 Название: Artificial Intelligence, Big Data and Data Science in Statistics
Автор: Ansgar Steland, Kwok-Leung Tsui
Издательство: Springer
Год: 2022
Страниц: 378
Язык: английский
Формат: pdf (true)
Размер: 11.0 MB
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.
Source code is available at github.com/Apress/beg-data-science-r4.
The change to data-centrism in many fields, the need to extract information and knowledge from big data, and the increasing success of Machine Learning (ML) and Artificial Intelligence (AI) have created both opportunities and challenges to the field of statistics. These developments have, to some extent, led to the creation of data science, partially regarded as a new discipline, related to statistics and computer science. The intersections among ML/AI, data science, and statistics are much larger than people expect, particularly on theory, models, practical methods, and problems under investigation. All communities can learn a lot from each other.
The impressive successes of ML and AI methods, especially deep learners and convolutional networks, in many practical problems might seem to devalue statistical approaches. Quite a few researchers as well as practitioners regard machine learning as being more focused on problem solving and benchmark data sets than statistics. But, on the other hand, ML solutions are often tailored to a specific problem and thus can be difficult to generalize and implement for a wide range of applications.
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.
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