Name: Data Literacy for Product Owners
Availability: InStock

What You'll Learn

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

Requirements

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

Professional Educator

An experienced instructor with expertise in this field and a passion for teaching.

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