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.