Advanced Analytics for the Modern Business Analyst

To be effective in a competitive business environment, a business analyst needs to be able to use predictive analytics to translate information into decisions and to convert information about past performance into reliable forecasts. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.

In this course, you gain the skills required to succeed in today's highly analytical and data-driven economy. This course introduces the basics of data management, decision trees, logistic regression, segmentation, design of experiments, and forecasting.

Presented by Catherine Truxillo

Dr. Catherine Truxillo has been a Statistical Training Specialist at SAS since 2000 and has written or co-written SAS training courses for advanced statistical methods including: multivariate statistics, linear and generalised linear mixed models, multilevel models, structural equation models, multiple imputation methods for missing data, statistical process control, design and analysis of experiments, and cluster analysis. Although she primarily works with advanced statistics topics, she also teaches SAS courses using SAS/IML (the interactive matrix language), Enterprise Guide, and JMP software.

Catherine's previous experiences with teaching, statistical consulting, and software design led her to a job teaching statistics for SAS.

Before moving to SAS, Catherine completed her Ph.D. in Social Psychology with an emphasis in Statistics at The University of Texas at Austin. While at UT Austin, she completed an internship with the Math and Computer Science department's statistical consulting help desk and taught a number of undergraduate courses. While teaching and performing her own graduate research, she worked for a software usability design company conducting experiments to assess the ease-of-use of various software interfaces and website designs.

She moved to Cary, North Carolina to join SAS in 2000, where she lives in the forest with her husband and two children. Her personal interests include bicycling, hiking, dancing, and singing.

Learn how to:

  • express a business problem as a manageable analytical question
  • identify the appropriate data to address the question
  • select statistical analyses that help answer the question
  • select a champion from a set of competing models (analyses)
  • apply the results of the champion analysis to new data for scoring, forecasting, or both
  • translate complex statistical results into actionable business decisions.

Who should attend

Business analysts with some experience in statistical analysis and reporting who want to make better use of their data.


Before attending this course, you should have taken a college-level course in statistics, covering distribution analysis, hypothesis testing, and regression techniques or have equivalent knowledge.

This course addresses SAS Enterprise Guide, SAS Enterprise Miner, SAS Forecast Server software.

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