END-TO-END DATA SCIENCE WITH SAS a hands-on programming guide;a hands-on programming guide.

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...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: GEARHEART, JAMES
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [S.l.] : SAS INSTITUTE, 2020.
Temas:
SAS (Computer file)
Data mining > Statistical methods.
Machine learning.
Electronic data processing.
Apprentissage automatique.
Data mining > Statistical methods
Electronic data processing
Machine learning
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

Ejemplares similares