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BG 401 – Data Analytics – Predictive Analytics

Duration: 3 – 4 days

This course examines the key methods of predictive analytics and advanced business intelligence concepts, and provides business decision-making context for these methods. Using real business cases, it illustrates the application and interpretation of these techniques for any organization. Through case studies and team interaction, attendees will attain the real-world implementation skills necessary to build a successful strategy for big data governance and stewardship. In addition, it provides valuable insight into the components of a successful enterprise information management program that will inform any organization’s approach to big data management.

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Description

Once an organization has adopted an analytics approach and has used descriptive analytics to improve visualization of results, many data scientists will want to explore the capabilities of predictive analytics, the advanced business intelligence methods. Many organizations also need to understand how analytics is connected to the core domains of enterprise data management (data governance, metadata management, data architecture).

This course examines the key methods of predictive analytics and advanced business intelligence concepts, and provides business decision-making context for these methods. Using real business cases, it illustrates the application and interpretation of these techniques for any organization. Through case studies and team interaction, attendees will attain the real-world implementation skills necessary to build a successful strategy for big data governance and stewardship. In addition, it provides valuable insight into the components of a successful enterprise information management program that will inform any organization’s approach to big data management.

Objectives

  • Demonstrate concepts of predictive analytics and their capabilities
  • Evaluate concepts of advanced problem solving through the application of data mining
  • Synthesize the common problems, issues, and trends found in the use of predictive analysis

Seminar Content

  • Introduction to Predictive Analytics – with an overview of Data Science
    • Data Science
    • Predictive Analytics Analytics
  • Enterprise Data Management and Predictive Analytics
    • Data Governance
    • Metadata Management
    • Data Architecture
  • Predictive Effect and Big Data
    • Big Data – definition, importance to analytics, challenges
    • Components of a predictive analytics approach
  • Predictive Analytics Methods
    • Predictive Data Safety
    • Types of predictive analytics models
    • Industry examples of predictive analytics methods
  • Challenges in Predictive Analytics
    • Implementation issues
    • Man vs. Machine
    • Ethics in Predictive Analytics
  • Workshop conclusion
    • Summary, additional exercises, sources for further reading, etc.

About the Course Designer

This training was designed by David Marco, PhD, an internationally recognized authority on data and AI governance, to help teams succeed in real organizational conditions. The curriculum equips participants with practical judgment, shared language, and decision clarity that hold under scale, risk, and executive accountability.

David Marco PHD EWSolutions

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