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Course Overview

Hello. I’m Terry Woodfield and I’m Chip Wells.

I’ve been using SAS software since 1978. Since receiving a Ph.D. in statistics in 1982 from Texas A&M University, I’ve held various academic and consulting positions related to applied analytics, data mining, and data science. I teach data mining, text mining, predictive modeling, time series forecasting, and other applied analytics courses offered by SAS Education.

I’ve been using SAS software system 1991. Since receiving a Ph.D. in Economics in 2008 from North Carolina State University, I’ve held academic and consulting positions related to applied econometrics, analytics, time series forecasting, and predictive modeling. I teach data mining, forecasting, predictive modeling, and other analytics courses offered by SAS Education.

Big data provides a challenge for many organizations. Since 1976, SAS has provided software for turning data into solutions.

This course provides tools for solving analytic problems involving very large data sets.

In the course, I describe SAS Solutions using SAS LASR Server technology, in particular, how PROC IMSTAT provides a collection of powerful exploration and prediction tools.

I talk about two different tool sets, SAS Visual Statistics and SAS Enterprise Miner. These products provide a graphical user interface to access SAS data mining technology.

SAS uses the EMSMA approach to large scale data mining: Explore, Modify, Segment, Model, Assess.

SAS Visual Statistics provides a full-featured suite of exploratory and graphical tools. You can easily derive segments to isolate homogeneous groups for further analysis.

PROC IMSTAT provides a programming environment that exploits the SAS LASR Server to utilize machine learning and statistical methodologies for solving large-scale data mining problems.

SAS Enterprise Miner tackles large-scale problems with a suite of high-performance nodes. The performance can be achieved using the SAS grid or using a single desktop computer with multiple core technology.

If you have a large data problem that can be solved using analytics, this course is meant for you.

Let’s get started.

