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  • Describe the information age and its role in achieving business success
  • Explain how to represent data in a variety of common formats
  • Explain big data and problems it presents


  • Explain the various techniques use for data preprocessing and cleansing
  • Explain data, what it represents and how it is organized
  • Discuss how to visualize data for exploratory and confirmatory analysis


  • Describe the difference between a transaction based system and a decision support system
  • Database and data warehousing
  • Explain how business intelligence helps organizations
  • Explain the components of online analytical processing (OLAP) systems


  • Discuss data modeling and predictive modeling
  • Describe the sequence of events associated with a typical data mining project
  • Examples of data mining, such as decision tree, linear models and probability estimation.
  • Social network analytics and text mining
  • Identify the issues of privacy that must be addressed in performing data mining

You will need this file for the Excel exercise.

Defense organizations have long sought to take full advantage of one of their most valuable resources— the vast amount of data they collect day in and day out. They want to be able to use that data to make more insightful, forward-looking decisions about readiness, logistics, manpower, intelligence, and a host of other critical defense concerns. A new generation of advanced analytics—high-level diagnostic and predictive—can provide them that opportunity.

Through its goals and objectives, the EDAS establishes a trajectory for the Army in building enterprise-wide decision analytics capabilities that capture the full business value of Army information resources. The Army will develop best-in-class analytics doctrine, organization, training, materiel, leadership and education, personnel, facilities, and policy (DOTMLPF-P) capabilities for a data-driven culture to drive fact-based, resource-informed decisions that generate readiness at best value.

All roads today lead to the data-driven enterprise—one that is agile, innovative, and customer-centric enough to survive and thrive in an increasingly complex and competitive environment. Undoubtedly, however, some organizations are getting there faster than others. The key for those racing ahead is data that is accessible, analyzable, and actionable—a critical asset that for many organizations remains locked down in organizational silos and legacy systems, held back by people and processes, misunderstood by corporate leaders, or slowed by inefficient infrastructure.