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Data science using Python and R

Author: Chantal D Larose; Daniel T Larose
Publisher: Hoboken, NJ : John Wiley & Sons, Inc, 2019. ©2019
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world's two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist "the hottest job in America." Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Larose, Chantal D.
Data science using Python and R.
Hoboken, NJ : John Wiley & Sons, Inc, 2019
(DLC) 2019007280
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Chantal D Larose; Daniel T Larose
ISBN: 9781119526841 1119526841 9781119526834 1119526833 9781119526865 1119526868
OCLC Number: 1089273491
Description: 1 online resource (xvii, 238 pages)
Contents: Preface xi About the Authors xv Acknowledgements xvii Chapter 1 Introduction to Data Science 1 1.1 Why Data Science? 1 1.2 What is Data Science? 1 1.3 The Data Science Methodology 2 1.4 Data Science Tasks 5 1.4.1 Description 6 1.4.2 Estimation 6 1.4.3 Classification 6 1.4.4 Clustering 7 1.4.5 Prediction 7 1.4.6 Association 7 Exercises 8 Chapter 2 The Basics of Python and R 9 2.1 Downloading Python 9 2.2 Basics of Coding in Python 9 2.2.1 Using Comments in Python 9 2.2.2 Executing Commands in Python 10 2.2.3 Importing Packages in Python 11 2.2.4 Getting Data into Python 12 2.2.5 Saving Output in Python 13 2.2.6 Accessing Records and Variables in Python 14 2.2.7 Setting Up Graphics in Python 15 2.3 Downloading R and RStudio 17 2.4 Basics of Coding in R 19 2.4.1 Using Comments in R 19 2.4.2 Executing Commands in R 20 2.4.3 Importing Packages in R 20 2.4.4 Getting Data into R 21 2.4.5 Saving Output in R 23 2.4.6 Accessing Records and Variables in R 24 References 26 Exercises 26 Chapter 3 Data Preparation 29 3.1 The Bank Marketing Data Set 29 3.2 The Problem Understanding Phase 29 3.2.1 Clearly Enunciate the Project Objectives 29 3.2.2 Translate These Objectives into a Data Science Problem 30 3.3 Data Preparation Phase 31 3.4 Adding an Index Field 31 3.4.1 How to Add an Index Field Using Python 31 3.4.2 How to Add an Index Field Using R 32 3.5 Changing Misleading Field Values 33 3.5.1 How to Change Misleading Field Values Using Python 34 3.5.2 How to Change Misleading Field Values Using R 34 3.6 Reexpression of Categorical Data as Numeric 36 3.6.1 How to Reexpress Categorical Field Values Using Python 36 3.6.2 How to Reexpress Categorical Field Values Using R 38 3.7 Standardizing the Numeric Fields 39 3.7.1 How to Standardize Numeric Fields Using Python 40 3.7.2 How to Standardize Numeric Fields Using R 40 3.8 Identifying Outliers 40 3.8.1 How to Identify Outliers Using Python 41 3.8.2 How to Identify Outliers Using R 42 References 43 Exercises 44 Chapter 4 Exploratory Data Analysis 47 4.1 EDA Versus HT 47 4.2 Bar Graphs with Response Overlay 47 4.2.1 How to Construct a Bar Graph with Overlay Using Python 49 4.2.2 How to Construct a Bar Graph with Overlay Using R 50 4.3 Contingency Tables 51 4.3.1 How to Construct Contingency Tables Using Python 52 4.3.2 How to Construct Contingency Tables Using R 53 4.4 Histograms with Response Overlay 53 4.4.1 How to Construct Histograms with Overlay Using Python 55 4.4.2 How to Construct Histograms with Overlay Using R 58 4.5 Binning Based on Predictive Value 58 4.5.1 How to Perform Binning Based on Predictive Value Using Python 59 4.5.2 How to Perform Binning Based on Predictive Value Using R 62 References 63 Exercises 63 Chapter 5 Preparing to Model the Data 69 5.1 The Story So Far 69 5.2 Partitioning the Data 69 5.2.1 How to Partition the Data in Python 70 5.2.2 How to Partition the Data in R 71 5.3 Validating your Partition 72 5.4 Balancing the Training Data Set 73 5.4.1 How to Balance the Training Data Set in Python 74 5.4.2 How to Balance the Training Data Set in R 75 5.5 Establishing Baseline Model Performance 77 References 78 Exercises 78 Chapter 6 Decision Trees 81 6.1 Introduction to Decision Trees 81 6.2 Classification and Regression Trees 83 6.2.1 How to Build CART Decision Trees Using Python 84 6.2.2 How to Build CART Decision Trees Using R 86 6.3 The C5.0 Algorithm for Building Decision Trees 88 6.3.1 How to Build C5.0 Decision Trees Using Python 89 6.3.2 How to Build C5.0 Decision Trees Using R 90 6.4 Random Forests 91 6.4.1 How to Build Random Forests in Python 92 6.4.2 How to Build Random Forests in R 92 References 93 Exercises 93 Chapter 7 Model Evaluation 97 7.1 Introduction to Model Evaluation 97 7.2 Classification Evaluation Measures 97 7.3 Sensitivity and Specificity 99 7.4 Precision, Recall, and Fss Scores 99 7.5 Method for Model