Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. This video empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action.By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.
About the Auhtor :
Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga.
Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world.
Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in Academia and then switched to a leadership position in the software industry for a decade. During this period, he worked for Infosys, Igate, and Starbase. He embraced Academia once again in 2001.
Viswa has taught extensively in diverse fields, including operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He authored a book entitled Data Analytics with R: A Hands-on Approach.
Viswa thoroughly enjoys hands-on software development, and has single-handedly conceived, architected, developed, and deployed several web-based applications.
Apart from his deep interest in technical fields such as data analytics, Artificial Intelligence, computer science, and software engineering, Viswa harbors a deep interest in education, with a special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further.
Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational during his early research career. He is also grateful to several extremely intelligent colleagues, notably Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely.
His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics.
CSV formats are best used to represent sets or sequences of records in which each record has an identical list of fields.
Sometimes we need to convert numerical data to categorical data or a factor.
In situations where we have categorical variables (factors) but need to use them in analytical methods that require numbers, we need to create dummy variables.
By partitioning data we can unbiasedly evaluate the quality of data.
Before even embarking on any numerical analyses, you may want to get a good idea about the data through a few quick plots. So we cover only the simplest forms of basic graphs.
We often want to see plots side by side for comparisons. This video shows how we can achieve this.
Getting an idea of how the model does in training data itself is useful, but you should never use that as an objective measure.
Receiver operating characteristic (ROC) charts helps by giving a visual representation of the true and false positives at various cutoff levels.
This video shows you how you can use the rpart package to build classification trees and the rpart.plot package to generate nice-looking tree diagrams.
The randomForest package can help you to easily apply the very powerful but computationally intensive random forest classification technique.
The e1071 package can help you to easily apply the very powerful Support Vector Machine (SVM) classification technique.
The e1071 package contains the naiveBayes function for the Naïve Bayes classification.
The class package contains the knn function for KNN classification.
The stats package contains the glm function for classification using logistic regression.
In this video, we will discuss linear regression, arguably the most widely used technique. The stats package has the functionality for linear regression and R loads it automatically at startup.
This video covers the use of tree models for regression. The rpart package provides the necessary functions to build regression trees.
This video looks at random forests—one of the most successful machine learning techniques.
The standard R package stats provides the function for K-means clustering. We also use the cluster package to plot the results of our cluster analysis.
The stats package offers the prcomp function to perform PCA. This recipe shows you how to perform PCA using these capabilities.
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