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R: Data Analysis and Visualization

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by Tony Fischetti & Brett Lantz & Jaynal Abedin
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R: Data Analysis and Visualization by Tony Fischetti & Brett Lantz & Jaynal Abedin

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 algorithms • Use R to optimize your trading strategy and build up your own risk management system • Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer What You Will Learn • Describe and visualize the behavior of data and relationships between data • Gain a thorough understanding of statistical reasoning and sampling • Handle missing data gracefully using multiple imputation • Create diverse types of bar charts using the default R functions • Produce and customize density plots and histograms with lattice and ggplot2 • Get to know the top classification algorithms written in R • Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on • Understand relationships between market factors and their impact on your portfolio • Harness the power of R to build machine learning algorithms with real-world data science applications • Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules,which are a mini-course in their own right.

As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs,which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. Through inspecting large datasets using tableplot and stunning 3D visualizations, you will know how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. You will finish this module feeling confident in your ability to know which data mining algorithm to apply in any situation. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative financ

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Ebook Details
Pages: 1783
Size: 103.5 MB
Publisher: Packt Publishing
Date published:   2016
ISBN: 9781786460486 (DRM-EPUB)

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This product is listed in the following categories:

Nonfiction > Computers > Data Processing
Nonfiction > Computers > Programming > Algorithms
Nonfiction > Computers > Data Modeling & Design

These authors have products in the following categories:

Nonfiction > Computers > Machine Theory
Nonfiction > Computers > Programming > Open Source
Nonfiction > Computers > Database Management
Nonfiction > Computers > Database Management > Database Mining
Nonfiction > Computers > Mathematical & Statistical Software
Nonfiction > Computers > Data Processing
Nonfiction > Computers > Programming > Algorithms
Nonfiction > Computers > Data Modeling & Design

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