Topic
Turning raw data or a messy set of facts into a clear conclusion is where most analysis work gets stuck. This page collects prompts built for that exact step: breaking down a dataset or report into key patterns, comparing options against a set of criteria, and structuring findings into a conclusion someone can actually act on. They're useful for analysts, product managers, and anyone who needs to make sense of numbers or unstructured information quickly. Every prompt is free and reusable — plug in your own data or context using the marked {{variable}} fields and get a structured, decision-ready answer instead of a generic summary.
62 prompts · page 2 of 7
Audits a dataset's health before analysis starts, using its columns, row count, source, time range, and a sample of rows to spot missing values, type mismatches, likely outliers, duplicate rows, and inconsistent categorical entries. Returns a 1-10 quality score with reasoning and a concrete cleaning step for every issue found, making it a natural first pass before writing any cleaning script.
Builds a repeatable validation suite for a dataset you'll keep receiving, based on its description, columns, and stated business rules. Produces per-column checks, cross-column business-logic rules, and statistical sanity checks, then wraps them into a Python script that reports failure counts per rule, sample bad rows, an overall quality score, and a priority order for fixes.
Documents a dataset column by column for a team about to receive it, using its name, source system, refresh frequency, and column list as inputs. - Per column: type, business meaning, source, valid values or range, null handling - Also covers: dependencies, known quality issues, and 3-5 example values - Output: a clean markdown table plus a JSON schema ready for automated validation
Walks from a target variable, candidate predictors, dataset size, and analysis purpose through a full regression build: exploratory relationships, feature selection, model choice with reasoning, assumption checks, and performance metrics like R², RMSE, or AUC depending on the target type. Delivers plain-language coefficient interpretation, residual analysis, and sample predictions with confidence intervals, plus complete scikit-learn or statsmodels code.
Forecasts a chosen metric over a set horizon from historical data, granularity, known patterns, and external factors, with a justified choice of model — moving average, exponential smoothing, ARIMA/SARIMA, or Prophet. Delivers 80% and 95% confidence intervals, a trend/seasonality/residual decomposition, holdout accuracy metrics like MAPE and RMSE, and optimistic, baseline, and pessimistic scenarios, so the forecast carries honest uncertainty bounds instead of a single line.
Builds three parallel plans for a strategic decision that's too uncertain for a single-point forecast. - Inputs: the decision, timeframe, key uncertainties, and affected stakeholders - Per scenario (best/likely/worst): assumptions, probability estimate, business impact - Also covers: leading indicators to watch and preparation steps for each case - Output: a monitorable watchlist, not just three narratives
Calculates TAM, SAM, and SOM for a product, geography, and customer segment, showing the methodology, data sources, and explicit assumptions behind each figure rather than just handing over a number. Adds growth projections and a suggested penetration strategy, making it useful for pitch decks and planning docs where reviewers will challenge the assumptions before the market-size figures themselves.
Runs a systematic PESTLE scan of an industry and region over a chosen timeframe, covering political, economic, social, technological, legal, and environmental factors. For each factor, reports the current state, trends, business impact, opportunities and threats, and strategic implications — a macro-level companion to competitor-focused analysis for teams building a full strategy document.
Handles the type-conversion and encoding grunt work needed before a dataset is ready for analysis or modeling. - Input: a list of columns with their current and target types - Converts types with edge-case handling (stray text in numeric columns, mixed-type columns) - Encodes categoricals appropriately: ordinal, one-hot/target, or frequency encoding for high-cardinality fields - Output: Python code plus a before/after conversion report flagging failed conversions