Statistical Learning And Data Sciences
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An Introduction to Statistical Learning
Author | : Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani |
Publsiher | : Springer Science & Business Media |
Total Pages | : 426 |
Release | : 2013-06-24 |
Genre | : Mathematics |
ISBN | : 9781461471387 |
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Statistical Learning and Data Science
Author | : Mireille Gettler Summa,Leon Bottou,Bernard Goldfarb,Fionn Murtagh,Catherine Pardoux,Myriam Touati |
Publsiher | : Chapman & Hall/CRC |
Total Pages | : 0 |
Release | : 2019-09-23 |
Genre | : Data mining |
ISBN | : 0367381893 |
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Driven by a vast range of applications, data analysis and learning from data are vibrant areas of research. Various methodologies, including unsupervised data analysis, supervised machine learning, and semi-supervised techniques, have continued to develop to cope with the increasing amount of data collected through modern technology. With a focus on applications, this volume presents contributions from some of the leading researchers in the different fields of data analysis. Synthesizing the methodologies into a coherent framework, the book covers a range of topics, from large-scale machine learning to synthesis objects analysis.
Data Science and Machine Learning
Author | : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman |
Publsiher | : CRC Press |
Total Pages | : 538 |
Release | : 2019-11-20 |
Genre | : Business & Economics |
ISBN | : 9781000730777 |
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Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Practical Statistics for Data Scientists
Author | : Peter Bruce,Andrew Bruce |
Publsiher | : "O'Reilly Media, Inc." |
Total Pages | : 395 |
Release | : 2017-05-10 |
Genre | : Computers |
ISBN | : 9781491952917 |
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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
Machine Learning and Data Science
Author | : Daniel D. Gutierrez |
Publsiher | : Unknown |
Total Pages | : 0 |
Release | : 2015 |
Genre | : Data mining |
ISBN | : 1634620968 |
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This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains.
Machine Learning and Data Science in the Power Generation Industry
Author | : Patrick Bangert |
Publsiher | : Elsevier |
Total Pages | : 276 |
Release | : 2021-01-14 |
Genre | : Technology & Engineering |
ISBN | : 9780128226001 |
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Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls
Introduction to Statistical and Machine Learning Methods for Data Science
Author | : Carlos Andre Reis Pinheiro,Mike Patetta |
Publsiher | : SAS Institute |
Total Pages | : 169 |
Release | : 2021-08-06 |
Genre | : Computers |
ISBN | : 9781953329622 |
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Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.
Statistics with Julia
Author | : Yoni Nazarathy,Hayden Klok |
Publsiher | : Springer Nature |
Total Pages | : 527 |
Release | : 2021-09-04 |
Genre | : Computers |
ISBN | : 9783030709013 |
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This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.