{"product_id":"data-analytics-with-r-9788126576463","title":"Data Analytics with R","description":"\u003cp\u003e • Author(s): Dr. Bharti Motwani\u003cbr\u003e • Publisher: Wiley\u003cbr\u003e • Publisher Imprint: Wiley\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eData 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. \u003c\/p\u003e\n\n\u003cp\u003ePART 1 Basics of R \u003c\/p\u003e\n\n\u003cp\u003eChapter 1 Introduction to R\u003c\/p\u003e\n\n\u003cp\u003e1.1 Features of R\u003c\/p\u003e\n\n\u003cp\u003e1.2 Installation of R\u003c\/p\u003e\n\n\u003cp\u003e1.3 Getting Started\u003c\/p\u003e\n\n\u003cp\u003e1.4 Variables in R\u003c\/p\u003e\n\n\u003cp\u003e1.5 Input of Data \u003c\/p\u003e\n\n\u003cp\u003e1.6 Output in R \u003c\/p\u003e\n\n\u003cp\u003e1.7 In-Built Functions in R \u003c\/p\u003e\n\n\u003cp\u003e1.8 Packages in R\u003c\/p\u003e\n\n\u003cp\u003eChapter 2 Data Types of R \u003c\/p\u003e\n\n\u003cp\u003e2.1 Vectors \u003c\/p\u003e\n\n\u003cp\u003e2.2 Matrices \u003c\/p\u003e\n\n\u003cp\u003e2.3 Arrays \u003c\/p\u003e\n\n\u003cp\u003e2.4 Lists \u003c\/p\u003e\n\n\u003cp\u003e2.5 Factors\u003c\/p\u003e\n\n\u003cp\u003e2.6 Data Frame\u003c\/p\u003e\n\n\u003cp\u003eChapter 3 Programming in R \u003c\/p\u003e\n\n\u003cp\u003e3.1 Decision-Making Structures \u003c\/p\u003e\n\n\u003cp\u003e3.2 Loops \u003c\/p\u003e\n\n\u003cp\u003e3.3 User-Defined Functions \u003c\/p\u003e\n\n\u003cp\u003e3.4 User-Defined Package \u003c\/p\u003e\n\n\u003cp\u003e3.5 Reports using Rmarkdown \u003c\/p\u003e\n\n\u003cp\u003eChapter 4 Data Exploration and Manipulation \u003c\/p\u003e\n\n\u003cp\u003e4.1 Missing Data Management \u003c\/p\u003e\n\n\u003cp\u003e4.2 Data Reshaping through Melting and Casting \u003c\/p\u003e\n\n\u003cp\u003e4.3 Special Functions across Data Elements \u003c\/p\u003e\n\n\u003cp\u003eChapter 5 Import and Export of Data\u003c\/p\u003e\n\n\u003cp\u003e5.1 Import and Export of Data in Text File\u003c\/p\u003e\n\n\u003cp\u003e5.2 Import and Export of Data in Excel\u003c\/p\u003e\n\n\u003cp\u003e5.3 Import and Export of Data in XML\u003c\/p\u003e\n\n\u003cp\u003e5.4 Import and Export of Data in JSON\u003c\/p\u003e\n\n\u003cp\u003e5.5 Import and Export of Data in MySQL\u003c\/p\u003e\n\n\u003cp\u003e5.6 Import and Export of Data in SPSS\u003c\/p\u003e\n\n\u003cp\u003e5.7 Import and Export of Data in SAS\u003c\/p\u003e\n\n\u003cp\u003ePART 2 Visualization Techniques \u003c\/p\u003e\n\n\u003cp\u003eChapter 6 Basic Visualization\u003c\/p\u003e\n\n\u003cp\u003e6.1 Pie Chart\u003c\/p\u003e\n\n\u003cp\u003e6.2 Bar Chart\u003c\/p\u003e\n\n\u003cp\u003e6.3 Histograms\u003c\/p\u003e\n\n\u003cp\u003e6.4 Line Chart\u003c\/p\u003e\n\n\u003cp\u003e6.5 Kernel Density Plots\u003c\/p\u003e\n\n\u003cp\u003e6.6 Quantile-Quantile (Q-Q) Plot\u003c\/p\u003e\n\n\u003cp\u003e6.7 Box-and-Whisker Plot\u003c\/p\u003e\n\n\u003cp\u003e6.8 Violin Plot\u003c\/p\u003e\n\n\u003cp\u003e6.9 Dot Chart\u003c\/p\u003e\n\n\u003cp\u003e6.10 Bubble Plot\u003c\/p\u003e\n\n\u003cp\u003e6.11 Image Plot\u003c\/p\u003e\n\n\u003cp\u003e6.12 Mosaic Plot\u003c\/p\u003e\n\n\u003cp\u003eChapter 7 Advanced Visualization \u003c\/p\u003e\n\n\u003cp\u003e7.1 Scatter Plot \u003c\/p\u003e\n\n\u003cp\u003e7.2 Corrgrams \u003c\/p\u003e\n\n\u003cp\u003e7.3 Star and Segment Plots \u003c\/p\u003e\n\n\u003cp\u003e7.4 Tree Maps \u003c\/p\u003e\n\n\u003cp\u003e7.5 Heat Map \u003c\/p\u003e\n\n\u003cp\u003e7.6 Perspective and Contour Plot \u003c\/p\u003e\n\n\u003cp\u003e7.7 Using ggplot2 for Advanced Graphics \u003c\/p\u003e\n\n\u003cp\u003ePART 3 Statistical Analysis \u003c\/p\u003e\n\n\u003cp\u003eChapter 8 Basic Statistics \u003c\/p\u003e\n\n\u003cp\u003e8.1 Descriptive Statistics \u003c\/p\u003e\n\n\u003cp\u003e8.2 Table in R \u003c\/p\u003e\n\n\u003cp\u003e8.3 Correlation and Covariance \u003c\/p\u003e\n\n\u003cp\u003e8.4 Simulation and Distributions \u003c\/p\u003e\n\n\u003cp\u003e8.5 Reproducing Same Data \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Web Analytics using Goal Funnels: Asset for e-Commerce Business \u003c\/p\u003e\n\n\u003cp\u003eChapter 9 Compare Means \u003c\/p\u003e\n\n\u003cp\u003e9.1 Parametric Techniques \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Green Building Certification \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Comparison of Personal Web Store and Marketplaces for Online Selling \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Effect of Training Program on Employee Performance \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Effect of Demographics on Online Mobile Shopping Apps \u003c\/p\u003e\n\n\u003cp\u003e9.2 Non-Parametric Tests \u003c\/p\u003e\n\n\u003cp\u003eChapter 10 Time-Series Models \u003c\/p\u003e\n\n\u003cp\u003e10.1 Time-Series Object in R \u003c\/p\u003e\n\n\u003cp\u003e10.2 