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Data Science for Managers – Applications, Visualizations & Storytelling – Cognitir

Tuesday, December 14 - Wednesday, December 15

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According to Stanford Graduate School of Business Professor Susan Athey, managers need to be able to understand and evaluate output from analysts, and they often find that if they cannot directly engage in the output themselves, they are left out or behind.

This course will involve managers and executives in discussions around data and how analytics can help organizations with problem-solving and strategy. An understanding of what foundational data science methods can accomplish coupled with data visualization and storytelling will empower managers and executives to fully employ existing data assets, invest in new data assets, and effectively communicate business learnings to key stakeholders.

It is a workshop that opens the eyes of executives and managers into the power of data science and how it can help their organizations create and sustain competitive advantages.

Attendees may use a PC or Mac to access slides and other resources we provide for the class.

This training is often taught together with Data Science for Managers – Building a Data-Driven Organization in a two-day workshop since both courses complement each other. Attendees usually take this class on the first day.

Early Bird Rate
$415 Members | $515 Nonmembers
Early Bird Rate expires December 7th

Course Curriculum
Course Developers
About Cognitir

Additional Details

What This Course Offers

  • Data Analytics and Big Data
  • Implementation Best Practices
  • Collaborative Exercises
  • Use Cases
  • Effective Communication Strategies
  • Support Beyond the Classroom

Who Is This For

This class is relevant for executives and managers who supervise teams that execute data analytics and business intelligence projects. Other junior and senior business professionals who want to better understand how data science can supercharge corporate analytics will also benefit from taking this training.

No prior experience with data science, big data, or machine learning required.