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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.
nFrom 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.
nUnraveling 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.
nGet 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."
nContents:
n**1.**Data Science: Key Concepts
n**2.**Data Wrangling
n**3.**Spotting Signals: An Overview
n4.1. Introduction to R
n4.2. Business Storytelling Using R
n5.1. Problem based Analysis
n5.2. Model
n6.1. Bivariate Analysis
n6.2. Cross Tabs
n7. Correlation Matrix
n8.1. Visualization and Visual Constructs
n8.2. Advance Visualization
n9.1. Machine Learning in Action
n9.2. Decision Trees
n9.3. Support Vector Machines
n9.4. Naive Bayes
n9.5. Linear Regression
n9.6. Regression
n9.7. A/B Testing
n9.8. Classification
n9.9. Introduction to Gradient Boosting
n10.1. Sample Preparation
n10.2. Data Train and Test Data
n11.1. Multivariate Analysis Topics
n11.2. Principal Component Analysis
n11.3. Factor Analysis
n11.4. ANOVA
n12.1. Additional Topics in Analytics
n12.2. Exploratory Data Analysis Case Study – Business Perspective
n13. Text Mining

Introduction to Data Science Using R

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