Smoothing \u003c\/p\u003e\n\n\u003cp\u003e10.3 Seasonal Decomposition \u003c\/p\u003e\n\n\u003cp\u003e10.4 ARIMA Modeling \u003c\/p\u003e\n\n\u003cp\u003e10.5 Survival Analysis \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Foreign Trade in India \u003c\/p\u003e\n\n\u003cp\u003ePART 4 Machine Learning \u003c\/p\u003e\n\n\u003cp\u003eChapter 11 Unsupervised Machine Learning Algorithms \u003c\/p\u003e\n\n\u003cp\u003e11.1 Dimensionality Reduction \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Balanced Scorecard Model for Measuring Organizational Performance \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Employee Attrition in an Organization \u003c\/p\u003e\n\n\u003cp\u003e11.2 Clustering \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Market Capitalization Categories \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Performance Appraisal in Organizations \u003c\/p\u003e\n\n\u003cp\u003eChapter 12 Supervised Machine Learning Problems \u003c\/p\u003e\n\n\u003cp\u003e12.1 Regression \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Relationship between Buying Intention and Awareness of Electric Vehicles \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Application of Technology Acceptance Model in Cloud Computing \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Impact of Social Networking Websites on Quality of Recruitment \u003c\/p\u003e\n\n\u003cp\u003e12.2 Classification \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Prediction of Customer Buying Intention due to Digital Marketing \u003c\/p\u003e\n\n\u003cp\u003eChapter 13 Supervised Machine Learning Algorithms \u003c\/p\u003e\n\n\u003cp\u003e13.1 Naïve Bayes Algorithm\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Measuring Acceptability of a New Product\u003c\/p\u003e\n\n\u003cp\u003e13.2 k-Nearest Neighbor’s (KNN) Algorithm\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Predicting Phishing Websites\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Loan Categorization\u003c\/p\u003e\n\n\u003cp\u003e13.3 Support Vector Machines (SVMs)\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Fraud Analysis for Credit Card and Mobile Payment Transactions\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Diagnosis and Treatment of Diseases\u003c\/p\u003e\n\n\u003cp\u003e13.4 Decision Trees\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Occupancy Detection in Buildings\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Artificial Intelligence and Employment\u003c\/p\u003e\n\n\u003cp\u003eChapter 14 Supervised Machine Learning Ensemble Techniques\u003c\/p\u003e\n\n\u003cp\u003e14.1 Bagging\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Measuring Customer Satisfaction related to Online Food Portals\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Predicting Income of a Person\u003c\/p\u003e\n\n\u003cp\u003e14.2 Random Forest\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Writing Recommendation\/Approval Reports\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Prediction of Sports Results\u003c\/p\u003e\n\n\u003cp\u003e14.3 Gradient Boosting\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Impact of Online Reviews on Buying Behavior\u003c\/p\u003e\n\n\u003cp\u003eCase Study: Effective Vacation Plan through Online Services\u003c\/p\u003e\n\n\u003cp\u003eChapter 15 Machine Learning for Text Data \u003c\/p\u003e\n\n\u003cp\u003e15.1 Text Mining \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Spam Protection and Filtering \u003c\/p\u003e\n\n\u003cp\u003e15.2 Sentiment Analysis \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Determining Online News Popularity \u003c\/p\u003e\n\n\u003cp\u003eChapter 16 Neural Network Models (Deep Learning) \u003c\/p\u003e\n\n\u003cp\u003e16.1 Steps for Building a Neural Network Model \u003c\/p\u003e\n\n\u003cp\u003e16.2 Multilayer Perceptrons Model (2D Tensor) \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Measuring Quality of Products for Acceptance or Rejection \u003c\/p\u003e\n\n\u003cp\u003e16.3 Recurrent Neural Network Model (3D Tensor) \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Financial Market Analysis \u003c\/p\u003e\n\n\u003cp\u003e16.4 Convolutional Neural Network Model (4D Tensor) \u003c\/p\u003e\n\n\u003cp\u003eCase Study: Facial Recognition in Security Systems \u003c\/p\u003e\n\n\u003cp\u003eAnswers to Objective Type Questions \u003c\/p\u003e\n\n\u003cp\u003eIndex\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Paperback","offer_id":45207153049751,"sku":"9788126576463","price":750.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9788126576463.webp?v=1769209106","url":"https:\/\/atlanticbooks.com\/products\/data-analytics-with-r-9788126576463","provider":"Atlantic Books","version":"1.0","type":"link"}