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Book Overview & Buying
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Table Of Contents
Data Science Using Python and R
By :
Data Science Using Python and R
By:
Overview of this book
Data science is hot. Bloomberg named a data scientist as the ‘hottest job in America’. Python and R are the top two open-source data science tools using which you can produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Each chapter in the book presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. You’ll learn how to prepare data, perform exploratory data analysis, and prepare to model the data. As you progress, you’ll explore what are decision trees and how to use them. You’ll also learn about model evaluation, misclassification costs, naïve Bayes classification, and neural networks. The later chapters provide comprehensive information about clustering, regression modeling, dimension reduction, and association rules mining. The book also throws light on exciting new topics, such as random forests and general linear models. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars.
By the end of this book, you’ll have enough knowledge and confidence to start providing solutions to data science problems using R and Python.
Table of Contents (20 chapters)
PREFACEBegin Reading
ACKNOWLEDGMENTS
Chapter 1: INTRODUCTION TO DATA SCIENCE
Chapter 2: THE BASICS OF PYTHON AND R
Chapter 3: DATA PREPARATION
Chapter 4: EXPLORATORY DATA ANALYSIS
Chapter 5: PREPARING TO MODEL THE DATA
Chapter 6: DECISION TREES
Chapter 7: MODEL EVALUATION
Chapter 8: NAÏVE BAYES CLASSIFICATION
Chapter 9: NEURAL NETWORKS
Chapter 10: CLUSTERING
Chapter 11: REGRESSION MODELING
Chapter 12: DIMENSION REDUCTION
Chapter 13: GENERALIZED LINEAR MODELS
Chapter 14: ASSOCIATION RULES
INDEX
END USER LICENSE AGREEMENT
APPENDIX DATA SUMMARIZATION AND VISUALIZATION
