Beginning Data Science with R - Manas A. Pathak

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Résumé :
?We live in the age of data. In the last few years, the methodology of extracting insights from data or data science has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of ?Beginning Data Science with R? is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.

Biographie:
Dr. Manas A. Pathak received a BTech degree in computer science from Visvesvaraya National Institute of Technology, Nagpur, India, in 2006, and MS and PhD degrees from the Language Technologies Institute at Carnegie Mellon University (CMU) in 2009 and 2012 respectively. His PhD thesis on Privacy-Preserving Machine Learning for Speech Processing was published as a monograph in the Springer best thesis series. His research received significant press coverage, including articles in the Economist and MIT Tech Review. He has many years of experience with data analysis using the R programming language. He is currently working as a staff software engineer at @WalmartLabs....

Sommaire:
4.4 Interactive Visualizations using Shiny 4.5 Chapter Summary & Further Reading References 5 Exploratory Data Analysis 5.1 Summary Statistics 5.1.1 Dataset Size 5.1.2 Summarizing the Data 5.1.3 Ordering Data by a Variable 5.1.4 Group and Split Data by a Variable 5.1.5 Variable Correlation 5.2 Getting a sense of data distribution 5.2.1 Box plots 5.2.2 Histograms 5.2.3 Measuring Data Symmetry using Skewness and Kurtosis 5.3 Putting it all together: Outlier Detection 5.4 Chapter Summary References 6 Regression 6.1 Introduction 6.1.1 Regression Models 6.2 Parametric Regression Models 6.2.1 Simple Linear Regression 6.2.2 Multivariate Linear Regression 6.2.3 Log-Linear Regression Models 6.3 Non-Parametric Regression Models 6.3.1 Locally Weighted Regression 6.3.2 Kernel Regression 6.3.3 Regression Trees 6.4 Chapter Summary References 7 Classification 7.1 Introduction 7.1.1 Training and Test Datasets 7.2 Parametric Classification Models 7.2.1 Naive Bayes 7.2.2 Logistic Regression 7.2.3 Support Vector Machines 7.3 Non-Parametric Classification Models 7.3.1 Nearest Neighbors 7.3.2 Decision Trees 7.4 Chapter Summary References 8 Text Mining 8.1 Introduction 8.2 Reading Text Input Data 8.3 Common Text Preprocessing Tasks 8.3.1 Stop Word Removal 8.3.2 Stemming 8.4 Term Document Matrix 8.4.1 TF-IDF Weighting Function 8.5 Text Mining Applications 8.5.1 Frequency Analysis 8.5.2 Text Classification 8.6 Chapter Summary