Ready-to-use ChatGPT prompts to simplify machine learning, data analysis, and storytelling for your next data science breakthrough.
Data science is more than just crunching numbers—it’s about uncovering insights that drive smart decisions. Whether you’re a beginner working on your first dataset or a pro optimizing machine learning pipelines, ChatGPT prompts for data science can speed up tasks like EDA, model interpretation, hypothesis testing, feature engineering, and storytelling. These AI-powered prompts help data scientists automate repetitive work, validate logic, generate Python and SQL code, and even explain complex results in plain English. Use them to boost productivity, improve model quality, and craft compelling narratives with your data.
Act as a senior data analyst.
I have a dataset with the following columns: [Insert column names].
Write Python code to:
- Load the data (assume it's a CSV)
- Show basic stats (mean, median, std dev)
- Handle missing values smartly
- Plot histograms and boxplots for numerical columns
- Show pairwise correlation and recommend possible multicollinearity
Give clean and commented code using pandas, matplotlib, and seaborn.
Act as a machine learning engineer.
I have a classification problem. The target column is [Insert target]. The features include [Insert features].
Generate Python code to:
- Preprocess the data (encode categoricals, normalize)
- Split train/test set (80/20)
- Train Logistic Regression, Random Forest, and XGBoost
- Print confusion matrix, accuracy, and ROC-AUC
- Recommend the best model based on results
Also explain each step as markdown comments.
Act as a feature engineering expert.
I’m working with this dataset: [briefly describe dataset]. I want to create better features.
Based on the column names: [Insert column list], suggest:
- At least 5 new derived features with formulas
- Interactions or aggregations that may help
- Dimensionality reduction techniques if needed
Make it specific to classification or regression (state which).
Act as a data analyst proficient in SQL.
I have a table called [table_name] with columns: [column1, column2, column3, date].
Write SQL queries to:
1. Count rows grouped by [column1]
2. Show monthly trends for [column2] over the last 12 months
3. Find top 5 [column3] values by average [column2]
Optimize for PostgreSQL.
Act as a data storyteller for a business audience.
Here are my insights: [Insert summary of insights from your analysis].
Convert this into:
- An executive summary (200 words)
- 3-slide presentation content with titles and bullet points
- A short LinkedIn post summarizing the results with impact
Make it simple, jargon-free, and business-focused.
Act as a statistical analyst.
I want to test if [Insert hypothesis, e.g., new feature improves click-through rate].
Given:
- Sample A has mean = [value], std = [value], n = [number]
- Sample B has mean = [value], std = [value], n = [number]
Generate Python code to:
- Perform appropriate hypothesis test (e.g., t-test or z-test)
- Show p-value and interpretation
- Visualize the distributions
Add commentary on statistical significance.
Act as a data cleaning assistant.
I uploaded a messy dataset with:
- Duplicates
- Inconsistent date formats
- Typos in categorical columns
- Missing values
Write Python code to:
- Detect and remove duplicates
- Standardize date formats
- Fix category typos using fuzzy matching
- Impute missing values smartly based on column types
Use pandas and explain each step.
Act as a data science educator.
Explain [Insert concept: e.g., overfitting, PCA, p-value, random forest] to a 12-year-old.
Use analogies, no jargon, and make it fun to understand.
End with a real-world example to make the idea stick.
🚀 Copy, customize, and use these prompts to save hours on data wrangling, modeling, and reporting—directly from your AI dashboard.
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