1. Exploratory Data Analysis Summary
Starting a new dataset without a structured plan is the fastest way to waste an afternoon chasing dead ends. A good EDA begins with defining the questions worth asking, identifying which visualizations will surface patterns efficiently, and flagging data quality issues before they corrupt downstream analysis. This prompt generates a structured EDA report outline from a plain-English description of your dataset — giving you a roadmap before you write a single line of code.
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I'm starting an exploratory data analysis on the following dataset. Generate a structured EDA report outline that includes: (1) the 5–7 most important analytical questions to investigate given the dataset's domain, (2) recommended visualizations for each question (chart type, axes, and what pattern to look for), (3) a data quality checklist covering nulls, duplicates, outliers, type mismatches, and cardinality issues to check for each key column, and (4) any domain-specific pitfalls or biases to watch out for. Dataset description: [describe the dataset — table name, row-level granularity, key columns and their types, business domain, and the decision this analysis is meant to inform]
The more context you give in the dataset description — especially the business decision the analysis informs — the more targeted the questions the AI surfaces. A generic “describe my dataset” prompt produces generic questions; “this analysis informs whether we should expand into a new market” produces questions worth answering.
2. SQL Query Explainer & Optimizer
Complex SQL queries — especially ones inherited from someone else, or written under deadline pressure — are notoriously hard to audit. Understanding what a query actually does, spotting where it’s going to be slow on large tables, and knowing which rewrites will help are three distinct skills that take years to develop. This prompt handles all three: it explains a query in plain English, flags the performance risks, and suggests concrete optimizations with the reasoning behind each one.
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Analyze the following SQL query and provide: (1) a plain-English explanation of what the query does, step by step, including what each JOIN, subquery, and aggregation is computing; (2) potential performance issues — flag any full table scans, missing index opportunities, N+1 patterns, expensive subqueries that could be CTEs, or operations that won't scale to large row counts; (3) specific optimization suggestions with the rewritten SQL for each one and an explanation of why it improves performance. Assume the target database is [PostgreSQL / BigQuery / Snowflake — specify yours]. Table row counts where known: [add if relevant]. SQL query: [paste your query here]
Specifying the database engine matters — optimization advice for BigQuery (columnar, no traditional indexes) differs significantly from PostgreSQL. If you’re working with a particularly large table, mention the row count; it helps the AI prioritize which performance flags are worth acting on versus theoretical.
3. Data Insights to Stakeholder Narrative
The hardest part of data analysis is often the last mile — translating a set of numbers, trends, and anomalies into a narrative that a business audience can act on without needing a statistics degree. Stakeholders don’t want to know that revenue was up 12% MoM; they want to know why, what it means, and what to do about it. This prompt converts raw findings into a clear, jargon-free summary structured for a business audience.
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Convert the following raw data analysis findings into a clear, non-technical stakeholder narrative. The output should: (1) open with a one-sentence "so what" that captures the most important insight, (2) explain the key trends and patterns in plain business language without statistical jargon, (3) call out any anomalies or surprises and offer a likely explanation, (4) end with 2–3 concrete, actionable recommendations tied directly to the findings. Audience: [describe the audience — e.g. "VP of Marketing with no data background" or "C-suite monthly business review"] Analysis findings: [paste your numbers, trends, tables, or bullet-point findings here]
The “so what” opening instruction is what separates useful stakeholder summaries from data dumps with paragraph headers. Stakeholders read the first sentence and decide whether to keep reading — lead with the insight, not the methodology.
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4. Python/Pandas Data Cleaning Script Generator
Data cleaning is the part of data science that takes the longest and gets the least recognition — because when it works, nobody notices. Writing a cleaning script from scratch for every new dataset means reinventing the same patterns: handling nulls, deduplicating, casting types, capping outliers. This prompt generates a pandas cleaning script skeleton with inline comments explaining each step, based on your description of what’s wrong with the data.
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Generate a Python pandas data cleaning script skeleton for the following dataset issues. For each issue, write the pandas code to address it with an inline comment explaining what it does and why. Include: (1) null handling (drop vs. fill — specify the strategy for each column), (2) duplicate removal (specify dedup keys), (3) type casting and format standardization, (4) outlier detection and capping using the IQR method for numeric columns, (5) a final validation block that prints a summary of remaining nulls, dtypes, row count before and after, and any remaining anomalies. DataFrame name: df Dataset issues: [describe each issue — e.g. "column 'signup_date' is stored as string in MM/DD/YYYY format, needs to be datetime", "column 'revenue' has ~8% nulls — fill with 0 for free-tier users and median for paid users", "email column has duplicates — keep the row with the most recent 'last_login' date", "age column has values above 120 and below 0 — cap at [0, 100]"]
The more specific your issue descriptions, the more immediately usable the output — vague inputs like “there are nulls” produce generic handlers, while specific inputs like “fill nulls in the revenue column with 0 for free-tier users” produce business-logic-aware code. Treat the output as a scaffold: review the logic, run it on a sample before applying to the full dataset.
5. A/B Test Results Interpretation
A/B test results are easy to misread — especially under pressure from a product team that already has a preferred outcome. Statistical significance doesn’t mean practical significance. Early peeks inflate false-positive rates. Segment-level winners can hide population-level losses. This prompt takes your raw test metrics and produces a statistically grounded interpretation that flags the common mistakes before they become bad product decisions, along with a clear business recommendation.
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Interpret the following A/B test results with statistical rigor and provide a clear business recommendation. Your analysis should cover: (1) statistical significance — calculate or confirm the p-value and confidence interval for the primary metric, and explain what they mean in plain English; (2) practical significance — assess whether the observed lift is large enough to matter for the business given the scale; (3) common pitfalls check — flag any concerns about peeking, underpowering, novelty effect, or segment imbalance based on the data provided; (4) a clear recommendation: ship, do not ship, or run a follow-up test — with the specific reasoning. Test details: - Hypothesis: [what change was tested and what outcome was expected] - Primary metric: [e.g. "checkout conversion rate"] - Control: [sample size] users, [conversions or metric value] - Variant: [sample size] users, [conversions or metric value] - Test duration: [X days] - Any secondary metrics or guardrail metrics: [list if available]
Include secondary metrics and guardrail metrics if you have them — a variant that improves checkout conversion while hurting average order value is not a clean win, and the AI will flag the tension if you give it the data. The recommendation framing (“ship, do not ship, or run a follow-up test”) forces the output to be actionable rather than a statistical summary that leaves the decision to whoever reads it last.