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ChatGPT can help with much of the data-science workflow: inspecting files, cleaning data, writing and running Python, creating charts, explaining SQL, prototyping models, and drafting reports. Its data-analysis capability—formerly commonly called Advanced Data Analysis or Code Interpreter—can, for some tasks, execute Python in a stateful Jupyter-style environment and work with uploaded files.
The safe rule is simple: ask for the plan and executable code, then verify the result. ChatGPT is an analysis assistant, not an independent source of truth. Review its code, filters, assumptions, denominators, statistical methods, and outputs before using a finding to make a decision.
Interface note: model names, plan limits, upload quotas, connectors, chart modes, and menu labels change frequently. Check availability in your account and consult the current OpenAI data-analysis documentation before relying on a specific feature or limit.
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The seven-step ChatGPT data-science workflow
- Define the decision. Say what action the analysis should support and what “done” means.
- Prepare the data. Use clear headers, consistent types, explicit units, and a documented date range.
- Upload or connect the source. Use a supported file or an available Drive, OneDrive, SharePoint, Excel, or Google Sheets connection.
- Audit before changing anything. Check completeness, missingness, duplicates, types, ranges, and possible leakage.
- Clean and explore reproducibly. Preserve the raw data, show transformations, and make the denominator behind every result explicit.
- Test or model cautiously. Select methods based on the question, sampling design, outcome type, and business cost.
- Validate and communicate. Recalculate important numbers, inspect charts and errors, document assumptions, and distinguish association from causation.
OpenAI’s data-analysis guidance similarly recommends starting with the decision, then reviewing the generated work rather than accepting an unexplained conclusion.
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What ChatGPT can do for data science
- Explain Python, pandas, NumPy, SQL, R, scikit-learn, matplotlib, and related tools.
- Inspect supported CSV, Excel, JSON, text, PDF, and other uploaded files, subject to account and file-capability limits.
- Summarize columns, distributions, missing values, duplicates, outliers, trends, and category frequencies.
- Clean, reshape, filter, aggregate, and merge datasets.
- Create derived columns, summary tables, and charts.
- Write and, for some data-analysis tasks, execute Python in a stateful notebook environment.
- Prototype statistical analyses and baseline machine-learning models.
- Explain errors, revise code, document an analysis, and draft an executive summary.
Some bar, line, pie, and scatter charts may be interactive, while other charts may be static. Connected sources and spreadsheet integrations are available only where the relevant plan, workspace, account, region, and app settings support them. See OpenAI’s current capability notes for qualifications.
Copy-and-paste master prompt
Act as a senior data analyst. Help me answer this decision question:
[question and decision]
Data context:
- Each row represents: [record]
- Date range: [start and end date]
- Important definitions and units: [details]
- Data dictionary:
- column_name: meaning, unit, expected type
- column_name: meaning, unit, expected type
Requirements:
1. First list every file and sheet you can access and report row and column counts.
2. Propose an analysis plan and identify data-quality risks before calculating conclusions.
3. Preserve the raw data and show executable Python or SQL for every transformation.
4. Do not invent columns, rows, sources, or results. Ask instead of guessing.
5. State filters, denominators, assumptions, exclusions, and uncertainty.
6. Separate observed facts, calculations, assumptions, and interpretations.
7. Validate important results with an independent calculation or cross-check.
8. If the file is too large or incomplete, say exactly what was inspected.
Return: an assumptions table, code, results tables, charts where useful, limitations,
and a plain-language summary.Dataset preparation checklist
ChatGPT is more reliable when the file resembles a well-formed table rather than a presentation. Before uploading:
- Use descriptive column names, preferably with a consistent naming style.
- Keep one record per row and one variable per column.
- Use consistent data types: do not mix numbers, text, dates, and error strings in one field.
- Document units such as dollars, kilograms, minutes, or percentages.
- Define important fields, identifiers, targets, and business rules in a data dictionary.
- Record the source, date range, timezone, refresh date, and known exclusions.
- Remove blank separator rows and columns.
- Do not place unrelated tables on the same worksheet.
- Represent values as text or numbers rather than screenshots or images.
- Remove direct identifiers or use approved, redacted, or synthetic data.
I am uploading a dataset for analysis.
Business question: [decision or question]
Data dictionary:
- column_name: meaning, unit, expected type
- column_name: meaning, unit, expected type
Before analyzing:
1. List all files and sheets you can access.
2. Report row and column counts for each.
3. Identify duplicate rows, missing values, suspicious data types, impossible values,
likely identifiers, target columns, and possible leakage variables.
