Statistical Learning And Data Sciences

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Statistical Learning And Data Science

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Author : Mireille Gettler Summa
language : en
Publisher: CRC Press
Release Date : 2011-12-19

Statistical Learning And Data Science written by Mireille Gettler Summa and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-12-19 with Business & Economics categories.

Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor

Statistical Learning And Data Sciences

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Author : Alexander Gammerman
language : en
Publisher: Springer
Release Date : 2015-04-02

Statistical Learning And Data Sciences written by Alexander Gammerman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-02 with Computers categories.

This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015. The 36 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on statistical learning and its applications, conformal prediction and its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.

The Elements Of Statistical Learning

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Author : Trevor Hastie
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11

The Elements Of Statistical Learning written by Trevor Hastie and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Machine Learning And Data Science

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Author : Daniel D. Gutierrez
language : en
Publisher:
Release Date : 2015

Machine Learning And Data Science written by Daniel D. Gutierrez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Data mining categories.

A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation.

Statistical Foundations Of Data Science

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Author : Jianqing Fan
language : en
Publisher: CRC Press
Release Date : 2020-09-20

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 2020-09-20 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.

An Introduction To Statistical Learning

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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2023-06-30

An Introduction To Statistical Learning written by Gareth James and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Mathematics categories.

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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Just Enough Data Science And Machine Learning

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Author : Mark Levene
language : en
Publisher: Addison-Wesley Professional
Release Date : 2024-12-04

Just Enough Data Science And Machine Learning written by Mark Levene and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-04 with Business & Economics categories.

An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and concepts Coverage of various types of data and techniques for analysis In-depth exploration of popular machine learning tools and methods Insight into specific data science topics, such as social networks and sentiment analysis Practical examples and case studies for real-world application Recommended further reading for deeper exploration of specific topics.

Statistics For Data Science

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Author : James D. Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-17

Statistics For Data Science written by James D. Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-17 with Computers categories.

Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples

Statistics Essentials For Beginner In Data Science

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Author : Jay Mishra
language : en
Publisher: AI Sciences LLC
Release Date : 2019-01-27

Statistics Essentials For Beginner In Data Science written by Jay Mishra and has been published by AI Sciences LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-27 with categories.

***** BUY NOW (will soon return to 24.95 $) *****Are you thinking of learning Statistics fundamentals for Data Science? If you are looking for a beginner book to master Statistics Learning fundamentals for Data Science, this book is for you. Who Should Read this Book?Aspiring data scientists who are looking forward to begin their journey in the vast field of data science. People who are seeking to learn and understand data analysis from its very deep-rooted basics have found the right book. Clear basic concepts make the foundation of a good knowledge base, which ultimately helps to gain sharp insights into this topic further. This book will give you the practical exposure along with its theory explained comprehensively. This book is the perfect compilation for beginners as well as intermediate learners who intend to learn statistics and data analysis techniques. Why this book?This book will guide you step by step from the very basics to how you can start your own data science project. The best part about this book is its structure, it's structured in such a way that integrates practicals along with its theory to make the concepts easily understandable. It will help you to understand a basic concept like mean, median, mode, scatter plot and histograms. Thus ensures no prior knowledge is required to start learning from this book. The content of this book is specially designed to encompass all the concepts that come under the domain of data science. This book will guide you through the problems and concepts of statistics. What is statistics?h2>Most of the people think statistics in data science is something different and more profound than what we learnt in our mathematics classes but it's not. It is the same concept of data collection followed by its organization, interpretation and presentation. Statistics is the key to develop a desired model in machine learning. Using statistics you can convert your raw meaningless chunk of data to a well-structured informative data. What's Inside This Book? Probability & Bayes Theorem, Data Exploration and Analysis Structured Data Estimates Mean and Median Estimates Variability Exploring the data distribution Percentiles and Boxplots Frequency table and Histograms Density Estimates Mode Correlation Categorical and Numeric Data Visualizing Multiple Variables Regression Analysis Clustering Analysis Statistical tests and ANOVA Classification Naïve Bayes Discriminant Analysis Linear regression Logistic Regression Statistical Machine Learning K_Nearest Neighbor Trees Models Bagging and Random Forest Boosting algorithms Principal Component Analysis K_means Clustering Hierarchical Clustering Model Based Clustering Sources & References From AI Sciences PublishingOur books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations.Frequently Asked QuestionsQ: Does this book include everything I need to become a data analyst expert?A: Unfortunately, no. This book is designed for readers taking their first steps in statistics and data science and further learning will be required beyond this book to master all aspects. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON *****

Practical Statistics For Data Scientists

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Author : Peter Bruce
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-05-10

Practical Statistics For Data Scientists written by Peter Bruce and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-10 with Computers categories.

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