Generative AI Workflows for Data Scientists

As data complexity grows, traditional analysis methods can be significantly accelerated by artificial intelligence. Integrating generative AI into your data science toolkit allows you to automate repetitive tasks, generate synthetic data, and extract deeper insights faster. This written course equips you with the foundational knowledge and practical code patterns needed to leverage large language models in your data projects. You will transition from manual data wrangling to AI-assisted workflows, enhancing your productivity and expanding your analytical capabilities. What you'll learn: - Understand the core principles of generative AI, transformer architectures, and large language models. - Apply prompt engineering techniques specifically optimized for data cleaning, feature engineering, and code generation. - Explore Retrieval-Augmented Generation (RAG) patterns and vector databases to query custom document collections. - Generate high-quality synthetic data to balance datasets and train robust machine learning models. - Analyze unstructured text data at scale using API-driven language model integrations. The course begins with essential terminology and the mechanics of language models before moving into step-by-step written guides on API integration, prompt design, and data augmentation. You will work through structured text explanations and clear code examples designed to fit directly into your existing Python pipelines. This course is designed for aspiring and practicing data scientists who want to integrate AI tools into their work; no prior experience with generative AI is required, though basic Python familiarity is helpful. Start reading today to modernize your data science workflow with generative AI.