Course

Using AI to Work with Data

AI tools like ChatGPT and other large language models are becoming part of everyday data work, but many people aren’t sure how to use them effectively. You don’t need to build models or write code to benefit from AI. What you do need is an understanding of how to ask the right questions, guide AI outputs, and use AI responsibly to support data-related tasks. This course is designed for non-technical learners who work with data and want practical, realistic ways to use AI to improve communication, exploration, and productivity.

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Part of the Data Literacy and AI Fundamentals path.

4.8 (359 reviews)
  • Beginner friendly
  • 6 hours
  • Self paced
  • 3 lessons
Otavio

“The learning paths on Dataquest are incredible. They give you a direction through the learning process – you don’t have to guess what to learn next.”

Otávio Silveira

Data Analyst @ Hortifruti

Using AI to Work with Data

Enroll For Free

Course overview

AI is becoming a common tool for working with data, even for non-technical roles. In this course, you’ll learn how to use AI tools to explore data, improve communication, and support everyday data tasks—without writing code or building models. You’ll gain practical skills for prompting AI effectively and understanding its strengths and limitations in real data work.

Key skills

  • Using AI tools to clarify and communicate data insights
  • Writing effective prompts for data-related questions and tasks
  • Improving productivity in data work without adding technical complexity
  • Evaluating AI outputs critically rather than accepting them at face value
  • Understanding when and when not to rely on AI for data tasks

Course outline

Using AI to Work with Data [3 lessons]

Using LLMs to Improve Data Communication 2h

Lesson Objectives
  • Translate data insights into clear, audience-appropriate language
  • Write effective prompts by providing context, audience, and tone
  • Adapt messaging across formats: emails, slides, and summaries
  • Recognize LLM limitations: they communicate findings, not analyze data
  • Iterate collaboratively with LLMs to refine communication quality

Introduction to Prompt Engineering for Data Professionals 2h

Lesson Objectives
  • Understand how prompt engineering enhances data professional workflows
  • Distinguish between system prompts and user prompts effectively
  • Apply special characters (quotes, backticks) for prompt clarity
  • Implement Retrieval-Augmented Generation for organizational knowledge access
  • Select appropriate AI model types for specific tasks
  • Craft instructional prompts with explicit constraints and formats
  • Use few-shot prompting to establish consistent output patterns
  • Apply 'Clarify, Confirm, Complete' technique for alignment
  • Iterate and adapt prompts based on response evaluation

Practical Application of Prompt Engineering for Data Professionals 2h

Lesson Objectives
  • Generate synthetic survey data with realistic distributions
  • Categorize qualitative feedback into meaningful themes using few-shot prompting
  • Extract structured JSON outputs from unstructured text responses
  • Perform multi-dimensional sentiment analysis on survey feedback
  • Combine AI-assisted analysis with code-based statistical implementation

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Aaron

Aaron Melton

Business Analyst at Aditi Consulting

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Jessica Ko

Machine Learning Engineer at Twitter

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