Book Image

Hands-On Data Science with R

By : Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias
Book Image

Hands-On Data Science with R

By: Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias

Overview of this book

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.
Table of Contents (16 chapters)

Summary

In this chapter, we learned what cloud computing is and what cloud services do. We also saw which factors need to be checked before picking a cloud service. This chapter discussed the different cloud companies and the advantages of selecting Azure as our cloud service provider.

Some services provided by Azure were briefly introduced. The reader also saw a detailed process of registration. We learned about the Azure Machine Learning Studio and how it works, by implementing a modules work. We then moved to using R in the cloud. Blood donation data, Azure's built-in dataset, was adopted for our experimental purpose.

In the next chapter, we will discuss the paths in data-science career, ways to seek help and to improve your skills.