Motwani, Bharti   <br/><br/>
Data analytics with R 

 - New Delhi  Wiley India Pvt. Ltd.  2022 
 - xvii, 646 p. 
<br/><br/>Table of content<br/>PART 1 Basics of R<br/><br/>Chapter 1 Introduction to R<br/><br/>1.1 Features of R<br/><br/>1.2 Installation of R<br/><br/>1.3 Getting Started<br/><br/>1.4 Variables in R<br/><br/>1.5 Input of Data<br/><br/>1.6 Output in R<br/><br/>1.7 In-Built Functions in R<br/><br/>1.8 Packages in R<br/><br/> <br/><br/>Chapter 2 Data Types of R<br/><br/>2.1 Vectors<br/><br/>2.2 Matrices<br/><br/>2.3 Arrays<br/><br/>2.4 Lists<br/><br/>2.5 Factors<br/><br/>2.6 Data Frame<br/><br/> <br/><br/>Chapter 3 Programming in R<br/><br/>3.1 Decision-Making Structures<br/><br/>3.2 Loops<br/><br/>3.3 User-Defined Functions<br/><br/>3.4 User-Defined Package<br/><br/>3.5 Reports using Rmarkdown<br/><br/> <br/><br/>Chapter 4 Data Exploration and Manipulation<br/><br/>4.1 Missing Data Management<br/><br/>4.2 Data Reshaping through Melting and Casting<br/><br/>4.3 Special Functions across Data Elements<br/><br/> <br/><br/>Chapter 5 Import and Export of Data<br/><br/>5.1 Import and Export of Data in Text File<br/><br/>5.2 Import and Export of Data in Excel<br/><br/>5.3 Import and Export of Data in XML<br/><br/>5.4 Import and Export of Data in JSON<br/><br/>5.5 Import and Export of Data in MySQL<br/><br/>5.6 Import and Export of Data in SPSS<br/><br/>5.7 Import and Export of Data in SAS<br/><br/>PART 2 Visualization Techniques<br/><br/>Chapter 6 Basic Visualization<br/><br/>6.1 Pie Chart<br/><br/>6.2 Bar Chart<br/><br/>6.3 Histograms<br/><br/>6.4 Line Chart<br/><br/>6.5 Kernel Density Plots<br/><br/>6.6 Quantile-Quantile (Q-Q) Plot<br/><br/>6.7 Box-and-Whisker Plot<br/><br/>6.8 Violin Plot<br/><br/>6.9 Dot Chart<br/><br/>6.10 Bubble Plot<br/><br/>6.11 Image Plot<br/><br/>6.12 Mosaic Plot<br/><br/> <br/><br/>Chapter 7 Advanced Visualization<br/><br/>7.1 Scatter Plot<br/><br/>7.2 Corrgrams<br/><br/>7.3 Star and Segment Plots<br/><br/>7.4 Tree Maps<br/><br/>7.5 Heat Map<br/><br/>7.6 Perspective and Contour Plot<br/><br/>7.7 Using ggplot2 for Advanced Graphics<br/><br/>PART 3 Statistical Analysis<br/><br/>Chapter 8 Basic Statistics<br/><br/>8.1 Descriptive Statistics<br/><br/>8.2 Table in R<br/><br/>8.3 Correlation and Covariance<br/><br/>8.4 Simulation and Distributions<br/><br/>8.5 Reproducing Same Data<br/><br/>Case Study: Web Analytics using Goal Funnels: Asset for e-Commerce Business<br/><br/> <br/><br/>Chapter 9 Compare Means<br/><br/>9.1 Parametric Techniques<br/><br/>Case Study: Green Building Certification<br/><br/>Case Study: Comparison of Personal Web Store and Marketplaces for Online Selling<br/><br/>Case Study: Effect of Training Program on Employee Performance<br/><br/>Case Study: Effect of Demographics on Online Mobile Shopping Apps<br/><br/>9.2 Non-Parametric Tests<br/><br/> <br/><br/>Chapter 10 Time-Series Models<br/><br/>10.1 Time-Series Object in R<br/><br/>10.2 Smoothing<br/><br/>10.3 Seasonal Decomposition<br/><br/>10.4 ARIMA Modeling<br/><br/>10.5 Survival Analysis<br/><br/>Case Study: Foreign Trade in India<br/><br/> <br/><br/>PART 4 Machine Learning<br/><br/>Chapter 11 Unsupervised Machine Learning Algorithms<br/><br/>11.1 Dimensionality Reduction<br/><br/>Case Study: Balanced Scorecard Model for Measuring Organizational Performance<br/><br/>Case Study: Employee Attrition in an Organization<br/><br/>11.2 Clustering<br/><br/>Case Study: