Generative AI for Data Science: Modernizing Workflows and Analytics

Data science is evolving rapidly, and generative AI is transforming how we clean, analyze, and model data. This text-based course guides you through incorporating these powerful tools into your daily data workflows without needing an advanced background in machine learning. By completing this course, you will transition from traditional manual data processes to AI-assisted workflows. You will learn how to leverage generative models to generate synthetic data, automate repetitive data preparation tasks, and optimize machine learning models. What you'll learn: - Understand the core terminology and mechanics of generative AI models in a data science context - Apply prompt engineering techniques to generate, clean, and format datasets efficiently - Create synthetic data to address class imbalances and privacy concerns in your analyses - Build and refine Python code for machine learning models using AI-assisted coding partners - Explore modern retrieval-augmented generation (RAG) concepts to query your own data repositories - Practice evaluating and validating AI-generated outputs to ensure accuracy and reliability Starting with fundamental definitions and core concepts, this course guides you step-by-step through practical applications in data preparation, exploratory data analysis, and model optimization. You will learn through clear, written explanations and code snippets that you can immediately adapt to your own projects. This course is designed for beginner data analysts, aspiring data scientists, and programming enthusiasts. No prior experience with generative AI is required, though a basic familiarity with Python is helpful. Start learning today to bring the power of generative AI to your data science toolkit.