Data Governance and Legal Compliance for Data Science

Data Science relies on vast amounts of information, but mishandling sensitive data can lead to serious legal and financial consequences. This course provides a foundational understanding of the legal landscape surrounding modern data science. You will learn how to implement robust data governance strategies and apply ethical frameworks to ensure your models are compliant, fair, and trustworthy. What you'll learn: * Understand the core principles of global data privacy and protection regulations. * Apply best practices for obtaining informed consent, anonymization, and data retention. * Analyze common legal risks associated with data collection, storage, and cross-border transfer. * Practice implementing ethical frameworks to identify and mitigate bias in machine learning models. * Configure basic data governance structures, including data lineage and access controls, within a project. * Learn how explainability (XAI) concepts relate to mandated transparency in automated decision-making. The course begins with essential terminology and definitions of data types and legal frameworks. We then move into practical applications of data governance, ethical modeling, and compliance checkpoints throughout the entire data science lifecycle. This course is designed for beginners—data scientists, analysts, and project managers—who have no prior experience with legal compliance or data governance. No prerequisites are required. Start building compliant and responsible data science solutions today.