Master data insights faster with these ready-to-use ChatGPT prompts tailored for analysts, researchers, and business professionals.
If you’re tired of spending hours cleaning messy data, building complex queries, or explaining insights to stakeholders—ChatGPT can be your full-stack data analyst. These data analysis ChatGPT prompts are built to help you with everything from data cleaning, EDA, statistical testing, to visual storytelling. Whether you’re a beginner in Excel or a seasoned Python pro, these prompts will help you analyze, visualize, and communicate data faster and better using AI.
Act as a professional data cleaning assistant. I will provide you raw data with missing values, inconsistent entries, and irrelevant columns. Your task is to:
- Identify and list all data quality issues
- Suggest appropriate methods for cleaning each issue (drop, impute, normalize, etc.)
- Provide Python code snippets using pandas to apply those fixes
- Explain why each fix is appropriate
Here’s the data context: [Insert brief description or dataset]
Output format:
1. Issues identified
2. Suggested fixes
3. Python code
4. Justification
Act as a data scientist performing exploratory data analysis (EDA). I will input a dataset or schema description. Your job is to:
- Provide an overview of the dataset (dimensions, types, missing values)
- Recommend key questions to explore
- Generate Python code (using pandas and seaborn/matplotlib) to visualize distributions, correlations, and outliers
- Highlight interesting patterns, anomalies, and business implications
Dataset context: [Insert dataset details]
Structure the output into sections: Summary, Key Questions, Code, Insights.
Act as a machine learning analyst. I have a dataset and want to build a predictive model. Your task is to:
- Recommend the right algorithm (classification/regression/clustering)
- Explain feature engineering steps based on the dataset
- Suggest evaluation metrics
- Generate a scikit-learn pipeline to preprocess and train the model
- Provide suggestions to improve model performance
Here’s the dataset description: [Insert dataset info]
Act as a statistical analyst. I will describe an experiment or dataset. You must:
- Identify suitable statistical tests (t-test, chi-square, ANOVA, etc.)
- Justify why that test is appropriate
- Provide Python code to perform the test
- Interpret the result in plain English with business relevance
Experiment/Data: [Insert description]
Confidence level: [default 95% unless stated]
Act as a data visualization expert. I will share a dataset or analysis goal. Your job is to:
- Suggest the most appropriate charts/plots for each insight
- Provide Python code using seaborn/matplotlib/plotly
- Make the visuals presentation-ready (titles, labels, colors)
- Recommend interactive dashboards using Plotly or Streamlit (optional)
Goal: [E.g. Show trends, compare categories, detect outliers]
Data context: [Insert details]
Act as a business data storyteller. Based on data findings or EDA results I provide, convert them into a simple story for non-technical stakeholders.
Steps:
- Identify the key takeaways (trends, risks, opportunities)
- Summarize the insights using simple language
- Use metaphors or analogies if needed
- Provide a 3-slide structure outline for an executive presentation
Findings: [Insert results or metrics]
Make it sound like a story, not a report.
Act as a SQL query generator. I will give you a plain English request and database schema. Your job is to:
- Convert the request into an optimized SQL query
- Explain what the query does
- Suggest indexing or performance improvements if the dataset is large
Request: [e.g., “Find top 5 products with highest sales in 2023”]
Schema: [Insert schema]
Provide both the query and explanation in plain text.
Act as an Excel formula expert. I will describe a data task and provide column headers. Your goal:
- Recommend the right Excel formula or combination of formulas
- Explain how it works
- Suggest how to create a pivot table if relevant
- Include best practices for Excel automation
Task: [E.g., “Calculate average sales per region ignoring blanks”]
Headers: [Insert column names]
💡 Start using these prompts now to boost your data game and impress your team with faster, sharper analysis.
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