Beginning Data Science In R 4

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Beginning Data Science In R 4

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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2022-06-28

Beginning Data Science In R 4 written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-28 with Computers categories.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

Beginning Data Science In R

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Author : Thomas Mailund
language : en
Publisher: Apress
Release Date : 2017-03-09

Beginning Data Science In R written by Thomas Mailund and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-09 with Computers categories.

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

Beginning Data Science With R

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Author : Manas A. Pathak
language : en
Publisher: Springer
Release Date : 2014-12-08

Beginning Data Science With R written by Manas A. Pathak and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-08 with Technology & Engineering categories.

“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.

An Introduction To Data Analysis In R

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Author : Alfonso Zamora Saiz
language : en
Publisher: Springer Nature
Release Date : 2020-07-27

An Introduction To Data Analysis In R written by Alfonso Zamora Saiz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-27 with Computers categories.

This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.

Beginner S Guide To R Programming

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Author : Agasti Khatri
language : en
Publisher: Educohack Press
Release Date : 2025-02-20

Beginner S Guide To R Programming written by Agasti Khatri and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.

Discover the world of data analysis with "Beginner's Guide to R Programming." This comprehensive resource is crafted to help individuals learn the R programming language and explore its diverse applications. Whether you're a complete beginner or an experienced analyst, our book offers a structured learning path that starts with the basics and progresses to advanced topics like statistical analysis, data visualization, and machine learning. Each chapter includes practical examples, exercises, and real-world case studies, encouraging hands-on experimentation with R code. You'll delve into data types, functions, data manipulation, statistical analysis, data visualization, and more, building a solid foundation in R programming and data analysis. Complex concepts are explained in clear, easy-to-understand language, with visual aids, code snippets, and step-by-step tutorials to help you grasp key ideas effectively. The book emphasizes practical applications of R in real-world scenarios, showcasing how you can use R to solve problems, analyze data, make informed decisions, and communicate insights. With access to supplementary resources, including downloadable datasets, code samples, and additional exercises, you'll further enhance your learning experience and practice your skills.

Fundamentals Of Data Science

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Author : Sanjeev J. Wagh
language : en
Publisher: CRC Press
Release Date : 2021-09-26

Fundamentals Of Data Science written by Sanjeev J. Wagh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-26 with Business & Economics categories.

Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science. Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue. This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge. Features : Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets. Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools. Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice. Information is presented in an accessible way for students, researchers and academicians and professionals.

Data Science With Semantic Technologies

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Author : Archana Patel
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-26

Data Science With Semantic Technologies written by Archana Patel 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 2022-10-26 with Computers categories.

DATA SCIENCE WITH SEMANTIC TECHNOLOGIES This book will serve as an important guide toward applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for scholars, researchers, professionals, and practitioners in this field. To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization. Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers? Audience Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.

Geographical Data Science And Spatial Data Analysis

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Author : Lex Comber
language : en
Publisher: SAGE
Release Date : 2020-12-02

Geographical Data Science And Spatial Data Analysis written by Lex Comber and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-02 with Science categories.

We are in an age of big data where all of our everyday interactions and transactions generate data. Much of this data is spatial – it is collected some-where – and identifying analytical insight from trends and patterns in these increasing rich digital footprints presents a number of challenges. Whilst other books describe different flavours of Data Analytics in R and other programming languages, there are none that consider Spatial Data (i.e. the location attached to data), or that consider issues of inference, linking Big Data, Geography, GIS, Mapping and Spatial Analytics. This is a ‘learning by doing’ textbook, building on the previous book by the same authors, An Introduction to R for Spatial Analysis and Mapping. It details the theoretical issues in analyses of Big Spatial Data and developing practical skills in the reader for addressing these with confidence.

Statistical Inference Via Data Science

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Author : Chester Ismay
language : en
Publisher: CRC Press
Release Date : 2025-05-02

Statistical Inference Via Data Science written by Chester Ismay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Mathematics categories.

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All of these tasks are performed by strongly emphasizing data visualization. Key Features in the Second Edition: Minimal Prerequisites: No prior calculus or coding experience is needed, making the content accessible to a wide audience. Real-World Data: Learn with real-world datasets, including all domestic flights leaving New York City in 2023, the Gapminder project, FiveThirtyEight.com data, and new datasets on health, global development, music, coffee quality, and geyser eruptions. Simulation-Based Inference: Statistical inference through simulation-based methods. Expanded Theoretical Discussions: Includes deeper coverage of theory-based approaches, their connection with simulation-based approaches, and a presentation of intuitive and formal aspects of these methods. Enhanced Use of the infer Package: Leverages the infer package for “tidy” and transparent statistical inference, enabling readers to construct confidence intervals and conduct hypothesis tests through multiple linear regression and beyond. Dynamic Online Resources: All code and output are embedded in the text, with additional interactive exercises, discussions, and solutions available online. Broadened Applications: Suitable for undergraduate and graduate courses, including statistics, data science, and courses emphasizing reproducible research. The first edition of the book has been used in so many different ways--for courses in statistical inference, statistical programming, business analytics, and data science for social policy, and by professionals in many other means. Ideal for those new to statistics or looking to deepen their knowledge, this edition provides a clear entry point into data science and modern statistical methods.

Data Science For Water Utilities

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Author : Peter Prevos
language : en
Publisher: CRC Press
Release Date : 2023-05-10

Data Science For Water Utilities written by Peter Prevos and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-10 with Computers categories.

This addition to the Data Science Series introduces the principles of data science and the R language to the singular needs of water professionals. The book provides unique data and examples relevant to managing water utility and is sourced from the author’s extensive experience. Data Science for Water Utilities: Data as a Source of Value is an applied, practical guide that shows water professionals how to use data science to solve urban water management problems. Content develops through four case studies. The first looks at analysing water quality to ensure public health. The second considers customer feedback. The third case study introduces smart meter data. The guide flows easily from basic principles through code that, with each case study, increases in complexity. The last case study analyses data using basic machine learning. Readers will be familiar with analysing data but do not need coding experience to use this book. The title will be essential reading for anyone seeking a practical introduction to data science and creating value with R.