Book Image

Big Data Analytics with R

By : Simon Walkowiak
Book Image

Big Data Analytics with R

By: Simon Walkowiak

Overview of this book

Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.
Table of Contents (16 chapters)
Big Data Analytics with R
About the Author
About the Reviewers

What is machine learning?

We will begin this chapter with a brief introduction to the concept of machine learning by presenting an overview of the most frequently used predictive algorithms, their classification, and typical characteristics. We will also list a number of resources where you can find more information about the specifics of chosen algorithms and we will guide you through the growing number of Big Data machine learning tools available to data scientists.

Machine learning algorithms

Machine learning methods encapsulate data mining and statistical techniques allowing researchers to make sense of data, model the relationships between variables or features, and extend these models to predict the values or classes of events in the future. So how does this field differ from the already well-known statistical testing? In general, we can say that machine learning methods are less stringent about the required format and characteristics of the data; that is, many machine learning algorithms...