links to lecture videos available on ITunes U and YouTube There is also a complete instructor's solutions manual available to instructors who require the book for a course.

Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Page : 395 pages
File Size : 53,9 Mb
Release : 2017-05-10
Category : Computers
ISBN : 9781491952917

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce Pdf

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

Author : Rafael A. Irizarry
Publisher : CRC Press
Page : 794 pages
File Size : 45,8 Mb
Release : 2019-11-20
Category : Mathematics
ISBN : 9781000708035

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Introduction to Data Science by Rafael A. Irizarry Pdf

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.