In-depth Actionable Training from the Comfort of Your Own Desk

Prerequisite: None

Deanne Larson, Ph.D.

DM, CBIP, President

Larson & Associates

Deanne Larson, Ph.D., is an active data science practitioner and academic. Her research has focused on enterprise data strategy, agile analytics, and data science best practices. She holds Project Management Professional (PMP), Project Management Agile Certified Practitioner (PMI-ACP), Certified Business Intelligence Professional (CBIP), and Six Sigma certifications. Deanne attended AT&T Executive Training at the Harvard Business School focusing on IT leadership, Stanford University focusing on data science, and New York University focusing on business analytics. She has presented at multiple conferences including TDWI, TDWI Europe, PMI, and other academic conferences. She is a faculty member at Purdue Global, has consulted for several Fortune 500 companies, and has authored multiple research articles on data science methodology and best practices.

DataOps is a process-focused and automated methodology for delivering data for machine learning and AI that concentrates on reducing cycle time and improving the quality of advanced analytics deliverables. DataOps builds on the concepts of DevOps, continuous integration and delivery (CI/CD), and agile. These concepts support quick and efficient software delivery, but analytics is more than software—it is also about the delivery of insights. DataOps needs to deliver consistent and meaningful data which means automating the data lifecycle of acquisition, understanding, integration, transformation, and deployment. This course will address best practices that contribute to consistent and automated analytics results.

You Will Learn

• The value of DataOps
• Agile and DevOps principles applied to DataOps
• The scope and components of DataOps
• Where CI/CD is applied in DataOps
• Best practices and how to get started

Geared To

• Project managers
• Data scientists
• Data engineers
• Analysts
• Analytics managers