4. Do not modify the original data.
5. Ask about ambiguity before making irreversible assumptions.Prompts by data-science task
Data audit
Perform a data audit. Return file and sheet names, row and column counts, data types,
missing-value counts and percentages, duplicate-row count, unique-value counts,
summary statistics, date ranges, suspicious values, likely IDs, targets, leakage
variables, and sensitive fields. Show the Python code. Do not silently drop, impute,
or convert anything.Assumptions and cleaning
Create an assumptions table with: issue, evidence, proposed treatment, justification,
alternative treatment, and whether my approval is needed. Wait for approval before
applying changes that could affect the analysis.
Then create a reproducible cleaning pipeline. Preserve the raw file, standardize
column names, parse dates without silently guessing, report every removed row and
reason, document every imputation rule, flag suspicious outliers rather than deleting
them automatically, save the result as a new file, and show the complete code.Missing values
Analyze missingness by column and by relevant subgroup. Do not impute yet. For each
important field, recommend deletion, imputation, an explicit missing category, or
another treatment. Explain how each choice could bias the result and show counts and
percentages with their denominators.Duplicates and business keys
Find exact duplicate rows and likely business-key duplicates. Infer candidate keys,
check whether each key is unique, and show conflicting values for likely duplicates.
Do not delete records. Explain the effect each duplicate treatment would have on row
counts and metrics.Joins and merges
Before merging these datasets:
1. Identify likely join keys.
2. Check key uniqueness on each side.
3. Count unmatched rows on both sides.
4. Detect one-to-many and many-to-many relationships.
5. Predict how the merge will change row counts.
Then perform the merge, validate row counts and key uniqueness, and show the code.Exploratory data analysis
Perform an exploratory analysis focused on [business question]. Include univariate
summaries, key distributions, category frequencies, missingness patterns, time trends,
segment comparisons, outliers, and relationships worth investigating.
For every finding, provide the exact metric, numerator and denominator where relevant,
population, time period, chart or table, and a caveat about interpretation.Charts and visualization
Create a decision-focused chart set:
1. [chart] showing [metric] over [period]
2. [chart] comparing [groups]
3. [chart] showing the distribution of [variable]
4. [chart] showing the relationship between [x] and [y]
Use clear titles, axis labels, units, readable scales, and an accessible color palette.
Explain why each chart is appropriate. Do not use a dual axis without explaining its
interpretation risk, and identify incomplete periods or unequal denominators.Time series
Parse the date field and report timezone assumptions, missing dates, duplicate dates,
frequency, gaps, and whether the data is complete enough for trend analysis. Plot the
series using an appropriate aggregation. Distinguish calendar effects, seasonality,
and genuine changes where the data supports that distinction.Statistical hypotheses
Based on the EDA, propose three testable hypotheses. For each, state the null and
alternative hypotheses, outcome and explanatory variables, recommended method,
assumptions, confounders, multiple-comparison issue, effect size, and what evidence
would change the conclusion. Do not run the tests until I approve the plan. Label the
analysis exploratory or confirmatory.Machine learning
Build a transparent baseline model to predict [target]. First define the target and
identify target leakage, post-outcome variables, duplicate entities, and temporal
contamination. Use an appropriate train/validation/test split and a reproducible seed.
Create a preprocessing pipeline, compare against a simple baseline, report metrics
appropriate to the target and class balance, inspect errors by meaningful subgroups,
and explain feature importance cautiously. Show all code and every major choice.SQL
Write a read-only SELECT query for [question]. State the SQL dialect, table assumptions,
join logic, filters, aggregation level, NULL treatment, and edge cases. Include comments
and a validation query for row counts, duplicate keys, and known control totals. Do not
use INSERT, UPDATE, DELETE, DROP, ALTER, MERGE, or CREATE.Documentation and reporting
Turn this verified analysis into a report with: question, source and date range,
data-quality findings, method, assumptions, results with denominators, limitations,
recommendation, and reproducibility details. Separate observed facts from interpretation
and do not use causal language unless the design supports causation.Debugging
Explain this error and provide the smallest safe fix. Show the corrected code, explain
why it works, identify any change in output, and suggest a test that prevents the error
from returning. Do not rewrite unrelated parts of the pipeline.Python, pandas, and SQL: what to request
You do not need to write every line yourself, but “no coding required” is an unsafe description of serious analysis. You need enough Python, SQL, and statistical knowledge to inspect what was generated. Ask for code that is readable, modular, and reproducible rather than a single opaque answer.
