Strategic Data Collection, Modeling, and Quality Management for AI Systems

In this course, data strategist Joe Squire introduces the foundational principles of strategic data collection, modeling, and quality management essential for building effective AI systems. Learn how to design a data collection strategy that aligns with business goals, focusing on the quality, relevance, and representativeness of data to support reliable AI insights. Discover ways to maintain data accuracy, identify and mitigate biases, and implement quality control practices to enhance data integrity across AI workflows. Additionally, explore data modeling basics to support AI applications, building a strong foundation for further topics in scalable storage, secure data management, and advanced AI processing techniques. By the end of this course, you will be equipped with actionable strategies to ensure high-quality data that drives accurate and unbiased AI outcomes.

Learning objectives
Design a strategic data collection plan that aligns with business objectives and supports the specific requirements of AI systems, ensuring data relevancy and comprehensiveness.
Recognize and mitigate common biases in data collection and modeling to enhance the fairness and reliability of AI outputs.
Identify and implement key data quality management practices that maintain accuracy, consistency, and completeness of data across AI pipelines.
Construct foundational data models suited for AI application, understanding how model choice impacts downstream data processing and insights.
Apply practical quality control techniques to monitor, cleanse, and validate data ensuring it meets the high standards necessary for successful AI development.