Practical LLMs for Modern Data Science

This is the repository for the LinkedIn Learning course Practical LLMs for Modern Data Science. The full course is available from LinkedIn Learning.

Course Description

Learn how large language models (LLMs) can be used as practical assistants in everyday data science work. Start by seeing LLM-generated code in action and then build your own reusable helpers that speed up repetitive tasks such as exploration, preprocessing, and boilerplate modeling. Find out how to use LLMs to generate code that can be inspected, executed, and validated, rather than trusting black-box outputs. Explore how LLMs behave in classification and regression tasks and how to evaluate those results using standard data science metrics.

Learning objectives

• Build reusable LLM-powered helpers that generate inspectable Python code for common data science tasks.
• Use LLMs effectively across key stages of the data science workflow, including exploration, preprocessing, modeling, and evaluation.
• Apply LLMs to classification and regression tasks using prompt-based approaches and validate the results with standard metrics.
• Decide when to rely on LLM assistance and when human judgment, validation, and domain knowledge are required.

Installing

1. To use these exercise files, you must have the following installed:
   • Python, at least version 3.10
   • A text editor such as Visual Studio Code, Sublime Text, Atom, or another editor
   • JupyterLab, or another environment that can run Jupyter notebooks
   • A Google Gemini API key from Google AI Studio
   • Ollama, for the local/private LLM exercise in chapter 5
2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
3. Detailed setup instructions are available in the Chapter 00_02 - Course Setup and What You'll Build.

Instructor

Parul Pandey

AI Educator | Kaggle Grandmaster (Notebooks)