Data science using Python and R. (2019)

• Record Type: Book
• Further Information: Note: Chantal D. Larose, Daniel T. Larose.
• Authors: Larose, Chantal D; Larose, Daniel T
• Contents: Preface Acknowledgements Chapter 1 Introduction to Data Science Why Data Science? What is Data Science? The Data Science Methodology Problem Understanding Phase Data Preparation Phase Exploratory Data Analysis Phase Setup Phase Modeling Phase Evaluation Phase Deployment Phase Data Science Tasks Description Estimation Classification Clustering Prediction Association Exercises Chapter 2 The Basics of Python and R Downloading Python 14 Basics of Coding in Python Using Comments in Python Executing Commands in Python Importing Packages in Python Getting Data into Python Saving Output in Python Accessing Records and Variables in Python Setting up Graphics in Python Downloading R and RStudio Basics of Coding in R Using Comments in R Executing Commands in R Importing Packages in R Getting Data into R Saving Output in R Accessing Records and Variables in R Exercises Chapter 3 Data Preparation The Bank Marketing Data Set The Problem Understanding Phase Clearly Enunciate the Project Objectives Translate These Objectives into a Data Science Problem Data Preparation Phase Adding an Index Field How to Add an Index Field Using Python How to Add an Index Field Using R Changing Misleading Field Values How to Change Misleading Field Values Using Python How to Change Misleading Field Values Using R Re-Expression of Categorical Data as Numeric How to Re-Express Categorical Field Values Using Python How to Re-Express Categorical Field Values Using R Standardizing the Numeric Fields How to Standardize Numeric Fields Using Python How to Standardize Numeric Fields Using R Identifying Outliers Using Z-Values How to Identify Outliers Using Python How to Identify Outliers Using R Exercises Chapter 4 Exploratory Data Analysis EDA versus HT Bar Graphs with Response Overlay How to Construct a Bar Graph with Overlay Using Python How to Construct a Bar Graph with Overlay Using R Contingency Tables How to Construct Contingency Tables Using Python How to Construct Contingency Tables Using R Histograms with Response Overlay How to Construct Histograms with Response Overlay Using Python How to Construct Histograms with Response Overlay Using R Binning Based on Predictive Value How to Perform Binning Based on Predictive Value Using Python How to Perform Binning Based on Predictive Value Using R Exercises Chapter 5 Preparing to Model the Data The Story So Far Partitioning the Data How to Partition the Data in Python How to Partition the Data in R Validating Your Partition Balancing the Training Data Set How to Balance the Training Data Set in Python How to Balance the Training Data Set in R Establishing Baseline Model Performance Exercises Chapter 6 Decision Trees Introduction to Decision Trees Classification and Regression Trees (CART) How to Build CART Decision Trees Using Python How to Build CART Decision Trees Using R The C5.0 Algorithm for Building Decision Trees How to Build C5.0 Decision Trees Using Python How to Build C5.0 Decision Trees Using R Random Forests How to Build Random Forests Using Python How to Build Random Forests Using R Exercises Chapter 7 Model Evaluation Introduction to Model Evaluation Classification Evaluation Measures Sensitivity and Specificity Precision, Recall, and F_β Scores Method for Model Evaluation An Application of Model Evaluation How to Perform Model Evaluation Using R How to Perform Model Evaluation Using Python Accounting for Unequal Error Costs Accounting for Unequal Error Costs Using R Comparing Models with and without Unequal Error Costs Data-Driven Error Costs Exercises Chapter 8 Naïve Bayes Classification Introduction to Naïve Bayes Bayes Theorem Maximum a Posteriori Hypothesis Class Conditional Independence Application of Naïve Bayes Classification Naïve Bayes in Python Naïve Bayes in R Exercises Chapter 9 Neural Networks Introduction to Neural Networks The Neural Network Structure Connection Weights and the Combination Function The Sigmoid Activation Function Back-Propagation An Application of a Neural Network Model How to Use Neural Networks in R Exercises Chapter 10 Clustering What is Clustering? Introduction to the k-Means Clustering Algorithm An Application of k-Means Clustering Cluster Validation How to Perform k-Means Clustering Using Python How to Perform k-Means Clustering Using R Exercises Chapter 11 Regression Modeling The Estimation Task Descriptive Regression Modeling An Application of Multiple Regression Modeling How to Perform Multiple Regression Modeling Using Python How to Perform Multiple Regression Modeling Using R Model Evaluation for Estimation How to Perform Estimation Model Evaluation Using Python How to Perform Estimation Model Evaluation Using R Stepwise Regression How to Perform Stepwise Regression Using R Baseline Models for Regression Exercises Chapter 12 Dimension Reduction The Need for Dimension Reduction Multicollinearity Identifying Multicollinearity Using Variance-Inflation Factors How to Identify Multicollinearity Using Python How to Identify Multicollinearity Using R Principal Components Analysis An Application of Principal Components Analysis How Many Components Should We Extract? The Eigenvalue Criterion The Proportion of Variance Explained Criterion Performing PCA with k = 4 Validation of the Principal Components How to Perform Principal Components Analysis Using Python How to Perform Principal Components Analysis Using R When is Multicollinearity Not a Problem? Exercises Chapter 13 Generalized Linear Models An Overview of General Linear Models Linear Regression as a General Linear Model Logistic Regression as a General Linear Model An Application of Logistic Regression Modeling How to Perform Logistic Regression Using Python How to Perform Logistic Regression Using R Poisson Regression An Application of Poisson Regression How to Perform Poisson Regression Using Python How to Perform Poisson Regression Using R Exercises Chapter 14 Association Rules Introduction to Association Rules A Simple Example of Association Rule Mining Support, Confidence, and Lift Mining Association Rules How to Mine Association Rules Using R Confirming Our Metrics <p&g
• Edition: 1st
• Publisher Details: Hoboken, New Jersey : John Wiley & Sons, Inc
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 006.312; Data mining; Python (Computer program language); R (Computer program language); Big data; Data structures (Computer science)
• Languages: English
• ISBNs: 9781119526841
• Related ISBNs: 9781119526834
• Notes: Note: Description based on CIP data; resource not viewed.
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