| Description |
1 online resource |
| Contents |
Intro -- Content -- About This Book -- What Does This Book Cover? -- Is This Book for You? -- SAS Software Requirements -- Programming Knowledge Assumed -- Icons Used in This Book -- Example Code and Data -- SAS University Edition -- We Want to Hear from You -- Author Acknowledgments -- About The Author -- Chapter 1: Data Science Overview -- Introduction to This Book -- Minimum Effective Dose -- The Current Data Science Landscape -- Types of Analytics -- Figure 1.1: Eight Levels of Analytics -- Data Science Skills -- Figure 1.2: Data Science Venn Diagram |
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Introduction to Data Science Concepts -- Supervised Versus Unsupervised -- Supervised Models -- Table 1.1: Wine Quality Data -- Table 1.2: Wine Data Set Predictive Weights -- Unsupervised Models -- Figure 1.3: Clustering Model Visualization -- Machine Learning Categories -- Figure 1.4: Machine Learning Categories -- Parametric Versus Non-parametric -- Figure 1.5: Data Distribution Types -- Parametric Models -- Non-Parametric Models -- Table 1.3: Parametric versus Non-Parametric Models -- Regression Versus Classification -- Table 1.4: Regression and Classification Models |
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Overfitting Versus Underfitting -- Overfitting -- Figure 1.6: Simple Linear Relationship -- Figure 1.7: High Degree Polynomial Model -- Underfitting -- Figure 1.8: Lower Degree Polynomial Model -- Batch Versus Online Learning -- Batch Models -- Online Learning Models -- Bias-Variance Tradeoff -- Bias -- Variance -- Figure 1.9: Optimal Model Complexity -- Training and Testing Data Sets -- Figure 1.10: Bias-Variance Tradeoff -- Step-by-Step Example of Finding Optimal Model Complexity -- Step 1 -- Simple Linear Regression -- Figure 1.11: Simple Linear Regression |
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Step 2 -- Linear Regression with Two Variables -- Figure 1.12: Linear Regression with Two Variables -- Step 3 -- Linear Regression with Three Variables -- Figure 1.13: Linear Regression with Three Variables -- Step 4 -- Linear Regression with Four Variables -- Figure 1.14: Linear Regression with Four Variables -- Step 5 -- Linear Regression with Five Variables -- Figure 1.15: Linear Regression with Five Variables -- Step 6 -- Linear Regression with Six Variables -- Figure 1.16: Linear Regression with Six Variables -- Step 7 -- Optimal Linear Regression Model |
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Figure 1.17: Optimal Linear Regression Model -- Curse of Dimensionality -- Figure 1.18: Dimension Increase -- Table 1.5: Consistent Density in High-Dimensional Space -- Hughes Phenomenon -- Figure 1.19: Hughes Phenomenon -- Transparent Versus Black Box Models -- Ethics -- No Free Lunch -- Chapter Review -- Chapter 2: Example Step-by-Step Data Science Project -- Overview -- Business Opportunity -- Initial Questions -- What is the business opportunity? -- Do we have the data to support this project? -- What type of work has been done previously on this type of problem? -- Study #1 -- Takeaway |
| Summary |
Learn data science concepts with real-world examples in SAS! End-to-End Data Science with SAS: A Hands-On Programming Guide provides clear and practical explanations of the data science environment, machine learning techniques, and the SAS programming knowledge necessary to develop machine learning models in any industry. The book covers concepts including understanding the business need, creating a modeling data set, linear regression, parametric classification models, and non-parametric classification models. Real-world business examples and example code are used to demonstrate each process |
| SUBJECT |
SAS (Computer file) http://id.loc.gov/authorities/names/n88028236
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SAS (Computer file) fast (OCoLC)fst01364029 |
| Subject |
Data mining -- Statistical methods.
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Machine learning.
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Electronic data processing.
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Data mining -- Statistical methods.
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Electronic data processing.
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Machine learning.
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| Form |
Electronic book
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| ISBN |
1642958050 |
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9781642958058 |
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1642958069 |
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9781642958065 |
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