TDWI Predictive Analytics 2016

Key Topics

• TDWI Predictive Analytics Fundamentals
  • Common applications of predictive analytics
  • Definitions, concepts, and terminology of predictive analytics
  • To distinguish among various predictive model types and understand the purpose and statistical foundations of each
  • How and where predictive analytics fits into a BI program and the relationships with business metrics, performance management, and data mining
  • Organizational considerations for predictive analytics, including roles, responsibilities, and the need for business, technical, and management skills
• TDWI Analytics Fundamentals
  • An analytics topology to make sense of the variety of analytic types and techniques
  • The concepts and practices of analytic modeling
  • Analytic techniques for exploration, experimentation, and discovery
  • The data side of analytics including data sourcing, data discovery, data cleansing, and data preparation
  • The organizational side of analytics: self-service, central services, governance, etc
  • The human side of analytics: communication, conversation, and collaboration
  • A bit about emerging techniques and technologies shaping the future of analytics
• TDWI Data Visualization Fundamentals
  • Preparing data for visualization
  • Visualization as a communication medium
  • Choosing and using charts and graphs
  • Components of visualization
  • Visual design techniques
  • Visual exploration and analysis
  • Visual storytelling
  • Extending visualization with infographics
  • Data visualization tools

Who should Attend

• Data scientists and analytics modelers
• Business analysts and data analysts
• BI leaders and managers
• Business analytics leaders and managers
• Anyone who develops charts and graphs to communicate about data