Introduction to Data Science Using ‘R’, Second Edition

<div><!--block-->Dive into the world of data science with "Introduction to Data Science Using R, 2nd Edition." This comprehensive guide transcends conventional statistical and computer science textbooks by offering a fresh perspective on the essential skills needed for a successful career in data science.From demystifying basic statistics concepts to equipping you with fundamental programming abilities in R, this book is your gateway to understanding the distinctiveness and value of data science in contemporary organizations grappling with big data. Delve into the three core components of the book: an enlightening overview of data science's interdisciplinary nature, practical applications of machine learning algorithms for predictive insights, and hands-on R programming exercises tailored for aspiring data scientists.Unraveling the importance of data science through diverse real-world examples spanning various domains, this book seamlessly integrates statistical and machine learning theories with practical R programming commands. Embark on a journey of discovery with engaging case studies that blend statistical insights with business acumen, decision science, and data engineering prowess.Get ready to embark on a thrilling data science odyssey filled with knowledge, practical applications, and illuminating case studies in "Introduction to Data Science Using R, 2nd Edition."<br><strong>Contents:<br>1.</strong>Data Science: Key Concepts<strong>2.</strong>Data Wrangling<strong>3.</strong>Spotting Signals: An Overview<strong>4.1.</strong> Introduction to R<strong>4.2.</strong> Business Storytelling Using R<strong>5.1.</strong> Problem based Analysis<strong>5.2.</strong> Model<strong>6.1.</strong> Bivariate Analysis<strong>6.2.</strong> Cross Tabs<strong>7.</strong> Correlation Matrix<strong>8.1.</strong> Visualization and Visual Constructs<strong>8.2.</strong> Advance Visualization<strong>9.1.</strong> Machine Learning in Action<strong>9.2.</strong> Decision Trees<strong>9.3.</strong> Support Vector Machines<strong>9.4.</strong> Naive Bayes<strong>9.5.</strong> Linear Regression<strong>9.6.</strong> Regression<strong>9.7.</strong> A/B Testing<strong>9.8.</strong> Classification<strong>9.9.</strong> Introduction to Gradient Boosting<strong>10.1.</strong> Sample Preparation<strong>10.2.</strong> Data Train and Test Data<strong>11.1.</strong> Multivariate Analysis Topics<strong>11.2.</strong> Principal Component Analysis<strong>11.3.</strong> Factor Analysis<strong>11.4.</strong> ANOVA<strong>12.1.</strong> Additional Topics in Analytics<strong>12.2.</strong> Exploratory Data Analysis Case Study – Business Perspective<strong>13.</strong> Text Mining<br><strong>About the Author:<br>Prema Alla</strong> is a freelance consultant and her company Prema Consulting engages in online training in data analytics. She is also a Guest Faculty for various colleges and universities in Hyderabad. At Symbiosis university she undertakees guest lectures for students in Master programs offered by the Department of Statistics. In colleges, St Anns and Indian Institute of Management and Commerce she teaches students in bachelor of Arts and science the analytics programme offered by their university.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; She is also an educational consultant for a college in USA, IGLOBAL University. She widely caters to the US online market and trains professionals online and works closely with the following clients Shreetek Inc and AR Solutions Inc.&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Her research interests include statistical modeling, all aspects of missing data, linear models, regression analysis, econometrics.</div>