Ebook: Hands-On Data Science with R: Techniques to perform data manipulation and mining to build smart analytical models using R
- Genre: Computers // Organization and Data Processing
- Year: 2018
- Publisher: Packt Publishing
- Language: English
- epub
A hands-on guide for professionals to perform various data science tasks in R
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
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
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