Market Capitalization Categories<br/><br/>Case Study: Performance Appraisal in Organizations<br/><br/> <br/><br/>Chapter 12 Supervised Machine Learning Problems<br/><br/>12.1 Regression<br/><br/>Case Study: Relationship between Buying Intention and Awareness of Electric Vehicles<br/><br/>Case Study: Application of Technology Acceptance Model in Cloud Computing<br/><br/>Case Study: Impact of Social Networking Websites on Quality of Recruitment<br/><br/>12.2 Classification<br/><br/>Case Study: Prediction of Customer Buying Intention due to Digital Marketing<br/><br/> <br/><br/>Chapter 13 Supervised Machine Learning Algorithms<br/><br/>13.1 Naïve Bayes Algorithm<br/><br/>Case Study: Measuring Acceptability of a New Product<br/><br/>13.2 k-Nearest Neighbor’s (KNN) Algorithm<br/><br/>Case Study: Predicting Phishing Websites<br/><br/>Case Study: Loan Categorization<br/><br/>13.3 Support Vector Machines (SVMs)<br/><br/>Case Study: Fraud Analysis for Credit Card and Mobile Payment Transactions<br/><br/>Case Study: Diagnosis and Treatment of Diseases<br/><br/>13.4 Decision Trees<br/><br/>Case Study: Occupancy Detection in Buildings<br/><br/>Case Study: Artificial Intelligence and Employment<br/><br/> <br/><br/>Chapter 14 Supervised Machine Learning Ensemble Techniques<br/><br/>14.1 Bagging<br/><br/>Case Study: Measuring Customer Satisfaction related to Online Food Portals<br/><br/>Case Study: Predicting Income of a Person<br/><br/>14.2 Random Forest<br/><br/>Case Study: Writing Recommendation/Approval Reports<br/><br/>Case Study: Prediction of Sports Results<br/><br/>14.3 Gradient Boosting<br/><br/>Case Study: Impact of Online Reviews on Buying Behavior<br/><br/>Case Study: Effective Vacation Plan through Online Services<br/><br/> <br/><br/>Chapter 15 Machine Learning for Text Data<br/><br/>15.1 Text Mining<br/><br/>Case Study: Spam Protection and Filtering<br/><br/>15.2 Sentiment Analysis<br/><br/>Case Study: Determining Online News Popularity<br/><br/> <br/><br/>Chapter 16 Neural Network Models (Deep Learning)<br/><br/>16.1 Steps for Building a Neural Network Model<br/><br/>16.2 Multilayer Perceptrons Model (2D Tensor)<br/><br/>Case Study: Measuring Quality of Products for Acceptance or Rejection<br/><br/>16.3 Recurrent Neural Network Model (3D Tensor)<br/><br/>Case Study: Financial Market Analysis<br/><br/>16.4 Convolutional Neural Network Model (4D Tensor)<br/><br/>Case Study: Facial Recognition in Security Systems<br/><br/>Answers to Objective Type Questions<br/><br/>Index<br/><br/>  
<br/><br/> Data analysis is the method of examining, cleansing, and modeling with the objective of determining useful information for effective decision-making and operations. It includes diverse techniques and tools and plays a major role in different business, science and social science areas. R software provides numerous functions and packages for using different techniques for producing desired outcome. Data Analytics with R will enable readers gain sufficient knowledge and experience to perform analysis using different analytical tools available in R. Each chapter begins with a number of important and interesting examples taken from a variety of sectors. 
<br/><br/><label>ISBN: </label>9788126576463 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data mining<br/>Information visualization<br/>R (Computer program language)
<br/><br/><label>Dewey Class. No.: </label>005.133  / MOT