Contents – End-to-End Data Science with SAS

Contents

Contents

About This Book

About The Author

Chapter 1: Data Science Overview

Introduction to This Book

The Current Data Science Landscape

Introduction to Data Science Concepts

Chapter Review

Chapter 2: Example Step-by-Step Data Science Project

Overview

Business Opportunity

Initial Questions

Get the Data

Select a Performance Measure

Train / Test Split

Target Variable Analysis

Predictor Variable Analysis

Adjusting the TEST Data Set

Building a Predictive Model

Decision Time

Implementation

Chapter Review

Chapter 3: SAS Coding

Overview

Get Data

Explore Data

Manipulate Data

Export Data

Chapter 4: Advanced SAS Coding

Overview

DO Loop

ARRAY Statements

SCAN Function

FIND Function

PUT Function

FIRST. and LAST. Statements

Macros Overview

Macro Variables

Macros

Defining and Calling Macros

Chapter Review

Chapter 5: Create a Modeling Data Set

Overview

ETL

Extract

Data Set

Transform

Load

Chapter Review

Chapter 6: Linear Regression Models

Overview

Regression Structure

Gradient Descent

Linear Regression Assumptions

Linear Regression

Simple Linear Regression

Multiple Linear Regression

Regularization Models

Chapter Review

Chapter 7: Parametric Classification Models

Overview

Classification Overview

Logistic Regression

Visualization

Logistic Regression Model

Linear Discriminant Analysis

Chapter Review

Chapter 8: Non-Parametric Models

Overview

Modeling Data Set

K-Nearest Neighbor Model

Tree-Based Models

Random Forest

Gradient Boosting

Support Vector Machine (SVM)

Neural Networks

Chapter Review

Chapter 9: Model Evaluation Metrics

Overview

General Information

Model Output

Accuracy Statistics

Black-Box Evaluation Tools

Chapter Review