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Free Download Product Management for AI & Data Science
Published 2/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 8m | Size: 3.09 GB
Master product strategy, data, and AI systems without writing code
What you'll learn
Differentiate clearly between Product Manager, Product Owner, and Project Manager roles-and explain how the AI Product Manager role uniquely operates
Identify and frame business problems that are suitable for AI and machine learning solutions, translating real-world needs into well-defined AI product
Design AI-ready product requirements and PRDs, including data dependencies, model constraints, evaluation metrics, and non-functional requirements
Collaborate effectively with data scientists, ML engineers, and platform teams by understanding the AI development lifecycle, model trade-offs
Evaluate and prioritize AI product decisions using data-driven trade-offs, balancing accuracy, business impact, user trust, operational cost, and scalability.
Plan and execute responsible AI product launches, incorporating MVP strategies, human-in-the-loop designs, monitoring plans, and ethical risk assessments.
Operate and iterate AI products in production, including monitoring model performance, handling data and model drift, and continuously improving product
Requirements
A basic understanding of business or product concepts, such as how companies build and sell products (no prior product management experience required).
Familiarity with technology or data concepts at a high level (e.g., knowing what data, software, or AI is-without needing to code).
Curiosity about AI, data, and how technology creates business impact, especially in modern digital products.
Basic computer skills, including using documents, spreadsheets, and presentation tools.
Description
"This course contains the use of artificial intelligence"Artificial Intelligence and data science are no longer experimental or optional technologies. Today, AI-powered and data-driven products sit at the core of how organizations compete, make decisions, and scale. As a result, companies are actively seeking Product Managers who understand how to build, manage, and own AI products-not just track timelines or manage backlogs.This course is designed specifically to prepare you for the Product Manager for AI & Data Science role. It focuses on the product thinking, decision-making, and leadership skills required to take an AI product from idea to production and beyond. Unlike traditional product management courses, this course addresses the realities of working with machine learning systems, data pipelines, Generative AI models, and AI platforms, where outcomes are uncertain and success depends on much more than feature delivery.A core emphasis of the course is helping you clearly differentiate between Product Managers, Product Owners, and Project Managers, and understand where the AI Product Manager fits within modern organizations. You will learn why AI Product Managers are accountable for problem selection, value creation, and risk management, while working closely with data scientists, ML engineers, and platform teams.Throughout the course, you will learn how to identify business problems that are suitable for AI solutions, and how to translate those problems into well-defined AI use cases. You will understand how AI systems actually work at a conceptual level-covering data collection, model training, inference, feedback loops, and monitoring-without needing to write code or understand complex mathematics. This allows you to communicate confidently with technical teams while staying focused on product outcomes.The course places strong emphasis on data as a product, helping you understand why data quality, labeling, bias, and availability directly impact product success. You will learn how to assess data readiness, identify gaps and risks, and make informed decisions when data is incomplete or imperfect. These skills are critical for AI Product Managers, as data constraints often shape what is feasible long before a model is built.You will also learn how to define AI-specific product requirements, including functional and non-functional constraints such as accuracy, explainability, latency, cost, scalability, and ethical risk. The course walks through how to write AI-ready PRDs, evaluate trade-offs between model performance and business impact, and align stakeholders around realistic expectations.As the course progresses, you will gain hands-on exposure to launching and operating AI products in production. This includes designing AI MVPs, using human-in-the-loop approaches, setting up monitoring for model drift and data drift, and planning for continuous improvement. Special attention is given to Generative AI and LLM-based products, where issues like hallucinations, trust, guardrails, and cost control become central product concerns.Responsible and ethical AI is treated as a product responsibility, not just a technical one. You will learn how Product Managers assess bias, fairness, transparency, compliance, and reputational risk, and how these considerations influence product decisions, user experience, and governance processes.By the end of the course, you will bring everything together through a portfolio-ready, end-to-end AI product case study, demonstrating your ability to move from problem discovery to launch metrics and post-launch iteration. The course also prepares you for AI Product Manager interviews, helping you confidently answer case studies, trade-off questions, and stakeholder communication scenarios that hiring managers commonly use.This course is ideal for aspiring Product Managers, career switchers, MBA students, traditional PMs transitioning into AI, and technical professionals who want to move into product leadership roles. If you want to stop feeling overwhelmed by AI buzzwords and start thinking and acting like a modern AI Product Manager, this course gives you the structure, language, and confidence to do exactly that.
Who this course is for
Aspiring Product Managers who want to break into product roles focused on AI, machine learning, or data-driven products.
Traditional Product Managers looking to transition into AI or data science product teams and confidently work with ML engineers and data scientists.
Business analysts, consultants, and MBA students who want to move into AI-focused product leadership roles.
Engineers, data analysts, or data scientists who want to shift into product ownership and decision-making roles.
Startup founders and entrepreneurs building AI-enabled products and needing a strong product strategy foundation.
Professionals working with AI platforms (cloud, data, analytics, or GenAI tools) who want to understand how product decisions are made.
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