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Data analytics with R (Record no. 4650)

MARC details
000 -LEADER
fixed length control field	05424nam a22001937a 4500
005 - DATE & TIME
control field	20190809163121.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field	190809b xxu\|\|\|\|\| \|\|\|\| 00\| 0 eng d
020 ## - ISBN
International Standard Book Number	9788126576463
Price	659
040 ## - CATALOGING SOURCE
Original cataloging agency	S.X.U.K
041 ## - Language
Language	English
082 ## - DDC NUMBER
Classification number	005.7 MOT(DAT)
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	Motwani, Bharti
245 ## - TITLE STATEMENT
Title	Data analytics with R
Statement of responsibility	Bharti Motwani
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc	New Delhi
Name of publisher, distributor, etc	Wiley
Date of publication, distribution, etc	c2019
300 ## - PHYSICAL DESCRIPTION
Pages	xvii; 646
Other Details	P.B
500 ## - GENERAL NOTE
General note	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
650 ## - Subject
Subject	DATA ANALYTICS WITH R
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	Commerce Books

Holdings
Source of classification or shelving scheme	Location (home branch)	Sublocation or collection (holding branch)	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Serial Enumeration / chronology	Koha issues (times borrowed)	Koha full call number	Barcode (Accession No.)	Koha date last seen	Koha date last borrowed	Copy Number	Price effective from	Koha item type
Dewey Decimal Classification	St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Lending Section	08/08/2019	Bharat	659.00	S.X.U.K	6	005.7 MOT(DAT)C3	L3326	12/19/2022	12/15/2022	4826;C3	08/09/2019	Commerce Books
Dewey Decimal Classification	St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Lending Section	08/08/2019	Bharat	659.00	S.X.U.K	3	005.7 MOT(DAT)C2	L3325	06/26/2023	08/02/2022	4825;C2	08/09/2019	Commerce Books
Dewey Decimal Classification	St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Lending Section	08/08/2019	Bharat	659.00	S.X.U.K	8	005.7 MOT(DAT)C1	L3324	04/24/2023	03/30/2023	4828;C1	08/09/2019	Commerce Books
Dewey Decimal Classification	St. Xavier's University, Kolkata	St. Xavier's University, Kolkata	Lending Section	08/08/2019	Bharat	569.00	S.X.U.K	7	005.7 MOT(DAT)	L3323	03/15/2024	03/05/2024	4827	08/09/2019	Commerce Books