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)


  1. Quiz-tion 1: Which statement about machine learning is NOT CORRECT?
    1. Machine learning is interdisciplinary
    2. It is only used by the finance industry
    3. Non-profit organizations are using it to solve ambiental and social issues
    4. Machine learning techniques can be used to build recommenders
  2. Quiz-tion 2: About machine learning algorithms—it would be wrong to say that:
    1. Hierarchical clustering and k-means clustering are unsupervised learning techniques
    2. Random forests can be characterized as ensemble learning
    3. Neural networks can't deal with classification problems
    4. Transformations can sometimes improve clustering
  1. Quiz-tion 3: Which of the following tricks are said to be related to SVM models?
    1. The hat trick
    2. The kernel trick
    3. The mirror trick
    4. The dragon-slayer trick