Statistical Foundations Of Data Science

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Statistical Foundations Of Data Science

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Author : Jianqing Fan
language : en
Publisher: CRC Press
Release Date : 2020-09-21

Statistical Foundations Of Data Science written by Jianqing Fan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-21 with Mathematics categories.

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Foundations Of Data Science

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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23

Foundations Of Data Science written by Avrim Blum 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 2020-01-23 with Computers categories.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Foundations Of Statistics For Data Scientists

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Author : Alan Agresti
language : en
Publisher: CRC Press
Release Date : 2021-11-29

Foundations Of Statistics For Data Scientists written by Alan Agresti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-29 with Business & Economics categories.

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds.rwth-aachen.de/) has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.

Statistical Foundations Reasoning And Inference

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Author : Göran Kauermann
language : en
Publisher: Springer Nature
Release Date : 2021-09-30

Statistical Foundations Reasoning And Inference written by Göran Kauermann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-30 with Mathematics categories.

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

Statistical Foundations Reasoning And Inference

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Author : Göran Kauermann
language : en
Publisher:
Release Date : 2021

Statistical Foundations Reasoning And Inference written by Göran Kauermann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

Algorithms For Data Science

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Author : Brian Steele
language : en
Publisher: Springer
Release Date : 2016-12-25

Algorithms For Data Science written by Brian Steele and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-25 with Computers categories.

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

Mathematical Foundations Of Data Science Using R

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Author : Frank Emmert-Streib
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2020-06-08

Mathematical Foundations Of Data Science Using R written by Frank Emmert-Streib and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-08 with Computers categories.

In order best exploit the incredible quantities of data being generated in most diverse disciplines data sciences increasingly gain worldwide importance. The book gives the mathematical foundations to handle data properly. It introduces basics and functionalities of the R programming language which has become the indispensable tool for data sciences. Thus it delivers the reader the skills needed to build own tool kits of a modern data scientist.

Data Science

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Author : Matthias Plaue
language : en
Publisher: Springer Nature
Release Date : 2023-08-31

Data Science written by Matthias Plaue and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-31 with Computers categories.

This textbook provides an easy-to-understand introduction to the mathematical concepts and algorithms at the foundation of data science. It covers essential parts of data organization, descriptive and inferential statistics, probability theory, and machine learning. These topics are presented in a clear and mathematical sound way to help readers gain a deep and fundamental understanding. Numerous application examples based on real data are included. The book is well-suited for lecturers and students at technical universities, and offers a good introduction and overview for people who are new to the subject. Basic mathematical knowledge of calculus and linear algebra is required.

Statistical Data Analytics

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Author : Walter W. Piegorsch
language : en
Publisher: John Wiley & Sons
Release Date : 2015-12-21

Statistical Data Analytics written by Walter W. Piegorsch 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 2015-12-21 with Mathematics categories.

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

Foundations Of Data Science And Statistics

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Author : Woody R Clermont
language : en
Publisher:
Release Date : 2025-10-05

Foundations Of Data Science And Statistics written by Woody R Clermont and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-05 with Mathematics categories.

Learn statistics for beginners, business, and data science - without the confusion. Whether you're a student, professional, or entrepreneur, Foundations of Data Science and Statistics: Analytics Made Simple gives you a clear, practical path to mastering the most important statistical concepts. This guide blends theory and application so you can use statistics to solve real-world problems, make data-driven decisions, and improve your analytical skills. Includes sections about econometrics, ARIMA, ANOVA, clustering, machine learning, neural networks, and so much more. Inside, you'll find: Beginner-friendly explanations of descriptive statistics, probability basics, and inferential methods. Practical statistics techniques you can apply in business, economics, and everyday decision-making. Step-by-step guidance on statistical analysis for data science projects. Examples and exercises designed for self-study, classroom learning, or workplace application. Applied statistics tools for business analytics, economics research, and performance improvement. No complicated jargon. No overwhelming formulas. Just clear, easy-to-follow lessons that help you build confidence in statistics and analytics - even if you've never enjoyed math before. If you need a statistics workbook, a statistics crash course, or a practical statistics guide you can use right away, this book delivers. Whether your goal is to pass a statistics course, analyze business data, or explore data science, you'll learn skills you can apply immediately. Perfect for: Beginners who want a quick yet thorough introduction to statistics. Business professionals who need to understand data. Students in economics, data science, or related fields. Self-learners seeking a step-by-step guide to statistics and analytics. Master statistics for data science, business, and economics - and turn numbers into knowledge.