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Useful instruction fragments include:
Show your work in executable Python.Use pandas and scikit-learn unless another library is necessary.State the denominator for every percentage.Separate facts, calculations, assumptions, and interpretations.Ask a clarifying question instead of guessing.Give a skeptical review of your own analysis.Report row counts before and after every transformation.
For large or live data, ask ChatGPT to draft SQL, review the query yourself, run it against a read-only or development database, and compare row counts and control totals. The data-analysis Python environment cannot independently make arbitrary external web requests or API calls; external data must be uploaded or provided through an available supported connection.
Choosing a statistical method
| Question | Possible method | Check first |
|---|---|---|
| Compare two independent group means | t-test or nonparametric alternative | Independence, distributions, variance, and sample size |
| Compare proportions | Proportion test or chi-square test | Correct denominator and small-count conditions |
| Compare more than two groups | ANOVA or suitable alternative | Independence, variance, and multiple-comparison control |
| Measure numeric association | Pearson or Spearman correlation | Outliers, nonlinear relationships, and the fact that correlation is not causation |
| Predict a continuous outcome | Linear or tree-based regression | Leakage, residual behavior, and generalization |
| Predict a binary outcome | Logistic regression or classification | Class balance, threshold, calibration, and business cost |
| Analyze repeated observations | Mixed-effects or panel methods | Dependence between observations |
| Analyze time-dependent data | Time-series methods or rolling validation | Future-data leakage and temporal ordering |
Ask why a method is appropriate, what assumptions it makes, what happens when those assumptions fail, whether multiple comparisons were made, whether effect size matters more than statistical significance, and whether the sample represents the population of interest. A test that runs successfully can still be methodologically wrong.
Machine-learning safeguards
- Target leakage and post-outcome variables.
- The same entity appearing in both training and test data.
- Temporal leakage from using future information.
- Preprocessing fitted on the full dataset before splitting.
- Class imbalance and metrics that hide poor minority-class performance.
- Missingness patterns that differ between splits.
- Tuning repeatedly against the test set.
- Overfitting caused by trying many variations.
- Metrics that do not reflect the cost of false positives and false negatives.
Audit this modeling workflow as a skeptical reviewer. Look specifically for leakage,
invalid splitting, target imbalance, preprocessing errors, unjustified hyperparameters,
misleading metrics, subgroup failures, and unsupported causal language. Rank findings
by severity and show the code or evidence for each.How to verify ChatGPT’s analysis
- Completeness: Did it load every file and sheet? Did it inspect the entire dataset, or sample or truncate it?
- Row accounting: Are row counts shown before and after every filter, join, and transformation?
- Definitions: Are date ranges, time zones, units, populations, and denominators explicit?
- Data quality: Were missing values, duplicates, impossible values, and outliers investigated rather than silently removed?
- Joins: Were key uniqueness, unmatched rows, and one-to-many effects checked?
- Calculations: Can you recompute important numbers independently from the raw data?
- Charts: Is the aggregation correct? Are axes, scales, filters, categories, and incomplete periods visible?
- Statistics: Does the method match the outcome and sampling design? Were assumptions, effect sizes, uncertainty, and multiple comparisons considered?
- Models: Is there leakage? Was the split appropriate? Does the baseline and metric reflect the real use case?
- Interpretation: Does the language describe association rather than causation where appropriate?
- Reproduction: Can another analyst rerun the code using the same data version, prompt, environment, and assumptions?
Recalculate this result from the raw data using an independent method. Show the
numerator, denominator, filters, grouping logic, and code. Compare both calculations
and explain every discrepancy.Common failures and recovery prompts
The analysis missed important rows
Complex workbooks, multiple tables, scanned PDFs, image-based tables, oversized files, unsupported formatting, sampling, or an earlier filter can all cause this.
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Stop the analysis. Verify whether the complete file was inspected. Report every file
and sheet loaded, row count before and after each transformation, any sampling or
truncation, failed parsing, excluded rows, and the exact completeness-check code.The number sounds right but is wrong
Ask for an independent recalculation, visible filters, and numerator and denominator. Do not accept a plausible narrative as validation.
