Generative AI for Data Scientists

This course is designed for everyone, including professionals, executives, students, and enthusiasts, interested in learning about generative AI and leveraging its capabilities in their work and lives. This course is your first step toward understanding the capabilities of generative AI, powered by different models, including large language models (LLMs). In this course, you will learn about the fundamentals and evolution of generative AI. You will explore the capabilities of generative AI in different domains, including text, image, audio, video, virtual worlds, code, and data. You will understand the applications of generative AI across different sectors and industries. You will learn about the capabilities and features of common generative AI models and tools, such as GPT, DALL-E, Stable Diffusion, and Synthesia. Hands-on labs, included in the course, provide an opportunity to explore the use cases of generative AI through IBM Generative AI Classroom and popular tools like ChatGPT. You will also hear from the practitioners about the capabilities, applications, and tools of generative AI.

This course is designed for everyone, including professionals, executives, students, and enthusiasts interested in leveraging effective prompt engineering techniques to unlock the full potential of generative artificial intelligence (AI) tools such as ChatGPT. Prompt engineering involves crafting inputs that guide generative AI models to produce accurate, relevant, and high-quality outputs. In this course, you will learn the techniques, approaches, and best practices for writing effective prompts. You will learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will apply various prompt engineering approaches such as the interview pattern, chain-of- thought, and tree-of-thought, which aim at generating precise and relevant responses. You will also explore text-to-image prompting techniques to generate high-impact visuals using image-generation models. Additionally, you will be introduced to commonly used prompt engineering tools like IBM watsonx Prompt Lab, Spellbook, and Dust. The hands-on labs included in the course offer an opportunity to optimize results by creating effective prompts in the IBM Generative AI Classroom. You will also hear from practitioners about the tools and approaches used in prompt engineering and the art of writing effective prompts. By the end of the course, you will have a strong foundation in prompt engineering techniques and strategies to effectively interact with generative AI systems.

Generative AI is now mainstream. Boost your career with a course that features leading-edge, in-demand, generative AI skills tuned to the needs of data scientists. This course is suitable for existing and aspiring data scientists, data professionals, analysts, and engineers. The course addresses real-world data science problems data scientists encounter—across multiple industries— with data generation, data augmentation, and feature engineering. Gain skills you can immediately put to use implementing generative AI models and techniques that address these real-world issues. Then, learn how to use generative AI to speed data visualizations, build models and to produce data insights. You’ll also learn about key ethics considerations around generative AI and data, key concerns for executives across industries. Demonstrate your new generative AI skills in a hands-on data augmentation and feature engineering project that you can apply in your real-life profession. Then complete your final quiz to earn your certificate. You can share both your project and certificate with your current or prospective employers.