The Big R-Book: From Data Science to Learning Machines and Big DataКНИГИ » ПРОГРАММИНГ
Название: The Big R-Book: From Data Science to Learning Machines and Big Data Автор: Philippe J.S. De Brouwer Издательство: Wiley Год: 2021 Страниц: 886 Язык: английский Формат: pdf (true) Размер: 36.3 MB
Introduces professionals and scientists to statistics and Machine Learning using the programming language R.
Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical Machine Learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science.
The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices.
Essentially, R is ... - a programming language built for statistical analysis, graphics representation and reporting; - an interpreted computer language which allows branching, looping, modular programming as well as object and functional oriented programming features.
R offers its users ... - integration with the procedures written in the C, C++, .Net, Python, or FORTRAN languages for efficiency; - zero purchase cost (available under the GNU General Public License), and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac; - simplicity and effectiveness; - a free and open environment; - an effective data handling and storage facility; - a suite of operators for calculations on arrays, lists, vectors, and matrices; - a large, coherent, and integrated collection of tools for data analysis; - graphical facilities for data analysis and display either directly at the computer or printing; - a supportive on-line community; - the ability for you to stand on the shoulders of giants (e.g. by using libraries).
R is arguably the most widely used statistics programming language and is used from universities to business applications, while it still gains rapidly in popularity.
Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site
The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.
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Beginning Data Science in R Название: Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist Автор: Thomas Mailund Издательство: Apress...
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The Big R-Book: From Data Science to Learning Machines and Big Data 
Название: The Big R-Book: From Data Science to Learning Machines and Big Data 