Course Curriculum

Required Setup for Demonstrations and Exercises File
Required Setup for Demonstrations and Exercises File 00:00:00
1.1a Introduction
1.1a Introduction 00:00:00
1.1b Objectives
1.1b Objectives 00:00:00
1.2a Big Data and Its Challenges
1.2a Big Data and Its Challenges 00:00:00
1.2b Modeling Approach Modification
1.2b Modeling Approach Modification 00:00:00
1.2c SAS In-Memory Interfaces for Hadoop
1.2c SAS In-Memory Interfaces for Hadoop 00:00:00
1.2d Which Tool Should Be Used
1.2d Which Tool Should Be Used 00:00:00
2.1a Introduction
2.1a Introduction 00:00:00
2.1b OBJECTIVE Data Exploration
2.1b OBJECTIVE Data Exploration 00:00:00
2.2a What Is SAS Visual Statistics
2.2a What Is SAS Visual Statistics 00:00:00
2.2b Accessing SAS Visual Statistics Functionality
2.2b Accessing SAS Visual Statistics Functionality 00:00:00
2.2c Course Data Introduction
2.2c Course Data Introduction 00:00:00
2.2d Self Study Course Data Introduction
2.2d Self Study Course Data Introduction 00:00:00
2.2e Demo Introduction to the SAS Visual Statistics Environment
2.2e Demo Introduction to the SAS Visual Statistics Environment 00:00:00
2.2f Demo Fitting an Automatic Model
2.2f Demo Fitting an Automatic Model 00:00:00
2.2g Exercise Using SAS Visual Analytics Data Explorer QUESTIONS
2.2g Exercise Using SAS Visual Analytics Data Explorer QUESTIONS 00:00:00
2.2g Exercise Using SAS Visual Analytics Data Explorer ANSWERS
2.2g Exercise Using SAS Visual Analytics Data Explorer ANSWERS 00:00:00
2.3a Clustering
2.3a Clustering 00:00:00
2.3b Demo Building a Cluster Analysis
2.3b Demo Building a Cluster Analysis 00:00:00
2.3c Exercise Performing a Cluster Analysis Exercise
2.3c Exercise Performing a Cluster Analysis Exercise 00:00:00
2.3c Exercise Performing a Cluster Analysis SOLUTION
2.3c Exercise Performing a Cluster Analysis SOLUTION 00:00:00
2.4a Self-Study
2.4a Self-Study 00:00:00
2.5a Dramatic Performance Improvement Airlines Industry Example
2.5a Dramatic Performance Improvement Airlines Industry Example 00:00:00
2.5b Distributed Computing with SAS High-Performance Analytics
2.5b Distributed Computing with SAS High-Performance Analytics 00:00:00
2.6a Principal Components Analysis
2.6a Principal Components Analysis 00:00:00
2.6b Input and Output Variables
2.6b Input and Output Variables 00:00:00
2.6c Selection of the Number of Principal Components
2.6c Selection of the Number of Principal Components 00:00:00
2.6d Example 1
2.6d Example 1 Data Exploration 00:00:00
2.6e Example 2 data exploration
2.6e Example 2 data exploration 00:00:00
2.6f Principal Components Analysis Pros and Cons
2.6f Principal Components Analysis Pros and Cons 00:00:00
2.6g Demo Principal Components Analysis, Part 1
2.6g Demo Principal Components Analysis, Part 1 00:00:00
2.6h Demo Principal Components Analysis, Part 2
2.6h Demo Principal Components Analysis, Part 2 00:00:00
3.1a Introduction Analysis Methods for Categorical Targets
3.1a Introduction Analysis Methods for Categorical Targets 00:00:00
3.1b objectives Analysis Methods for Categorical Targets
3.1b objectives Analysis Methods for Categorical Targets 00:00:00
3.2a Logistic Regression in SAS Visual Analytics
3.2a Logistic Regression in SAS Visual Analytics 00:00:00
3.2b Logistic Regression Properties and Filters
3.2b Logistic Regression Properties and Filters 00:00:00
3.2c Demo Creating a Logistic Regression in SAS Visual Analytics
3.2c Demo Creating a Logistic Regression in SAS Visual Analytics 00:00:00
3.2d Exercise Building a Logistic Regression in SAS Visual Analytics Exercise
3.2d Exercise Building a Logistic Regression in SAS Visual Analytics Exercise 00:00:00
3.2d Exercise Building a Logistic Regression in SAS Visual Analytics Solution
3.2d Exercise Building a Logistic Regression in SAS Visual Analytics Solution 00:00:00
3.3a Binary Logistic Models Group By Examples
3.3a Binary Logistic Models Group By Examples 00:00:00
3.3b Interactive Group By Analysis
3.3b Interactive Group By Analysis 00:00:00
3.3c Demo Adding a Group By Variable to a Logistic Regression
3.3c Demo Adding a Group By Variable to a Logistic Regression 00:00:00
3.4a Decision Trees in SAS Visual Analytics
3.4a Decision Trees in SAS Visual Analytics 00:00:00
3.4b Demo Creating a Decision Tree Analysis in SAS Visual Analytics
3.4b Demo Creating a Decision Tree Analysis in SAS Visual Analytics 00:00:00
3.4c Demo Cultivating a Decision Tree Autonomously
3.4c Demo Cultivating a Decision Tree Autonomously 00:00:00
3.4d Exercise Building a Logistic Regression in SAS Visual Analyticss Exercise
