Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course empowers you to harness AI and modern tools for effective data modeling, improving your ability to design, analyze, and optimize databases and analytics systems. You'll gain practical experience using ChatGPT and GitHub Copilot to accelerate data model creation and documentation. The journey begins with foundational concepts, covering conceptual, logical, and physical data models, dimensional modeling, additivity, conformed and slowly changing dimensions, normalization, and star schema design. You will explore practical demonstrations in Power BI and diagramming tools, ensuring a strong grasp of data modeling principles. This course is ideal for data analysts, BI professionals, developers, and anyone seeking to leverage AI-assisted tools for data modeling. No prior experience is required, though familiarity with databases is helpful. By the end of the course, you will be able to design conceptual, logical, and dimensional models, implement ETL pipelines, document schemas, perform data quality checks, and integrate AI tools to streamline modeling workflows.

Syllabus

Welcome to the Course

In this module, we will introduce the course and explore why AI ethics matter. We will analyze AI in popular culture and discuss the associated risks. Learners will gain foundational insights into ethical considerations for AI systems.

Introduction to AI Ethics

In this module, we will cover the fundamentals of AI and its ethical relevance. Learners will explore distinctions between ethics, law, and compliance. Past technological lessons will guide understanding of ethical challenges in AI.

Ethical Frameworks for AI

In this module, we will examine key ethical frameworks that guide AI development. Learners will understand different philosophical approaches and apply them to practical cases. Ethical decision-making in AI will be contextualized through examples.

Fairness, Bias, and Discrimination

In this module, we will explore fairness and bias in AI systems. Learners will study cases highlighting discrimination and learn ethical responses. The focus is on understanding challenges and implementing mitigation strategies.

Transparency, Explainability, and the Black-Box Problem

In this module, we will introduce transparency and explainability in AI. Learners will analyze the black-box problem and its risks. Ethical importance of transparent AI systems will be emphasized.

Autonomy, Labor, and Economic Impact

In this module, we will explore AI’s impact on labor and the economy. Learners will examine human-AI collaboration and job displacement. Ethical considerations for equitable AI benefits will be discussed.

Safety, Alignment, and Control

In this module, we will cover AI safety, alignment, and control issues. Learners will explore research directions and potential risks. Emphasis is placed on ethical deployment of autonomous systems.

Power, Governance, and Regulation

In this module, we will examine AI governance and regulatory frameworks. Learners will assess value embedding, corporate influence, and global oversight. Ethical considerations in AI policymaking will be explored.

AI in High-Risk Domains

In this module, we will explore ethical concerns of AI in sensitive domains. Learners will examine risks in healthcare, law enforcement, and social media. Strategies for ethical AI deployment will be discussed.

Practical Ethics and Professional Responsibility

In this module, we will focus on practical ethics and professional responsibility. Learners will learn frameworks for workplace ethical decisions. Building an ethical AI culture in organizations will be emphasized.

Conclusion

In this module, we will review key concepts from the course. Learners will reflect on AI ethics and governance. The section consolidates insights for practical ethical application in AI.