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The statistical test is inappropriate
Act as a statistical reviewer. Assess whether this test matches the outcome type,
sampling design, independence, distribution, sample size, variance structure, and
multiple-comparison situation. Recommend alternatives and explain the consequences.The model score is suspiciously high
Check leakage, duplicate entities, temporal contamination, preprocessing before splitting, test-set tuning, class imbalance, and whether the test sample is representative.
The chart is misleading
Check for a truncated y-axis, wrong aggregation, hidden filters, missing categories, unequal denominators, inappropriate colors, incomplete time periods, and dual axes that imply an unsupported relationship.
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The SQL might change production data
Use a read-only connection or development database and require a SELECT-only query. Validate its row count, duplicate keys, NULL handling, and totals before using the result.
Privacy, governance, and account differences
Do not treat “ChatGPT” as one privacy setting. Consumer accounts, Business and Enterprise workspaces, education accounts, the OpenAI API, third-party connected apps, and local notebook or IDE workflows have different controls, retention arrangements, permissions, and risks.
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OpenAI states that business customer content in ChatGPT Business and Enterprise is not used to train models by default. It also says API data is not used to train or improve models unless the customer explicitly opts in, subject to applicable data-use and abuse-monitoring policies. These statements do not make every account or connector automatically private. Review your organization’s policy, OpenAI’s privacy information, the API data-use policy, and each connected app’s terms and permissions.
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Before uploading data:
- Remove direct identifiers where possible.
- Use an approved workspace for company information.
- Check retention, training, and data-control settings.
- Review connector permissions and what data the app can access.
- Do not upload regulated, confidential, proprietary, or personal data without authorization.
- Use synthetic or redacted data for tutorials and experimentation.
File limits and operational constraints
OpenAI’s file-upload documentation lists a 512 MB maximum per uploaded file. That is a documented hard limit, not a promise that every 512 MB file will analyze successfully. Practical limits, supported formats, quotas, project limits, and capabilities vary by plan, model, workspace, account, and date. The same documentation gives plan-specific examples such as up to 20 files per project for Plus and up to 40 for Pro, Team, Education, and Business; verify the live documentation because these figures can change.
Common constraints include:
- A successful upload does not guarantee complete analysis.
- Scanned PDFs and image-based tables may not yield reliable exact values.
- Complex layouts, unrelated tables, and image-heavy workbooks can confuse parsing.
- Large files may need to be split into logical, validated parts.
- A generated chart may be static rather than interactive.
- A connector may be disabled by workspace policy.
- A required Python package may be unavailable.
- Upload quotas and rate limits vary.
- The notebook environment does not automatically fetch arbitrary live web or API data.
When another tool is better
| Need | Better fit | Why |
|---|---|---|
| Small-file conversational exploration | ChatGPT | Upload data, iterate in natural language, generate charts and code. |
| Code execution and document-heavy analysis | Claude | A credible alternative with file creation, code execution, charts, and reports; see its official capability documentation. |
| Google-centered files and collaboration | Gemini | Useful for teams already working in Google Drive, Docs, and Sheets; availability varies by region and plan. Check Google’s current plans. |
| Inline coding in an IDE | GitHub Copilot | Better suited to repository-aware completion, debugging, and code explanation than standalone file analysis. See GitHub’s product page. |
| Repeatable or production analysis | Jupyter, VS Code, hosted notebooks | Provide version control, tests, package management, environment control, and scheduled execution. |
| Large, governed, continuously updated data | Warehouse and BI tools | Keep access centralized and support scale, auditing, scheduled refreshes, dashboards, and role-based permissions. |
These tools are not universal winners. A common professional setup is conversational AI for planning and code scaffolding, a controlled notebook or IDE for execution, and a warehouse or BI platform for governed data access and reporting.
Printable quick reference
Always ask for
- A data audit before conclusions.
- Executable Python or SQL.
- Row counts before and after transformations.
- Visible filters, denominators, units, and date ranges.
- Assumptions, limitations, and uncertainty.
- Independent recalculation of important results.
- Leakage checks before trusting model metrics.
Red flags
- “I analyzed everything” without file and row counts.
- Numbers without a denominator or filter definition.
- Automatic deletion or imputation without approval.
- Causal claims based only on observational data.
- Near-perfect model metrics without leakage analysis.
- A chart with hidden filters, incomplete periods, or distorted axes.
- SQL containing write operations or unreviewed production access.
Never upload confidential or regulated data without authorization. Always ask for code, preserve the raw source, and verify conclusions independently.
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