3.4d Exercise Building a Logistic Regression in SAS Visual Analyticss Exercise 00:00:00
3.4d Exercise Building a Logistic Regression in SAS Visual Analytics Solution
3.4d Exercise Building a Logistic Regression in SAS Visual Analytics Solution 00:00:00
3.5a self-study
3.5a self-study 00:00:00
3.6a PROC IMSTAT Statements
3.6a PROC IMSTAT Statements 00:00:00
3.6b Decision Trees In PROC IMSTAT
3.6b Decision Trees In PROC IMSTAT 00:00:00
3.6c Demo Growing a Decision Tree, Part 1
3.6c Demo Growing a Decision Tree, Part 1 00:00:00
3.6d Demo Growing a Decision Tree, Part 2
3.6d Demo Growing a Decision Tree, Part 2 00:00:00
3.6e Demo Growing a Decision Tree, Part 3
3.6e Demo Growing a Decision Tree, Part 3 00:00:00
3.6f Demo Growing a Decision Tree, Part 4
3.6f Demo Growing a Decision Tree, Part 4 00:00:00
3.6g Demo Deriving a Decision Tree
3.6g Demo Deriving a Decision Tree 00:00:00
3.6h ASSESS Statement and Selected Options
3.6h ASSESS Statement and Selected Options 00:00:00
3.6i Demo Assessing a Decision Tree
3.6i Demo Assessing a Decision Tree 00:00:00
3.7a self-studty
3.7a self-studty 00:00:00
3.7b Random Forests in IMSTAT
3.7b Random Forests in IMSTAT 00:00:00
3.7c Demo Growing a Forest of Trees
3.7c Demo Growing a Forest of Trees 00:00:00
3.7d Scoring with a Random Forest
3.7d Scoring with a Random Forest 00:00:00
3.7f Demo Scoring Data with the RANDOMWOODS Score Code
3.7f Demo Scoring Data with the RANDOMWOODS Score Code 00:00:00
3.8a Logistic Regression in PROC IMSTAT
3.8a Logistic Regression in PROC IMSTAT 00:00:00
3.8b Demo Developing and Assessing a Logistic Regression Model, Backward Elimination
3.8b Demo Developing and Assessing a Logistic Regression Model, Backward Elimination 00:00:00
3.8c Demo Developing and Assessing a Logistic Regression Model, RANDOMWOODS Input Selection
3.8c Demo Developing and Assessing a Logistic Regression Model, RANDOMWOODS Input Selection 00:00:00
4.1a introduction Analysis Methods for Interval Targets
4.1a introduction Analysis Methods for Interval Targets 00:00:00
4.1b objectives Analysis Methods for Interval Targets
4.1b objectives Analysis Methods for Interval Targets 00:00:00
4.2a Regression Models Linear Regression
4.2a Regression Models Linear Regression 00:00:00
4.2b Linear Regression Roles and Properties
4.2b Linear Regression Roles and Properties 00:00:00
4.2c Demo Creating and Exploring the Linear Regression Model
4.2c Demo Creating and Exploring the Linear Regression Model 00:00:00
4.2d Demo Refining the Linear Regression Model
4.2d Demo Refining the Linear Regression Model 00:00:00
4.2e Demo Generalizing the Linear Model
4.2e Demo Generalizing the Linear Model 00:00:00
4.2f Exercise Building a Linear Model to Predict the Amount of a Donation exercise
4.2f Exercise Building a Linear Model to Predict the Amount of a Donation exercise 00:00:00
4.2f Exercise Building a Linear Model to Predict the Amount of a Donation solution
4.2f Exercise Building a Linear Model to Predict the Amount of a Donation solution 00:00:00
4.3a Generalized Linear Models
4.3a Generalized Linear Models 00:00:00
4.3b Generalized Linear Model Roles and Properties
4.3b Generalized Linear Model Roles and Properties 00:00:00
4.3c Demo Building a GLM Model
4.3c Demo Building a GLM Model 00:00:00
4.3d Exercise; Building a GLM Model to Predict the Amount of Donations EXERCISE
4.3d Exercise; Building a GLM Model to Predict the Amount of Donations EXERCISE 00:00:00
4.3d Exercise; Building a GLM Model to Predict the Amount of Donations SOLUTION
4.3d Exercise; Building a GLM Model to Predict the Amount of Donations SOLUTION 00:00:00
4.4A Generalized Linear Models
4.4A Generalized Linear Models 00:00:00
4.4b Demo Generalized Linear Models for Count Data
4.4b Demo Generalized Linear Models for Count Data 00:00:00
4.4c Demo Generalized Linear Models for Interval Targets
4.4c Demo Generalized Linear Models for Interval Targets 00:00:00
4.4d GLM Statement and the Lognormal Model
4.4d GLM Statement and the Lognormal Model 00:00:00
4.4e Demo Fitting a Lognormal Model to an Interval Target
4.4e Demo Fitting a Lognormal Model to an Interval Target 00:00:00
4.5a Zero-Inflated Poisson (ZIP) Distribution
4.5a Zero-Inflated Poisson (ZIP) Distribution 00:00:00
4.5b Demo Fitting a Zero-Inflated Poisson Model Using the HP GLM Node in SAS Enterprise Miner
4.5b Demo Fitting a Zero-Inflated Poisson Model Using the HP GLM Node in SAS Enterprise Miner 00:00:00

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