AI & Data as a Product
AI & Data Product Management
Overview
Learn how to treat analytics as a product and maximize their impact and alignment at each step in their lifecycle. This course introduces the key concepts and best practices for AI & Data Product Management.
Learning Outcomes
After this course, students will be able to:
Learn how to treat analytics as a product and maximize their impact and alignment at each step in their lifecycle. This course introduces the key concepts and best practices for AI & Data Product Management.
Learning Outcomes
After this course, students will be able to:
- Describe and participate in the definition of the analytics solution lifecycle including requirements gathering, design, development, and release management.
Length
2 Days (8 hours/day)
Pre-Requisites
This course requires a basic understanding of product management and analytics concepts.
Course Content
This course contains the following modules:
This course contains the following modules:
Module 1: Intro to product management
- This course begins with an overview of the concepts and terminology of AI/ Data Product Management and user-centric design.
- Students will learn the stages of the analytic solution lifecycle.
Module 2: Analytics alignment & impact
- This module provides a deep dive into how to manage the strategic alignment and financial return on investment (impact) of analytic solutions.
- This includes how to define, measure, and improve the impact of analytic solutions.
Module 3: Requirements gathering & user stories
- Next, this course explores the best practices and methods for collecting a clear articulation of the business problem and design of the solution.
- Students will learn how to interview stakeholders, synthesize requirements, and communicate the business problem as technical requirements for the analytic solutions.
- They will also learn how to write user stories for analytic solutions to break functionality into modular pieces for more granular prioritization.
Module 4: Prototyping & roadmapping
- Lastly, students will learn how to prioritize a set of user stories and requirements for testing with stakeholders through prototyping.
- This includes reporting and interface prototyping as well as user testing methods.
Exercises
- User and system research examples
- Write out quality user stories
Analytics Program & Project Management
Overview
Managing an analytics project involves a new class of project management and operational challenges. This course helps project/ product managers navigate the operational hurdles of building and shipping analytic solutions.
Learning Outcomes
After this course, students will be able to:
Managing an analytics project involves a new class of project management and operational challenges. This course helps project/ product managers navigate the operational hurdles of building and shipping analytic solutions.
Learning Outcomes
After this course, students will be able to:
- Start efficiently managing analytics, data science, and AI projects.
- Identify key stakeholders and risks associated with analytics, data science, and AI projects.
- Mitigate risks and effectively communicate/manage expectations and project status.
- Understand how multiple projects roll up to a program.
- Make continuous improvements to project management methods over time.
Length
2 Days (8 hours/day)
Pre-Requisites
This course requires some knowledge of basic project management concepts.
Course Content
This course contains the following modules:
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.
Exercises
- 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
AI & Data Product Design
Overview
Aligning analytic solutions to business problems requires a unique design skillset and approach. This hands-on course teaches how to understand the business process, articulate requirements, and prototype solutions to increase impact and adoption.
Learning Outcomes
After this course, students will be able to:
Aligning analytic solutions to business problems requires a unique design skillset and approach. This hands-on course teaches how to understand the business process, articulate requirements, and prototype solutions to increase impact and adoption.
Learning Outcomes
After this course, students will be able to:
- Rapidly prototype AI solutions and receive feedback
- Understand integrated systems and processes in place to consider when generating a design
- Conduct user and system research to ensure the solutions built are high-quality and ethical
Length
2 Days (8 hours/day)
Pre-Requisites
This course requires a basic experience and understanding of design principals and analytics.
Course Content
This course contains the following modules:
This course contains the following modules:
Module 1: Process and system design
- This module will cover the intersection of systems, people, and technology and how to apply core design principals to designing AI and data solutions.
Module 2: Ethical considerations when designing data and AI solutions
- Designing with everyone in mind.
- Applying ethical frameworks to the design process
Module 3: Designing for architecture
- Basic overview of the technical fundamentals that are necessary to build and support AI and data solutions.
- The impact that technical constraints have on design decisions.
Module 4: Feedback loops and improvement cycles
- This module will cover techniques on incorporating feedback cycle capabilities.
- Capturing critical data to properly assess impact and adoption of solutions.
Exercises
- Find the root cause of system failure with examples.
- Design a system and go through the ethical framework to ensure it addresses critical risks.
- Design for optimal user experience and capabilities for feedback mechanism through multiple scenarios.