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- AI
Generative AI for Data Science
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes: Preparing Data with Generative AI Querying Data with Generative AI Data Analysis with Generative AI Feature Engineering with Generative AI Generating Synthetic Datasets with Generative AI Visualizing Data and Storytelling with Generative AI Model Evaluation and Interpretation with Generative AI Automating Data Science Tasks with Generative AI Integrating Generative AI into Data Science workflows Responsible and Ethical use of Generative AI in Data Science
Generative AI offers powerful ways to speed up and enrich every step of the data science process. This path guides you through how to use GenAI to prepare data, analyze datasets, engineer features, visualize insights, evaluate models, and automate reporting. Along the way, you’ll learn best practices for prompt design, ethical considerations, and how to integrate GenAI tools into real-world data science workflows.
Content in this path
Generative AI for Data Science
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What You'll Learn
- You will learn how to use generative AI across every stage of the data science workflow, from preparing, querying, and analyzing data to generating new features and synthetic datasets.
- You will learn how to turn natural-language prompts into powerful analytical tools, using GenAI to write queries, summarize insights, create visualizations, and explain complex patterns with clarity.
- You will learn how to enhance model evaluation and interpretation with AI, leveraging GenAI to compare models, explain predictions, and improve the transparency of your machine-learning work.
- You will learn how to automate repetitive data science tasks with GenAI, enabling faster experimentation, streamlined reporting, and greater productivity with AI-driven assistants.
- You will learn how to apply responsible and ethical principles when using GenAI in data science, ensuring fairness, privacy, and appropriate human oversight.
- Learners are expected to be familiar with the Data Science lifecycle and workflow. Familiarity with Generative AI is helpful, but not required.
- Generative AI
- Large Language Models
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