Introduction To Probability For Data Science
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Introduction to Probability for Data Science
Author | : Stanley H. Chan |
Publsiher | : Michigan Publishing Services |
Total Pages | : 0 |
Release | : 2021 |
Genre | : Computer science and applied mathematics |
ISBN | : 1607857464 |
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"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.
High Dimensional Probability
Author | : Roman Vershynin |
Publsiher | : Cambridge University Press |
Total Pages | : 299 |
Release | : 2018-09-27 |
Genre | : Business & Economics |
ISBN | : 9781108415194 |
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An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Probability for Data Scientists First Edition
Author | : Juana Sánchez |
Publsiher | : Cognella Academic Publishing |
Total Pages | : 341 |
Release | : 2019-05-31 |
Genre | : Computer science |
ISBN | : 1516532708 |
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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.
Statistics for Data Scientists
Author | : Maurits Kaptein,Edwin van den Heuvel |
Publsiher | : Springer Nature |
Total Pages | : 342 |
Release | : 2022-02-02 |
Genre | : Computers |
ISBN | : 9783030105310 |
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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
Author | : Norman Matloff |
Publsiher | : CRC Press |
Total Pages | : 295 |
Release | : 2019-06-21 |
Genre | : Business & Economics |
ISBN | : 9780429687112 |
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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.
Statistics for Data Scientists
Author | : Maurits Kaptein,Edwin van den Heuvel |
Publsiher | : Springer |
Total Pages | : 321 |
Release | : 2022-02-27 |
Genre | : Computers |
ISBN | : 303010530X |
Download Statistics for Data Scientists Book in PDF, Epub and Kindle
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
Author | : Charles Miller Grinstead,James Laurie Snell |
Publsiher | : American Mathematical Soc. |
Total Pages | : 536 |
Release | : 1997 |
Genre | : Mathematics |
ISBN | : 0821807498 |
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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.
Introduction to Probability and Statistics for Science Engineering and Finance
Author | : Walter A. Rosenkrantz |
Publsiher | : CRC Press |
Total Pages | : 680 |
Release | : 2008-07-10 |
Genre | : Mathematics |
ISBN | : 9781584888130 |
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Integrating interesting and widely used concepts of financial engineering into traditional statistics courses, Introduction to Probability and Statistics for Science, Engineering, and Finance illustrates the role and scope of statistics and probability in various fields. The text first introduces the basics needed to understand and create