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)

Introduction to data wrangling with R

The effort required to perform data wrangling operations, also known as data munging, is an understated aspect to all data science activities. Online courses or web-based examples generally provide pre-cleansed datasets for end users. This may give the impression that real-world data is similar to that used for data mining exercises and/or courses. In fact, real-world data is seldom, if ever, anywhere close to the pristine datasets depicted in such courses.

Real-world data will very likely not be in the format you need for your machine learning activities, may contain inaccurate or missing data, have mixed data types in the same column (for example, numbers and characters in the price column), and pose a host of other challenges that few of us are prepared for at the onset.

...