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R for Data Science: Visualize, Model, Transform, Tidy, & Import
This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.
The Data Science Handbook: Advice and Insights
This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.
Exploring Data Science (Nina Zumel, et al)
This book introduces readers to various areas in data science and explains which methodologies work best for each, with practical examples in R, Python, and other languages.
Introduction to Data Science (Jeffrey Stanton)
This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics.
School of Data Handbook
This textbook will provide the detail and background theory to support the Data Science courses and challenges. It will guide you through the key stages of a data project. These stages can be thought of as a pipeline, or a process.
The Fourth Paradigm: Data-Intensive Scientific Discovery
This book presents the first broad look at the rapidly emerging field of data-intensive science, with the goal of influencing the worldwide scientific and computing research communities and inspiring the next generation of scientists.
Modeling with Data: Tools and Techniques for Scientific Computing
Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, etc..