Hands-On Data Science with R: Techniques to perform data manipulation and mining to build smart analytical models using R. Este artículo no está disponible.Hands-On Data Science with R: Techniques to perform data manipulation and mining to build smart analytical models using R

A hands-on guide for professionals to perform various data science tasks in R

Key Features

• Explore the popular R packages for data science
• Use R for efficient data mining, text analytics and feature engineering
• Become a thorough data science professional with the help of hands-on examples and use-cases in R

Book Description

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems.

The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data.

Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.

What you will learn

• Understand the R programming language and its ecosystem of packages for data science
• Obtain and clean your data before processing
• Master essential exploratory techniques for summarizing data
• Examine various machine learning prediction, models
• Explore the H2O analytics platform in R for deep learning
• Apply data mining techniques to available datasets
• Work with interactive visualization packages in R
• Integrate R with Spark and Hadoop for large-scale data analytics

Who this book is for

If you are a budding data scientist keen to learn about the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course

Table of Contents

1. Getting started with Data Science and R
2. Descriptive and Inferential Statistics
3. Data Wrangling with R
4. KDD, Data Mining, and Text Mining
5. Data Analysis with R
6. Machine Learning with R
7. Forecasting and ML App with R
8. Neural Networks and Deep Learning
9. Markovian in R
10. Visualizing Data
11. Going to Production with R
12. Large Scale Data Analytics with Hadoop
13. R on Cloud
14. The Road Ahead

Doug Ortiz is an experienced enterprise cloud, big data, data analytics, and solutions architect who has architected, designed, developed, engineered, re-engineered, and integrated enterprise solutions. The technologies he has experience with include: Amazon Web Services, Azure, Google Cloud, Business Intelligence, Data Science, Hadoop, Spark, NoSQL and Graph Databases, and Web Front-End Technologies.

Vitor Bianchi Lanzetta (@vitorlanzetta) has a master's degree in Applied Economics (University of So PauloUSP) and works as a data scientist in a tech start-up named RedFox Digital Solutions. He has also authored a book called R Data Visualization Recipes. The things he enjoys the most are statistics, economics, and sports of all kinds (electronics included). His blog, made in partnership with Ricardo Anjoleto Farias (@R_A_Farias), can be found at ArcadeData dot org, they kindly call it R-Cade Data.

Nataraj Dasgupta is the vice president of advanced analytics at RxDataScience Inc. Nataraj has been in the IT industry for more than 19 years, and has worked in the technical and analytics divisions of Philip Morris, IBM, UBS Investment Bank, and Purdue Pharma. At Purdue Pharma, Nataraj led the data science division, where he developed the company's award-winning big data and machine learning platform. Prior to Purdue, at UBS, he held the role of Associate Director, working with high-frequency and algorithmic trading technologies in the foreign exchange trading division of the bank.

Ricardo Anjoleto Farias is an economist who graduated from the Universidade Estadual de Maring in 2014. In addition to being a sports enthusiast (electronic or otherwise) and enjoying a good barbecue, he also likes math, statistics, and correlated studies. His first contact with R was when he embarked on his master's degree, and since then, he has tried to improve his skills with this powerful tool.