Courses Details
Course Overview
Managers require a sophisticated understanding of what one can (and cannot) infer from data, and how to use those inferences to make good decisions.  This course provides the analytical techniques for using data to make appropriate inferences and good decisions.  It is intended to put ideas and concepts of data analysis and interpretation into effective use in an enterprise.  Concepts of corporate measures such as critical success factors, key performance indicators and sensitivity analysis will also be covered. 
Course Schedule
Target Audience
Professionals in supervisory and managerial positions.
Course Prerequisites
A basic understanding of statistical techniques
Expected Accomplishments
  • Understand data collection formatting and analysis
  • Know how to measure business performance
  • Classify alternatives for data presentation
  • Understand competitive data structure for acquisition
  • Understand the science of econometrics
  • Data mining exploitation
  • Developing neural patterns for forecasting
  • Be aware of data access quality measures
Course Outline
Introduction – Data analysis within an enterprise
Objectives and structure
About data analysis
Business measures for performance
Data analysis solutions
Basic data analysis concepts

Acquisition and presentation of data
Sources of data
Which sources to use
Simple presentation of numerical data
Alternatives in presentation of statistical data
Internal vs. external data: using the web

Strategic data analysis for business and competitive structure
Merger and acquisition analysis
Competitive scenarios and selecting scenarios of importance
Competitive analysis for acquisition
Comparative operational analytics
Econometric considerations

Enterprise performance management – measuring business activities
Measuring business processes
Critical success factors
Decision analysis and scenarios
Measures for quality
Key performance indicators
Process performance, pulse points
Cycle, transport, wait times and others

Data mining – looking for the few gems in a pile
Finding patterns in data
Interpreting the results
Using neutral nets for patterns and forecasting
Presenting alternatives
Issues in data access quality