Practical LLMs for Modern Data Science
1h 6mIntermediate2026-06-23
Authors
Parul Pandey
Course details
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.
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.
Skills covered
Machine Learning FundamentalsTraditional AI and Machine LearningData AnalysisArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsOne-Off
Concepts
Introduction - Get Productive Fast
- How LLMs fit into data science
- Building your first LLM helper - From prompt to inspectable Python code
Exploratory Data Analysis Helpers
- Generate an EDA summary helper
- Extend the EDA helper with visual diagnostics
- Validate and refine EDA helper outputs
Preprocessing Helpers
- Build a data cleaning helper
- Create a feature encoding and scaling helper
- Combine preprocessing helpers into a reusable pipeline
Modeling Helpers
- Build a baseline classification helper
- Build a regression modeling helper
- Refine and customize model helpers
Evaluation Helpers
- Build a classification evaluation helper
- Build a regression evaluation helper
- Validate model performance with baselines
Judgment, Control, and Responsible Use
- Make LLM helpers auditable and reproducible
- Run helpers with local or privacy-preserving LLMs
End-to-End Application
- Run the full helper-based workflow
- Stress-testing helpers on a new dataset
Wrap-Up and Next Steps
- Patterns for building your own LLM helpers