Generative AI for Data Management and Remediation: Use Generative AI to Remediate and Improve Data

This course teaches you how to apply generative AI text models to automate and enhance data operations across the data lifecycle—from ingestion to storage—with an emphasis on improving data quality and remediating data issues.

Each module focuses on a specific stage of the data lifecycle. At every stage, you will explore how generative AI can be used to detect, resolve, and prevent data quality issues. For example, you'll learn how generative AI can improve data quality right from the ingestion stage, more easily define policies or access controls of stored data, and support decision-making in other key lifecycle stages.

What you’ll learn and how to apply it

By the end of this course, you will be able to:

• Understand how generative AI models can be applied to common data operations with a focus on data quality and remediation.
• Automate essential data management tasks using generative AI, allowing higher data quality in less time.
• Identify the differences between traditional/manual approaches and AI-driven approaches to various data operations and automate manual approaches using LLMs.

This course is for you because

This course is geared toward learners with a basic understanding of data management and AI, and is best suited for:

• Beginner to intermediate data practitioners and analysts.
• Professionals interested in applying generative AI to everyday data operations and challenges.
• Anyone seeking to deepen their understanding of AI-assisted data quality management.
• This course simplifies foundational data remediation and profiling techniques, while introducing generative AI-based tools and workflows. While the overall level is beginner-friendly, some intermediate concepts will also be introduced.

Prerequisites

• Cursory knowledge of data operations, such as data ingestion, profiling for data quality issues, data management, and data remediation
• ursory knowledge of data lifecycles: ingestion, improvement, remediation, storage, transformation, deletion, etc., and what happens at each stage
• Cursory knowledge of generative AI text models (e.g. ChatGPT and other LLMs), and their capabilities

Next Steps

• Take a course focused on a specific generative AI model or application area (e.g., text summarization, code generation).
• Enroll in a course on advanced data pipelines or complex data operations to learn how to apply AI in more intricate workflows.
• Explore a course on data governance, advanced remediation, or data quality practices to strengthen your data management expertise.

Course Materials

• (Live Course) ChatGPT for Data Analytics (Tobias Zwingmann) (Link)
• (Live Course) Generative AI for Automating Data Pipelines and Analytic Queries (Link)
• (Book) What is Generative AI? (Kyle Strattis)
• (Book) AI & Data Literacy (Bill Schmarzo)
• (Book) Prompt Engineering for Generative AI (James Phoenix, Mike Taylor) (Link)