Exploratory Analysis for Large and Complex Problems
Duration: 3.0 days CEU: 1.8
Presented by Jeff Zeanah, President of Z Solutions, Inc.
This course presents perspective-changing combinations of graphics and predictive analytics in a framework that addresses the realities that an analytical staff faces in developing and presenting new exploratory findings. Complex exploratory predictive models are built with real-world data and investigated.
Learn how to
- use new methods to screen data sets with large numbers of variables
- use new graphical techniques to present findings
- find hidden relationships in multiple dimensions.
Who should attend
Data analysts who support tasks such as market research, sales analysis and forecasting, list mining, R&D, and process improvementPrerequisites
To maximize the return on investment from the class, you should have the following skills and experience:- background in analytical methods
- experience with predictive modeling
- familiarity with Base SAS, SAS/STAT, and SAS/GRAPH software, which you can acquire by taking the SAS Programming I: Essentials or Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course.
This class is taught in SAS Enterprise Miner and foundation SAS. Familiarity with SAS Enterprise Miner at the level presented in the Applied Analytics Using SAS Enterprise Miner 5 course is helpful. Most of the techniques shown in this course using SAS Enterprise Miner are supplemented with similar approaches in foundation SAS.
Course Contents
Predictive Analytics and Exploratory Data Mining- the relationship between predictive analytics and exploratory data mining
- the role of graphics in exploratory analysis
- complexity in a PowerPoint world
- the analyst's dilemma
- data streams versus structured data
- social network analysis as a solution to unstructured problems
- statistical mechanics of network analyses
- predicting with a network
- complex networks versus reductionism
- exploratory data mining success
- predictive modeling methods
- logistic regression
- decision trees
- neural networks
- the truth about neural networks
- comparing and contrasting predictive modeling methods
- model structure and impact on exploratory results
- graphical review of model results
- multi-dimensional graphics
- initial data screening
- elements of an exploratory script
- developing complex predictive models for exploratory efforts
- identifying important variables
- analyzing variables, domains, and clusters
- graphical review of models and data
- extracting new hypotheses (exploratory findings) from the predictive model
- building confidence with the exploratory findings
- recognizing and overcoming impediments to acceptance by the target audience
Software Addressed
This course addresses the following software product(s): SAS Enterprise Miner, SAS/STAT.Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.U.S. Schedule
| 18AUG2008 New York, NY | 12NOV2008 Cary, NC |
Check for additional and updated schedule information online at support.sas.com/courses/beap.html.