Data Analytics with R

• ISBN13: 9788126576463
• Binding: Paperback
• Subject: N/A
• Publisher: Wiley
• Publisher Imprint: Wiley
• Publication Date: March 18, 2019
• Pages: 664
• Original Price: INR 999.0
• Language: English
• Edition: N/A
• Item Weight: 500 grams
• BISAC Subject(s): N/A

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. PART 1 Basics of R Chapter 1 Introduction to R 1.1 Features of R 1.2 Installation of R 1.3 Getting Started 1.4 Variables in R 1.5 Input of Data 1.6 Output in R 1.7 In-Built Functions in R 1.8 Packages in R Chapter 2 Data Types of R 2.1 Vectors 2.2 Matrices 2.3 Arrays 2.4 Lists 2.5 Factors 2.6 Data Frame Chapter 3 Programming in R 3.1 Decision-Making Structures 3.2 Loops 3.3 User-Defined Functions 3.4 User-Defined Package 3.5 Reports using Rmarkdown Chapter 4 Data Exploration and Manipulation 4.1 Missing Data Management 4.2 Data Reshaping through Melting and Casting 4.3 Special Functions across Data Elements Chapter 5 Import and Export of Data 5.1 Import and Export of Data in Text File 5.2 Import and Export of Data in Excel 5.3 Import and Export of Data in XML 5.4 Import and Export of Data in JSON 5.5 Import and Export of Data in MySQL 5.6 Import and Export of Data in SPSS 5.7 Import and Export of Data in SAS PART 2 Visualization Techniques Chapter 6 Basic Visualization 6.1 Pie Chart 6.2 Bar Chart 6.3 Histograms 6.4 Line Chart 6.5 Kernel Density Plots 6.6 Quantile-Quantile (Q-Q) Plot 6.7 Box-and-Whisker Plot 6.8 Violin Plot 6.9 Dot Chart 6.10 Bubble Plot 6.11 Image Plot 6.12 Mosaic Plot Chapter 7 Advanced Visualization 7.1 Scatter Plot 7.2 Corrgrams 7.3 Star and Segment Plots 7.4 Tree Maps 7.5 Heat Map 7.6 Perspective and Contour Plot 7.7 Using ggplot2 for Advanced Graphics PART 3 Statistical Analysis Chapter 8 Basic Statistics 8.1 Descriptive Statistics 8.2 Table in R 8.3 Correlation and Covariance 8.4 Simulation and Distributions 8.5 Reproducing Same Data Case Study: Web Analytics using Goal Funnels: Asset for e-Commerce Business Chapter 9 Compare Means 9.1 Parametric Techniques Case Study: Green Building Certification Case Study: Comparison of Personal Web Store and Marketplaces for Online Selling Case Study: Effect of Training Program on Employee Performance Case Study: Effect of Demographics on Online Mobile Shopping Apps 9.2 Non-Parametric Tests Chapter 10 Time-Series Models 10.1 Time-Series Object in R 10.2 Smoothing 10.3 Seasonal Decomposition 10.4 ARIMA Modeling 10.5 Survival Analysis Case Study: Foreign Trade in India PART 4 Machine Learning Chapter 11 Unsupervised Machine Learning Algorithms 11.1 Dimensionality Reduction Case Study: Balanced Scorecard Model for Measuring Organizational Performance Case Study: Employee Attrition in an Organization 11.2 Clustering Case Study: Market Capitalization Categories Case Study: Performance Appraisal in Organizations Chapter 12 Supervised Machine Learning Problems 12.1 Regression Case Study: Relationship between Buying Intention and Awareness of Electric Vehicles Case Study: Application of Technology Acceptance Model in Cloud Computing Case Study: Impact of Social Networking Websites on Quality of Recruitment 12.2 Classification Case Study: Prediction of Customer Buying Intention due to Digital Marketing Chapter 13 Supervised Machine Learning Algorithms 13.1 Naïve Bayes Algorithm Case Study: Measuring Acceptability of a New Product 13.2 k-Nearest Neighbor’s (KNN) Algorithm Case Study: Predicting Phishing Websites Case Study: Loan Categorization 13.3 Support Vector Machines (SVMs) Case Study: Fraud Analysis for Credit Card and Mobile Payment Transactions Case Study: Diagnosis and Treatment of Diseases 13.4 Decision Trees Case Study: Occupancy Detection in Buildings Case Study: Artificial Intelligence and Employment Chapter 14 Supervised Machine Learning Ensemble Techniques 14.1 Bagging Case Study: Measuring Customer Satisfaction related to Online Food Portals Case Study: Predicting Income of a Person 14.2 Random Forest Case Study: Writing Recommendation/Approval Reports Case Study: Prediction of Sports Results 14.3 Gradient Boosting Case Study: Impact of Online Reviews on Buying Behavior Case Study: Effective Vacation Plan through Online Services Chapter 15 Machine Learning for Text Data 15.1 Text Mining Case Study: Spam Protection and Filtering 15.2 Sentiment Analysis Case Study: Determining Online News Popularity Chapter 16 Neural Network Models (Deep Learning) 16.1 Steps for Building a Neural Network Model 16.2 Multilayer Perceptrons Model (2D Tensor) Case Study: Measuring Quality of Products for Acceptance or Rejection 16.3 Recurrent Neural Network Model (3D Tensor) Case Study: Financial Market Analysis 16.4 Convolutional Neural Network Model (4D Tensor) Case Study: Facial Recognition in Security Systems Answers to Objective Type Questions Index

Dr. Bharti Motwani is an analytical professional, IT and analytics consultant, result-driven and articulate academician who can think out of the box, with more than 21 years of experience in corporate world and academics. She has written more than 75 research papers in leading national and international journals and books, including journals indexed in Scopus. She is the recipient of Young Scientist of the Year award (2015) from the Institute of Research and Journals. She has proved dexterousness in research methodologies and software development by conducting various seminars and workshops related to latest tools in research and software and guiding various research and software projects.