Introduction
As data science evolves, prompt engineering has emerged as a key skill to leverage the power of large language models (LLMs) for analytical tasks. This course empowers data professionals to craft effective prompts that can clean data, generate code, interpret models, extract insights, and automate reporting—all through natural language interfaces.

Prerequisites

• Basic knowledge of data science concepts and workflows

• Familiarity with Python, Jupyter Notebooks, or data tools (e.g., pandas, SQL)

• Understanding of large language models (optional but helpful)

Table of Contents

1. Introduction to Prompt Engineering in Data Science
   1.1 Role of LLMs in Modern Data Workflows
   1.2 Capabilities and Limitations of AI in Analytics
   1.3 Examples of Prompt-Driven Data Science Tasks

2. Data Cleaning and Transformation with Prompts
   2.1 Describing Data Issues in Natural Language
   2.2 Prompting for Python or SQL Data Fixes
   2.3 Structuring Prompts for Data Normalization & Formatting

3. Generating Code with LLMs for Data Analysis
   3.1 Prompting for Exploratory Data Analysis (EDA)
   3.2 Writing Functions, Queries, and Scripts via Prompts
   3.3 Code Review and Optimization Suggestions from LLMs

4. Statistical Analysis and Interpretation
   4.1 Asking for Statistical Tests or Models through Prompts
   4.2 Interpreting Regression, Correlation, and Clustering Results
   4.3 Visualizing Output with Prompt-Generated Code

5. Prompting for Machine Learning Workflows
   5.1 Building ML Models Step-by-Step with Prompts
   5.2 Feature Engineering and Model Selection
   5.3 Tuning Hyperparameters and Explaining Results

6. Data Storytelling and Reporting Automation
   6.1 Summarizing Key Findings in Natural Language
   6.2 Generating Executive-Level Reports with Prompts
   6.3 Converting Notebooks to Narratives

7. Case Studies in Prompt-Driven Analytics
   7.1 Customer Segmentation
   7.2 Sales Forecasting
   7.3 Sentiment Analysis on Social Data

8. Best Practices in Prompt Engineering for Analysts
   8.1 Iterative Prompt Refinement
   8.2 Structuring Inputs and Context
   8.3 Avoiding Hallucination in Analytical Tasks

9. Tools and Platforms for Prompt-Driven Data Work
   9.1 Using LLMs in Jupyter, VS Code, or Chat Interfaces
   9.2 Integrating OpenAI, Azure OpenAI, or Google PaLM into Workflows
   9.3 Prompt Libraries and Reusability in Teams

10. Ethical and Responsible Prompting in Data Science
    10.1 Transparency and Explainability in AI Outputs
    10.2 Managing Data Privacy and Security
    10.3 Validating AI-Generated Analytical Insights

Prompt engineering enhances data science by enabling faster analysis, smarter automation, and natural language collaboration with AI. By learning to craft precise, context-aware prompts, data professionals can improve productivity, boost insight generation, and adapt to a future of AI-augmented analytics.