| Description |
1 online resource (280 pages) |
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text file |
| Contents |
Cover; Half Title Page; Title Page; Copyright Page; About the Author; Dedication; Table of Contents; Preface; Chapter 1 Data Environment; 1.1 Introduction; 1.2 Python; 1.3 R -- Statistical Programming Language; 1.4 Bash Shell; 1.5 Inspect Data; Chapter 2 Statistics: A Primer; 2.1 Single Variable: Shape and Distribution; 2.2 Binomial Distribution; 2.3 Normal Distribution; 2.4 Poisson Distribution; 2.5 Discrete Uniform Distribution; 2.6 Continuous Uniform Distribution; 2.7 Conclusions; Chapter 3 Data Analysis -- Concepts; 3.1 Structured Data; 3.2 Rectangular Data; 3.3 Dataframes; 3.4 Graph Data |
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3.5 Estimates Of Location3.6 Mean; 3.7 Median And Robust Estimates; 3.8 Estimates Of Variability; 3.9 Standard Deviation And Related Estimates; 3.10 Conclusions; Chapter 4 Data Science With Python And R; 4.1 Dataframe; 4.2 Reading The Files; 4.3 Indexing And Slicing; 4.4 Data Selection; 4.5 Function Mapping And Grouping; 4.6 Aggregate; 4.7 Conclusions; Chapter 5 Error Analysis; 5.1 Uncertainties In Data; 5.2 Propagation of Errors; 5.3 Conclusions; Chapter 6 Principal Component Analysis; 6.1 Preparing Our TB Data; 6.2 Using R For PCA; 6.3 Exploring Data Structure With K-Means Clustering |
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6.4 Cluster Interpretation6.5 Centroids Comparison Chart; 6.6 A Second Level of Clustering; 6.7 Conclusions; Chapter 7 Cluster Analysis; 7.1 Cluster Analysis; 7.2 Data Preparation; 7.3 Types of Clustering; 7.4 Determine The Number of Clusters In K-Means Clustering; 7.5 Hierarchical Clustering (Agglomerative Clustering); 7.6 Clustering Algorithms; 7.7 Determine The Number of Clusters In Hierarchical Clustering; 7.8 Interpretation of Results; 7.9 Best Approach: Combination of Both Techniques; 7.10 Assess Clustering Tendency (Clusterability); 7.11 Determine The Optimal Number Of Clusters |
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7.12 Clustering For Mixed Data7.13 Cluster Analysis (Numeric Variables) In R; 7.14 Conclusions; Chapter 8 Dimensionality Reduction; 8.1 Introduction; 8.2 Reduce Dimensions -- But Why?; 8.3 Remove Redundant Variables; 8.4 Random Forest; 8.5 Feature Selection With Random Forest; 8.6 Conclusions; Chapter 9 Regression; 9.1 Introduction; 9.2 When To Use Correlation and Regression; 9.3 Null Hypothesis; 9.4 Independent Vs Dependent Variables; 9.5 How The Test Works; 9.6 Linear Regression; 9.7 Standardized Coefficients; 9.8 Measures of Model Performance; 9.9 R Script: Linear Regression |
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9.10 Understanding AIC and BIC9.11 Calculating Variance Inflation Factor (VIF); 9.12 K-Fold Cross-Validation; 9.13 Conclusions; Chapter 10 Sentiment Analysis; 10.1 Sentiment Analysis; 10.2 Sentiment Analysis With Machine Learning In R; 10.3 Sentiment Analysis For Tweets; 10.4 Conclusions; Chapter 11 Support Vector Machines; 11.1 Introducing Support Vector Machine (SVM; 11.2 Maximum Margin Classifiers; 11.3 Support Vector Machine Simplified; 11.4 SVM: Nonlinear Separable Data; 11.5 How SVM Works; 11.6 SVM -- Standardization; 11.7 Tuning Parameters of SVM |
| Note |
11.8 R Code: Support Vector Machine (SVM |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Data reduction.
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Data reduction. |
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Data mining.
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Data mining. |
| Genre/Form |
Electronic books.
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| Other Form: |
Print version: Raghavender, U.S. Data Reduction and Analysis. Ashland : Arcler Press, ©2019 |
| ISBN |
1773615742 |
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9781773615745 (electronic book) |
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