Generative AI for Data Science

This course introduces practical techniques for effectively using, evaluating, and responsibly applying generative AI in data science and statistics. Participants will gain a clear understanding of how generative AI models work and learn how to integrate AI tools into their own analytical workflows to enhance productivity, insight generation, and communication. The course focuses on four key areas: understanding the underlying principles, strengths, and limitations of generative AI models; developing a structured framework for ongoing learning and professional development with AI; best practices for transparently reporting and documenting generative AI use; and promoting safe, ethical, and responsible use of generative AI in data-driven work. This course is designed for data analytics professionals who want to use generative AI more effectively in their work. It is suitable for those with some experience in data analysis who are new to generative AI, as well as practitioners seeking to strengthen their understanding of its capabilities, risks, and best practices. By the end of the course, participants will be able to confidently evaluate generative AI tools, integrate them into their workflows, communicate their use clearly and responsibly, and make informed decisions about when and how generative AI should be applied in data science contexts

• Understand how generative AI models work and integrate AI tools within workflows
• Evaluate generative AI tools responsibly and integrate them in data analysis
• Adopt ethical and transparent practices in generative AI use
• Enhance insight generation and communication through AI integration

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work