Steal these expert-crafted AI prompt templates to supercharge your data science workflow, analysis, learning, and career roadmap.
If you’re a data scientist, analyst, or aspiring ML engineer, these ChatGPT prompts for data science will save you hours. From EDA to model tuning and storytelling with data, these optimized prompts help you write cleaner code, debug faster, explain complex concepts, and even prep for interviews—all using natural language. Whether you’re working with Python, pandas, or PyTorch, these AI prompts act as your virtual data mentor.
Act as a professional data scientist. You are given a dataset with the following columns: [insert columns or dataset info].
Perform a thorough Exploratory Data Analysis using Python. Include the following:
1. Basic dataset overview (shape, info, missing values)
2. Summary statistics
3. Data distribution visualizations (histogram, boxplot)
4. Correlation heatmap
5. Outlier detection and treatment suggestions
6. Feature insights and data imbalance analysis
7. Final EDA summary with next-step suggestions
Provide only Python code (using pandas, matplotlib, seaborn) and brief inline explanations.
Act as a machine learning expert. I have built a model using [e.g. Random Forest] for [insert problem statement].
I need help evaluating the model. Please provide Python code to:
1. Calculate classification metrics (Accuracy, Precision, Recall, F1-Score)
2. Generate a confusion matrix and classification report
3. Plot ROC curve and AUC score
4. Explain what each metric means and when to prioritize one over the other
5. Offer tips to improve performance (hyperparameter tuning, feature engineering, etc.)
Use scikit-learn, matplotlib, and seaborn where applicable.
Act as a senior AI mentor. Explain the concept of "[insert topic: e.g., Gradient Descent / Overfitting / Regularization]" in simple terms
to a beginner who has basic knowledge of Python but is new to data science.
Include:
- A clear analogy
- Python code examples
- Common misunderstandings
- Real-world use cases
Act as a career coach and expert data science mentor. I’m building a portfolio to land a data science job.
Suggest 5 high-impact, real-world data science project ideas that are:
- Relevant to 2024 job market trends
- Solvable with public datasets
- Use a variety of ML techniques (NLP, time series, classification, etc.)
- Visually appealing with dashboards or reports
- Resume-worthy with measurable impact
Include a 1-line project summary, problem statement, tools to use, and dataset suggestions.
Act as a senior data science hiring manager.
Here's my resume content (paste it). Optimize it for a job in data science by:
1. Rewriting bullet points to be more data-driven and results-focused
2. Using action verbs and metrics
3. Matching current job descriptions and keywords
4. Removing fluff and buzzwords
5. Structuring it for readability (ATS-friendly)
Return the improved version in Markdown format with clean formatting.
Act as a SQL expert. Convert the following plain English request into an optimized SQL query:
"[Insert plain-language question: e.g., Show me the top 5 products by sales in 2023 for category 'Electronics']."
Make sure to:
- Assume a reasonable table schema
- Include JOINs or GROUP BY if needed
- Add comments to explain logic
Act as a FAANG-level data science interviewer. Conduct a mock interview session for a data science role.
Include:
- 3 technical questions (coding, statistics, ML)
- 2 behavioral questions
- Sample answers or hints
- Tips on how to structure STAR method responses
Make it beginner-friendly but still challenging. Add follow-up questions to simulate real interview flow.
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