Generative AI for Data Science

Overview

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.

Syllabus

• Module 1: Understanding
Our first module, Understanding, introduces generative AI and how large language models work. It explores their strengths and limitations, helping learners build a conceptual understanding for responsible use in learning and study.
• Module 2: Development
Our second module, Development, focuses on using generative AI in projects and tasks. It covers building effective strategies, responsible application, and understanding when AI or human judgement is most appropriate.
• Module 3: Reporting
Our third module, Reporting, examines how generative AI use should be declared and documented. It explores reporting practices, regulations, and ethical considerations across different work and research contexts.
• Module 4: Safety
Our final module, Safety, addresses organisational approaches to generative AI. It covers staff education, risk management, and establishing processes and documentation to support responsible and consistent use.

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Taught by

Jennifer Gaskell, Craig Alexander, Jake Lever, and Vinny Davies

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Subjects

Data Science