**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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