ChatGPT Prompts for Data Science

Provides a structured prompt template pattern where ChatGPT assumes specific data science roles (data scientist, ML engineer, SQL expert, statistician) to deliver specialized expertise. The template follows a consistent three-part structure: role specification ('I want you to act as [role]'), task description ('[specific task]'), and input placeholders ('[user context]'). This role-assumption pattern primes ChatGPT's response generation toward domain-specific terminology, methodologies, and best practices without requiring explicit instruction on each interaction.

Generates Python code for data science tasks (model training, data manipulation, visualization) by providing ChatGPT with dataset descriptions, target variables, and desired outcomes. The prompt templates guide code generation for specific libraries (pandas, scikit-learn, matplotlib) and patterns (train-test splits, hyperparameter tuning, feature engineering). Code is generated as complete, executable snippets that can be directly pasted into Jupyter notebooks or scripts.

Provides career guidance and learning resource recommendations for data scientists by providing career goals, current skills, and interests to ChatGPT with career-focused prompts ('act as career advisor'). The prompt guides ChatGPT to suggest skill development paths, recommend learning resources, and provide portfolio project ideas. Output includes both recommendations and rationale for career progression.

Provides guidance on effective prompt engineering for ChatGPT by documenting prompt design patterns, best practices, and optimization techniques. The repository includes a dedicated section on prompt engineering that explains how to structure prompts for clarity, specificity, and effectiveness. This meta-capability enables users to improve their own prompts and understand why the provided templates work well.

Generates natural language explanations of existing Python or SQL code by providing code snippets to ChatGPT with a role-assumption prompt ('act as code explainer'). The prompt guides ChatGPT to break down logic, explain library usage, describe data transformations, and identify potential issues. Output is formatted as readable documentation suitable for code comments, docstrings, or knowledge base entries.

Analyzes existing Python or SQL code and generates optimization suggestions by providing code snippets to ChatGPT with optimization-focused prompts ('act as performance engineer'). The prompt guides ChatGPT to identify bottlenecks, suggest faster algorithms, recommend library-specific optimizations (pandas vectorization, numpy broadcasting), and provide refactored code. Output includes both explanation of optimization rationale and executable improved code.

Generates SQL queries for data extraction, transformation, and analysis by providing ChatGPT with database schema descriptions, desired output, and optimization requirements. The prompt templates guide query generation for common data science tasks (aggregation, joins, window functions, CTEs). Includes both query generation and optimization prompts to improve readability and performance. Output is executable SQL suitable for direct database execution.

Translates code between programming languages (Python to R, SQL to pandas, etc.) by providing source code and target language to ChatGPT with translation-focused prompts ('act as code translator'). The prompt guides ChatGPT to maintain logic equivalence while adapting to target language idioms and libraries. Output is executable code in the target language with equivalent functionality.

Explains data science concepts, algorithms, and methodologies by providing concept names or questions to ChatGPT with explanation-focused prompts ('act as data science educator'). The prompt guides ChatGPT to provide clear explanations suitable for different audience levels, include practical examples, and connect concepts to real-world applications. Output is formatted as educational content suitable for learning materials or documentation.

Generates feature engineering ideas and model improvement suggestions by providing dataset descriptions, current model performance, and target variables to ChatGPT with ideation-focused prompts ('act as ML engineer'). The prompt guides ChatGPT to suggest new features, identify potential data quality issues, recommend feature selection techniques, and propose model architecture changes. Output includes both feature ideas and rationale for why they might improve model performance.

Provides debugging and troubleshooting guidance for data science code by providing error messages, code snippets, and context to ChatGPT with debugging-focused prompts ('act as debugging expert'). The prompt guides ChatGPT to identify root causes, suggest fixes, and explain why errors occurred. Output includes both diagnosis and corrected code or configuration.

Provides guidance on statistical analysis and experimental design by providing research questions, data descriptions, and constraints to ChatGPT with statistics-focused prompts ('act as statistician'). The prompt guides ChatGPT to recommend appropriate statistical tests, suggest experimental designs (A/B tests, multivariate tests), and explain statistical assumptions. Output includes both recommendations and rationale for methodological choices.