This course contains the following modules:
Module 1: The role of project and program management for analytics, data science & AI
- This module will cover the benefit of having a disciplined practice of organizing projects for analytics, data science, and AI.
Module 2: Traditional methods of project and program management
- This module covers the traditional methods of project management typically practiced in operational projects and software projects including waterfall, kanban, and agile.
Module 3: Core characteristics of analytics, data science, and AI projects
- This module covers the unique characteristics of analytics, data science, and AI projects, the typical roles that are critical for each type of project, and the optimal methods to apply to each phase to support project acceleration.
Module 4: Managing dependencies, relationships, and expectations
- This module will review typical blockers, dependencies, critical relationships to management, key risks to identify and manage, and mitigation strategies. This module will also cover setting appropriate expectations and communication methods to manage those expectations.
Module 5: Reporting on key project metrics
- The practice of project management must be iterated on and improved over time. This module covers what metrics are important to track and monitor as a project manager to ensure teams are working efficiently.
Module 6: Program management
- It is critical to report progress across multiple initiatives to provide visibility and communicate status strategically. This module will provide guidance on how to manage a program of analytics, data science, and AI projects.
- Mapping phases of a project for each type of project and identifying key characteristics - matching these phases to methods
- Map out sample key project milestones, risks, communications plan, and expectations
- Define and understand critical metrics to track to understand project efficiencies and progress