Cambridge University Press, 2009. — 585 p. — ISBN 9780521760119. Statistical techniques can be used to address new situations. This is important in a rapidly evolving risk management and financial world. Analysts with a strong statistical background understand that a large data set can represent a treasure trove of information to be mined and can yield a strong competitive...
Packt Publishing, 2017. — 442 p. — ISBN: 1787124460. !Only code files! Mine valuable insights from your data using popular tools and techniques in R About This Book Understand the basics of data mining and why R is a perfect tool for it. Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it. Apply effective...
2nd.ed. — Packt, 2017. — 523 p. — ISBN: 978-1787124479. Only sample files! Key Features Analyse your data using the popular R packages with ready-to-use and customizable recipes Find meaningful insights from your data and generate dynamic reports A practical guide to help you put your data analysis skills in R to practical use Book Description This book will show you how you...
Материалы (рабочие текстовые файлы, файлы примеров и код) к книге: Jockers M.L.,Text Analysis with R for Students of Literature Архив содержит: Код на R к примерам из глав 3, 4, 5, 6, 7. Полные тексты произведений для разбираемых в книге примеров в форматах *.txt и *.xml. Вспомогательные файлы к примерам.
Код и наборы данных (datasets) к книге: Berlinger E., Illés F., Vadász T. et al., Mastering R for Quantitative Finance В архив включены: Исходный код на R к примерам для глав 1-13. Данные к примерам для глав 2, 6, 7, 11, 12, 13
2nd Ed. — Apress, 2019. — 712 p. in color. — ISBN: 9781484242148, EISBN 9781484242155. Code files only! Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming...
2nd Edition. — Manning Publications, 2020. — 568 p. — ISBN: 9781617295874. !Code files Practical Data Science with R, Second Edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the R language. By concentrating on the most important tasks you'll face on the job, this friendly guide is comfortable both for business analysts...
New York: Apress, 2017. - 103 p. Learn how to manipulate functions and expressions to modify how the R language interprets itself. This book is an introduction to metaprogramming in the R language, so you will write programs to manipulate other programs. Metaprogramming in R shows you how to treat code as data that you can generate, analyze, or modify. R is a very high-level...
Apress, 2016. — 217 p. — ISBN: 9781484221778, EISBN: 9781484221785 Code files only! This book explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a backtester, strategy optimizer, and fully functional...
Packt, 2019. — 438 p. — ISBN: 1788629159. Code files only! Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R Build efficient forecasting models using traditional time series models and machine learning algorithms. Key Features Perform time series analysis and forecasting using R packages such as Forecast and h2o Develop models and find...
Packt Publishing, 2016. — 278 p. — ISBN: 978-1-78646-515-3. In this book, we cover not only classical data structures, but also functional data structures. We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the...
Packt Publishing, 2018. — 422 p. — ISBN: 978-1788627306. Only CODE files! Key Features Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R A complete guide...
Packt Publishing, 2016. — 1783 p. Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining...
Apress, 2021. — 404 p. — ISBN 978-1484268308. Source code only! Master the syntax for working with R’s plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This...
Код и учебные материалы к книге: Kumar A., Paul A., Mastering Text Mining with R. В архив включены: Код для примеров на R к главам 4, 6, 7. Данные к примерам и тестовые материалы для глав 2, 5, 6.
Apress, 2017. — 352 с. — ISBN: 978-1-4842-2670-4. Code files only! 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...
Apress, 2017. — 262 p. — ISBN: 978-1-4842-3143-2. Code files only! Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages is not mutable: for example you’ll learn how to change variable-value bindings by modifying environments, which...
Packt Publishing, 2018. — 376 p. — ISBN: 978-1788624145. !Only code files Key Features Implement machine learning algorithms to build ensemble-efficient models Explore powerful R packages to create predictive models using ensemble methods Learn to build ensemble models on large datasets using a practical approach Book Description Ensemble techniques are used for combining two...
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