What will you in The Product Management for AI & Data Science Course
Master the end-to-end product management lifecycle specifically for Data Science and AI initiatives.
Translate complex business problems into high-impact, data-driven use cases using frameworks like PIE.
Define and track success metrics (precision, recall, ROI) and design robust experimentation (A/B tests, canary releases).
Collaborate effectively with data scientists and engineers through agile workflows, model scoping (MVP vs. MLP), and retrospectives.
Develop comprehensive AI roadmaps, build strong business cases with cost–benefit analyses, and secure stakeholder buy-in.
Implement MLOps best practices: CI/CD for models, monitoring for data drift, scalable serving (batch vs. real-time).
Program Overview
Module 1: Introduction to Data Science Product Management
⏳ 30 minutes
- Role differentiation: Data Science PM vs. Traditional PM.
- Overview of the AI product lifecycle and key stakeholders.
Module 2: Problem Framing & Opportunity Sizing
⏳ 45 minutes
- Applying the PIE framework to prioritize use cases.
- Estimating business impact vs. technical feasibility.
Module 3: Metrics & Experimentation Design
⏳ 60 minutes
- Defining precision, recall, ROI, and guardrails.
- Designing A/B tests, canary releases, and evaluating statistical significance.
Module 4: Data & Feature Strategy
⏳ 45 minutes
- Conducting data discovery and quality assessments.
- Roadmapping feature engineering: balancing volume, velocity, and variety.
Module 5: Working with Data Science Teams
⏳ 60 minutes
- Translating product requirements into ML model scope (MVP vs. MLP).
- Running agile sprints, notebook reviews, and model iteration retrospectives.
Module 6: Building the AI Roadmap & Business Case
⏳ 45 minutes
- Crafting cost–benefit analyses and securing stakeholder buy-in.
- Planning sprints, milestones, and resource allocation.
Module 7: MLOps & Deployment Strategies
⏳ 75 minutes
- Introduction to MLOps: CI/CD pipelines for models, drift monitoring.
- Choosing between batch and real-time serving; scaling considerations.
Module 8: Responsible AI & Governance
⏳ 30 minutes
- Applying ethical AI frameworks and conducting bias audits.
- Building transparency: model cards, data lineage, and compliance (GDPR/CCPA).
Module 9: Go-to-Market & Adoption
⏳ 45 minutes
- Planning launches, user training, and feedback collection.
- Embedding AI insights into dashboards and workflows for adoption.
Module 10: Capstone Project & Best Practices
⏳ 60 minutes
- End-to-end case study: problem discovery → production monitoring.
- Templates, playbooks, and lessons learned for repeatable success.
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Job Outlook
High-Demand Roles: Data Science Product Manager, AI Product Lead, ML Program Manager.
Salary Potential: ₹12–30 LPA in India; $110K–$160K annually in the U.S.
Growth Areas: Enterprise AI strategy, MLOps leadership, and AI ethics/governance.
Career Impact: Positioned at the nexus of business and technology, DS/AI PMs drive high-value transformation initiatives and command premium compensation.
Specification: The Product Management for AI & Data Science Course
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