Book Image

R Data Analysis Projects [Video]

By : Gopi Subramanian
Book Image

R Data Analysis Projects [Video]

By: Gopi Subramanian

Overview of this book

<p>R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis.</p> <p>This video will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets.</p> <p>You’ll implement time-series modeling for anomaly detection and understand cluster analysis for streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow code.</p> <p>With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The video covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.</p> <p>By the end of this video, you’ll have a better understanding of data analysis with R, and will be able to put your knowledge to practical use without any hassle.</p> <h1>Style and Approach</h1> <p>This video takes a unique, learn-as-you-do approach, as you build on your understanding of data analysis progressively with each project. This video is designed in a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently.</p>
Table of Contents (8 chapters)
Chapter 6
Streaming Data Clustering Analysis in R
Content Locked
Section 1
Introducing Stream Clustering
There is a great demand today to perform analysis on data in motion, also called streaming data. Streaming data is becoming ubiquitous with the number of addressable sensors and devices being added to the internet. Clustering can be defined as the task of separating a set of observations/tuples into groups/clusters so that the intra-cluster records are similar and the inter-cluster records are dissimilar. We will also see streaming data challenges. - Study the challenges in streaming - Understand the stream clustering concept