The thrilling history of the Targaryens comes to life in this masterly work by the author of A Song of Ice and Fire, the inspiration for HBO’s Game of Thrones.
Readers have glimpsed small parts of this narrative in such volumes as The World of Ice & Fire, but now, for the first time, the full tapestry of Targaryen history is revealed.
Centuries before the events of A Game of Thrones, House Targaryen—the only family of dragonlords to survive the Doom of Valyria—took up residence on Dragonstone.
Fire & Blood begins their tale with the legendary Aegon the Conqueror, creator of the Iron Throne, and goes on to recount the generations of Targaryens who fought to hold that iconic seat, all the way up to the civil war that nearly tore their dynasty apart.
What really happened during the Dance of the Dragons? Why was it so deadly to visit Valyria after the Doom? What were Maegor the Cruel’s worst crimes? What was it like in Westeros when dragons ruled the skies? These are but a few of the questions answered in this essential chronicle, as related by a learned maester of the Citadel and featuring more than eighty all-new black-and-white illustrations by artist Doug Wheatley.
With all the scope and grandeur of Gibbon’s The History of the Decline and Fall of the Roman Empire, Fire & Blood is the the first volume of the definitive two-part history of the Targaryens, giving readers a whole new appreciation for the dynamic, often bloody, and always fascinating history of Westeros.
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Learn how to use R to turn raw data into insight, knowledge, and understanding.
Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.
You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details.
This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results.
Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
You’ll learn how to:Wrangle—transform your datasets into a form convenient for analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, generate hypotheses, and quickly test themModel—provide a low-dimensional summary that captures true “signals” in your datasetCommunicate—learn R Markdown for integrating prose, code, and results.
Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience.
The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This book presents some of the most important modeling and prediction techniques, along with relevant applications.
Color graphics and real-world examples are used to illustrate the methods presented.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
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This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R.
Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc.
The author’s goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data.
This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information.
Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.
By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and datesThe difference between different data structures and how to create, add additional components to, and subset each data structureHow to acquire and parse data from locations previously inaccessibleHow to develop functions and use loop control structures to reduce code redundancyHow to use pipe operators to simplify code and make it more readableHow to reshape the layout of data and manipulate, summarize, and join data sets.
Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks.
It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions.
Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data.
It will improve readers’ computational reasoning of real-world data analyses.
Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis.
The book’s collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naïve Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills.