Scientific Data Analysis With R

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R For Data Science

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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12

R For Data Science written by Hadley Wickham 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 2016-12-12 with Computers categories.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Introduction To Data Science

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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2019-11-12

Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Mathematics categories.

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. A complete solutions manual is available to registered instructors who require the text for a course.

Data Analysis Using R Low Priced Edition A Primer For Data Scientist

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Author : DR DHAVAL MAHETA.
language : en
Publisher: Notion Press
Release Date : 2021-11-22

Data Analysis Using R Low Priced Edition A Primer For Data Scientist written by DR DHAVAL MAHETA. and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-22 with Education categories.

The objective of this book is to address the needs of students in using software for data science. The pace and content of this book is set in such a way so that it gives user friendly approach to students and other learners. The key features of this book are more than 60 packages have been discussed with relevant examples. The author has also incorporated the data analysis using R-Commander which is Graphic User Interface (GUI) of R programming. This book is a primer for upcoming fields such as Data Science and Machine Learning.

Using R For Data Analysis In Social Sciences

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Author : Quan Li
language : en
Publisher: Oxford University Press
Release Date : 2018

Using R For Data Analysis In Social Sciences written by Quan Li and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computers categories.

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.

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.

Mastering Data Analysis With R

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Author : Gergely Daróczi
language : en
Publisher:
Release Date : 2015

Mastering Data Analysis With R written by Gergely Daróczi 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.

Gain sharp insights into your data and solve real-world data science problems with R--from data munging to modeling and visualizationAbout This Book* Handle your data with precision and care for optimal business intelligence* Restructure and transform your data to inform decision-making* Packed with practical advice and tips to help you get to grips with data miningWho This Book Is ForIf you are a data scientist or R developer who wants to explore and optimize your use of R's advanced features and tools, this is the book for you. A basic knowledge of R is required, along with an understanding of database logic.What You Will Learn* Connect to and load data from R's range of powerful databases* Successfully fetch and parse structured and unstructured data* Transform and restructure your data with efficient R packages* Define and build complex statistical models with glm* Develop and train machine learning algorithms* Visualize social networks and graph data* Deploy supervised and unsupervised classification algorithms* Discover how to visualize spatial data with RIn DetailR is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently.This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage.Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods.Style and approachCovering the essential tasks and skills within data science, Mastering Data Analysis provides you with solutions to the challenges of data science. Each section gives you a theoretical overview before demonstrating how to put the theory to work with real-world use cases and hands-on examples.

Scientific Data Analysis With R

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Author : Azizur Rahman
language : en
Publisher: CRC Press
Release Date : 2024-11-28

Scientific Data Analysis With R written by Azizur Rahman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Mathematics categories.

In an era marked by exponential growth in data generation and an unprecedented convergence of technology and healthcare, the intersection of biostatistics and data science has become a pivotal domain. This book is the ideal companion in navigating the convergence of statistical methodologies and data science techniques with diverse applications implemented in the open-source environment of R. It is designed to be a comprehensive guide, marrying the principles of biostatistics with the practical implementation of statistics and data science in R, thereby empowering learners, researchers, and practitioners with the tools necessary to extract meaningful knowledge from biological, health, and medical datasets. This book is intended for students, researchers, and professionals eager to harness the combined power of biostatistics, data science, and the R programming language while gathering vital statistical knowledge needed for cutting-edge scientists in all fields. It is useful for those seeking to understand the basics of data science and statistical analysis, or looking to enhance their skills in handling any simple or complex data including biological, health, medical, and industry data. Key Features: Presents contemporary concepts of data science and biostatistics with real-life data analysis examples Promotes the evolution of fundamental and advanced methods applying to real-life problem-solving cases Explores computational statistical data science techniques from initial conception to recent developments of biostatistics Provides all R codes and real-world datasets to practice and competently apply into reader’s own domains Written in an exclusive state-of-the-art deductive approach without any theoretical hitches to support all contemporary readers

Data Science In Education Using R

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Author : Ryan A. Estrellado
language : en
Publisher: Routledge
Release Date : 2020-10-26

Data Science In Education Using R written by Ryan A. Estrellado and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-26 with Education categories.

Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.

Mastering Shiny

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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-04-29

Mastering Shiny written by Hadley Wickham 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 2021-04-29 with Computers categories.

Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production

Behavioral Data Analysis With R And Python

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Author : Florent Buisson
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-15

Behavioral Data Analysis With R And Python written by Florent Buisson 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 2021-06-15 with Business & Economics categories.

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way