The Product Management for AI & Data Science Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction to Data Science Product Management

Estimated time: 0.5 hours

• Role differentiation: Data Science PM vs. Traditional PM
• Overview of the AI product lifecycle
• Key stakeholders in AI projects
• Core responsibilities of a Data Science Product Manager

Module 2: Problem Framing & Opportunity Sizing

Estimated time: 0.75 hours

• Applying the PIE framework to prioritize use cases
• Assessing technical feasibility of AI solutions
• Estimating business impact and ROI potential
• Aligning AI initiatives with strategic goals

Module 3: Metrics & Experimentation Design

Estimated time: 1 hour

• Defining success metrics: precision, recall, and accuracy
• Establishing guardrails for model performance
• Designing A/B tests and canary releases
• Evaluating statistical significance in experiments

Module 4: Data & Feature Strategy

Estimated time: 0.75 hours

• Conducting data discovery and inventory
• Assessing data quality and availability
• Roadmapping feature engineering efforts
• Managing trade-offs between volume, velocity, and variety of data

Module 5: Working with Data Science Teams

Estimated time: 1 hour

• Translating product requirements into model scope
• Differentiating between MVP and MLP in ML projects
• Running agile sprints with data science teams
• Conducting notebook reviews and model iteration retrospectives

Module 6: Building the AI Roadmap & Business Case

Estimated time: 0.75 hours

• Creating cost–benefit analyses for AI initiatives
• Securing stakeholder buy-in and alignment
• Planning sprints and milestones for AI projects
• Allocating resources and managing dependencies

Module 7: MLOps & Deployment Strategies

Estimated time: 1.25 hours

• Introduction to MLOps principles and practices
• Implementing CI/CD pipelines for machine learning models
• Monitoring for data and concept drift
• Choosing between batch and real-time model serving
• Scaling considerations for production workloads

Module 8: Responsible AI & Governance

Estimated time: 0.5 hours

• Applying ethical AI frameworks
• Conducting bias and fairness audits
• Ensuring compliance with GDPR and CCPA
• Building transparency with model cards and data lineage

Module 9: Go-to-Market & Adoption

Estimated time: 0.75 hours

• Planning AI feature launches and rollouts
• Designing user training and enablement programs
• Collecting user feedback for iteration
• Embedding AI insights into dashboards and workflows

Module 10: Capstone Project & Best Practices

Estimated time: 1 hour

• End-to-end case study: from problem discovery to deployment
• Applying PIE framework and success metrics
• Using templates and playbooks for real-world execution
• Reviewing lessons learned and best practices

Prerequisites

• Familiarity with basic product management concepts
• Foundational understanding of data science or machine learning
• Experience working with technical teams preferred but not required

What You'll Be Able to Do After

• Define and prioritize high-impact AI use cases using the PIE framework
• Design effective experimentation strategies with appropriate success metrics
• Collaborate efficiently with data science and engineering teams using agile methods
• Build compelling business cases and secure stakeholder buy-in for AI projects
• Lead AI initiatives from concept through deployment with MLOps and governance best practices