Data Literacy for Product Owners
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This course contains the use of artificial intelligence.
Duration: 21 Weeks · 105 Teaching Days
Audience: Non-technical Product Owners, AI PMs, Business Leaders
Data Literacy for Product Owners is a comprehensive, business-focused program designed to help product leaders understand how data, data quality, and AI readiness shape successful digital and AI-powered products.
This course is built for Product Owners, Product Managers, AI Product Managers, and business leaders who do not need to become data scientists, but do need to make confident decisions about data-driven products. You will learn how to evaluate whether data is useful, trustworthy, complete, biased, fresh, and ready to support product decisions or AI systems.
Across 21 weeks, learners explore how data is created, collected, structured, monitored, and used in real-world product environments. The course explains the difference between structured data, unstructured data, behavioral data, self-reported data, event data, logs, and third-party data sources. You will learn why data does not magically exist, how instrumentation shapes what teams can measure, and why poor data collection often leads to poor product outcomes.
A major focus of the course is data quality. Learners will examine key dimensions such as accuracy, completeness, consistency, freshness, data drift, and data decay. You will learn how small data quality issues can quietly create major business problems, especially when dashboards, metrics, and AI systems are trusted without proper validation.
The course also covers bias, representation, and data limits in a practical, non-technical way. You will understand concepts such as sampling bias, historical bias, proxy variables, missing users, majority vs minority data effects, and why data cannot always support strong fairness claims. These lessons help product leaders avoid overconfidence and make more responsible decisions.
For AI-focused products, this course explains why AI systems are probabilistic, why training data differs from live data, why labels and ground truth are difficult, and how issues like data leakage, concept drift, feedback loops, and silent degradation can break AI products after launch.
By the end of the course, learners will be able to assess data readiness, ask better questions of data teams, communicate data risk to stakeholders, evaluate feasibility, and make stronger go / no-go decisions for AI initiatives. The final capstone helps learners conduct a complete data readiness and risk review for an AI product.
This course is ideal for anyone who wants to lead AI and data-driven products with better judgment, clearer communication, and stronger cross-functional collaboration.
No prior data science or AI experience is required
Designed for product owners, business leaders, project managers, and non-technical professionals
Basic familiarity with digital products, apps, or business workflows is helpful
No coding, mathematics, or machine learning background is needed
A willingness to think critically about data, AI, and decision-making is important
Access to a computer and internet connection is recommended for exercises and discussions
Helpful for anyone working with dashboards, analytics, AI tools, or product metrics
Curiosity about how AI products succeed or fail in the real world will help you get the most value from the course
Ideal for learners who want practical business understanding rather than deep technical implementation
All concepts are explained conceptually and in plain language, making the course beginner-friendly
Understand how data is collected, structured, stored, and used in modern AI and digital products
Identify poor-quality, biased, incomplete, or misleading data before it impacts product decisions
Evaluate whether an AI or analytics initiative is truly feasible based on data readiness and constraints
Communicate effectively with data, AI, engineering, legal, and security teams using the right terminology and concepts
Recognize data drift, decay, feedback loops, and hidden operational risks in production systems
Make smarter product decisions under uncertainty using imperfect or incomplete data
Understand the difference between correlation and causation without requiring advanced statistics knowledge
Assess fairness, representation, and bias risks in datasets and AI systems
Build stronger product strategies by translating business goals into practical data requirements
Lead AI and data-driven initiatives with realistic expectations, sound judgment, and cross-functional alignment
Product Owners and Product Managers who want to make smarter data-driven and AI product decisions
Business leaders and executives responsible for evaluating AI initiatives, dashboards, and analytics strategies
AI Product Managers and AI Program Managers who need stronger judgment around data quality, readiness, and risk
Non-technical professionals who work with data teams but want concepts explained in plain business language
Startup founders and innovation leaders exploring AI-powered products and automation opportunities
Project managers, operations leaders, and consultants involved in digital transformation initiatives
Professionals frustrated by misleading dashboards, unclear metrics, or unrealistic AI expectations
Anyone responsible for making decisions based on analytics, reporting, or AI-generated insights
Teams that want to improve cross-functional collaboration between product, data, engineering, legal, and security groups
Learners who want practical understanding of data quality, bias, observability, and AI risk without learning to code
