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)

Good practices of KDD and data mining

Although the practices that are about to be discussed are associated with KDD and data mining, they are not restricted to them, and I believe that the vast majority can be easily extrapolated to other contexts.

Since its origin, humankind has been taking advantage (and sometimes being misguided) from its strong pattern recognition capabilities. We humans can now gather and store far more data than our natural abilities are capable of making sense of. We also developed tools and methods to deal with this data overload. KDD is one of these tools—it's an interactive, iterative, stage-wise approach that's used to make sense out of big and unstructured databases.

Stages of KDD