Evaluation 100 7.6 An Application of Model Evaluation 100 7.6.1 How to Perform Model Evaluation Using R 103 7.7 Accounting for Unequal Error Costs 104 7.7.1 Accounting for Unequal Error Costs Using R 105 7.8 Comparing Models with and without Unequal Error Costs 106 7.9 Data-Driven Error Costs 107 Exercises 109 Chapter 8 Naive Bayes Classification 113 8.1 Introduction to Naive Bayes 113 8.2 Bayes Theorem 113 8.3 Maximum a Posteriori Hypothesis 114 8.4 Class Conditional Independence 114 8.5 Application of Naive Bayes Classification 115 8.5.1 Naive Bayes in Python 121 8.5.2 Naive Bayes in R 123 References 125 Exercises 126 Chapter 9 Neural Networks 129 9.1 Introduction to Neural Networks 129 9.2 The Neural Network Structure 129 9.3 Connection Weights and the Combination Function 131 9.4 The Sigmoid Activation Function 133 9.5 Backpropagation 134 9.6 An Application of a Neural Network Model 134 9.7 Interpreting the Weights in a Neural Network Model 136 9.8 How to Use Neural Networks in R 137 References 138 Exercises 138 Chapter 10 Clustering 141 10.1 What is Clustering? 141 10.2 Introduction to the K-Means Clustering Algorithm 142 10.3 An Application of K-Means Clustering 143 10.4 Cluster Validation 144 10.5 How to Perform K-Means Clustering Using Python 145 10.6 How to Perform K-Means Clustering Using R 147 Exercises 149 Chapter 11 Regression Modeling 151 11.1 The Estimation Task 151 11.2 Descriptive Regression Modeling 151 11.3 An Application of Multiple Regression Modeling 152 11.4 How to Perform Multiple Regression Modeling Using Python 154 11.5 How to Perform Multiple Regression Modeling Using R 156 11.6 Model Evaluation for Estimation 157 11.6.1 How to Perform Estimation Model Evaluation Using Python 159 11.6.2 How to Perform Estimation Model Evaluation Using R 160 11.7 Stepwise Regression 161 11.7.1 How to Perform Stepwise Regression Using R 162 11.8 Baseline Models for Regression 162 References 163 Exercises 164 Chapter 12 Dimension Reduction 167 12.1 The Need for Dimension Reduction 167 12.2 Multicollinearity 168 12.3 Identifying Multicollinearity Using Variance Inflation Factors 171 12.3.1 How to Identify Multicollinearity Using Python 172 12.3.2 How to Identify Multicollinearity in R 173 12.4 Principal Components Analysis 175 12.5 An Application of Principal Components Analysis 175 12.6 How Many Components Should We Extract? 176 12.6.1 The Eigenvalue Criterion 176 12.6.2 The Proportion of Variance Explained Criterion 177 12.7 Performing Pca with K = 4 178 12.8 Validation of the Principal Components 178 12.9 How to Perform Principal Components Analysis Using Python 179 12.10 How to Perform Principal Components Analysis Using R 181 12.11 When is Multicollinearity Not a Problem? 183 References 184 Exercises 184 Chapter 13 Generalized Linear Models 187 13.1 An Overview of General Linear Models 187 13.2 Linear Regression as a General Linear Model 188 13.3 Logistic Regression as a General Linear Model 188 13.4 An Application of Logistic Regression Modeling 189 13.4.1 How to Perform Logistic Regression Using Python 190 13.4.2 How to Perform Logistic Regression Using R 191 13.5 Poisson Regression 192 13.6 An Application of Poisson Regression Modeling 192 13.6.1 How to Perform Poisson Regression Using Python 193 13.6.2 How to Perform Poisson Regression Using R 194 Reference 195 Exercises 195 Chapter 14 Association Rules 199 14.1 Introduction to Association Rules 199 14.2 A Simple Example of Association Rule Mining 200 14.3 Support, Confidence, and Lift 200 14.4 Mining Association Rules 202 14.4.1 How to Mine Association Rules Using R 203 14.5 Confirming Our Metrics 207 14.6 The Confidence Difference Criterion 208 14.6.1 How to Apply the Confidence Difference Criterion Using R 208 14.7 The Confidence Quotient Criterion 209 14.7.1 How to Apply the Confidence Quotient Criterion Using R 210 References 211 Exercises 211 Appendix Data Summarization and Visualization 215 Part 1: Summarization 1: Building Blocks of Data Analysis 215 Part 2: Visualization: Graphs and Tables for Summarizing and Organizing Data 217 Part 3: Summarization 2: Measures of Center, Variability, and Position 222 Part 4: Summarization and Visualization of Bivariate Elationships 225 Index 231
Responsibility: Chantal D. Larose, Daniel T. Larose.

Abstract:

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world's two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist "the hottest job in America." Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naIve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

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