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Prompt Engineering for Data Professionals

You are missing AI's potential to transform your data analysis workflow. This course will teach you systematic prompt engineering techniques to unlock reliable, efficient outputs from LLMs for data tasks.

Beginner
21m

Created by Dan Tofan

Last Updated Oct 08, 2025

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  • Course

Prompt Engineering for Data Professionals

You are missing AI's potential to transform your data analysis workflow. This course will teach you systematic prompt engineering techniques to unlock reliable, efficient outputs from LLMs for data tasks.

Beginner
21m

Created by Dan Tofan

Last Updated Oct 08, 2025

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What you'll learn

Data professionals are missing significant productivity gains and professional advancement opportunities by not effectively leveraging AI tools for their analysis workflows. In this course, Prompt Engineering for Data Professionals, you'll learn to systematically structure prompts to unlock AI's full potential for your data analysis tasks and accelerate your professional capabilities. First, you'll explore the Role-Task-Context-Format framework and essential templates for data cleaning, statistical analysis, and SQL generation. Next, you'll discover how to prompt AI tools to generate visualization code and automate reporting tasks effectively. Finally, you'll learn persona-based prompting techniques to communicate complex data findings to different stakeholder audiences and implement quality control practices for consistent results. When you're finished with this course, you'll have the skills and knowledge of systematic prompt engineering needed to transform your data analysis workflow and position yourself at the forefront of AI-enhanced professional practice.

Prompt Engineering for Data Professionals
Beginner
21m
Table of contents

About the author
Dan Tofan - Pluralsight course - Prompt Engineering for Data Professionals
Dan Tofan
18 courses 4.7 author rating 259 ratings

As a software engineer and lifelong learner, Dan wrote a PhD thesis and many highly-cited publications on decision making and knowledge acquisition in software architecture. Dan used Microsoft technologies for many years, but moved gradually to Python, Linux and AWS to gain different perspectives of the computing world.

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