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Want this course at another location or on a different date? Let us know your course needs.On-site Pricing
Course fee and EPTO/APTO units differ for on-site training.Exploratory Analysis for Large and Complex Problems
Business Knowledge Series course
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Duration: 3.0 days Course fee: $2,175 EPTO units: 4.2 CEUs: 1.8 | ||
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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 improvement
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Prerequisites
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.
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
This course addresses 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.
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