Data exploration
Exploratory analysis: profiling a dataset, finding patterns and anomalies, analyst SQL.
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- Reconcile two datasets
Reconciles two datasets that should agree, such as bank versus ledger or CRM versus billing, by matching records, listing mismatches and explaining likely causes. Use for month-end checks.
- Write a dataframe transformation
Writes pandas or polars code for a described transformation with built-in checks on row counts, nulls, key uniqueness and join cardinality. Use when reshaping, joining or aggregating data.
- Analyse contact centre performance data
Analyses contact centre data - volume by interval, handle time, abandonment, service level, repeat contacts and contact reasons - and recommends staffing alignment and process fixes.
- Analyse whether promotions paid off
Analyses promotion data for incremental lift, cannibalisation, pull-forward and margin impact against a fair baseline, and says which to repeat. Use after a sale, coupon or discount campaign.
- Analyse nonprofit donor data
Analyses nonprofit donor data for retention, lapsed donors, gift size distribution and upgrade potential, with segment-level actions. Use for annual fundraising planning or a donor file review.
- Analyse an employee engagement survey
Analyses an employee engagement survey with group scores under minimum-group-size privacy rules, eNPS, comment themes and three priorities to act on. Use after an engagement or pulse survey closes.
- Analyse energy usage data
Analyses household or building energy data for baseload, daily and seasonal patterns, anomalies and the savings worth chasing, ranked by money. Use with smart meter exports or a year of bills.
- Analyse farm or orchard yield records
Analyses farm or orchard yield records by field, variety, input and season, separating weather and field effects from management, and designs fair on-farm trials for next season.
- Analyse a hiring funnel
Analyses recruiting pipeline data for stage conversion, time to hire, source quality and drop-off, with fair comparisons by role. Use for a recruiting review or when roles take too long to fill.
- Analyse inventory and stock data
Analyses inventory and sales data for stock turns, days of cover, dead and slow stock and stockout risk, with a ranked action list. Use for a stock review, reorder planning or freeing up cash.
- Analyse location data
Analyses location data for stores, customers or deliveries to find catchments, density and distance patterns, with the method, code and mapping guidance. Use for site, coverage or delivery questions.
- Compare marketing attribution models
Compares last-click, first-click, linear, position-based and data-driven attribution on supplied channel data and explains what each implies for budget. Use before moving marketing spend.
- Analyse process cycle times
Analyses process timestamps for lead time, wait versus work time, bottleneck steps and variability, from tickets, orders or case records. Use when a process feels slow.
- Analyse comparable property sales
Analyses comparable property sales to estimate a price range for a home, with adjustments for size, condition and location stated openly. Use before buying, selling or challenging a valuation.
- Analyse sales performance
Analyses sales data by product, customer, region and time to find what drives revenue, seasonality, best and worst performers, and the actions worth taking. Use for a sales performance review.
- Analyse school attendance data
Analyses school attendance data for patterns by group, day, term and reason, identifies pupils who may need support without labelling them, and suggests interventions to test.
- Analyse sports performance data
Analyses player or team performance data for trends, consistency, strengths and weaknesses, with fair rate-based comparisons and small-sample warnings. Use for scouting or coaching reviews.
- Analyse survey results
Analyses quantitative survey responses with cleaning, tabulation, cross-tabs and optional weighting, and states the caveats about sample and response bias. Use before reporting survey numbers.
- Analyse website analytics
Analyses website analytics (GA4 or similar) for traffic sources, landing pages, engagement and conversion, flags tracking problems first and gives prioritised actions. Use as a marketer or site owner.
- Analyse workforce data
Analyses HR data for headcount movement, attrition, tenure and representation under minimum-group-size privacy rules, and flags patterns worth a closer look. Use for a workforce review or board pack.
- Anonymise a dataset before sharing
Plans anonymisation or pseudonymisation of a dataset before sharing, classifying identifiers, choosing techniques and assessing re-identification and residual risk. Use before data leaves your team.
- Answer a question with SQL
Turns a business question and a schema into an analytical SQL query, states the assumptions behind it and explains how to read the result. Use when you know the question but not the query.
- Build a cohort retention analysis
Builds a cohort retention analysis from event data (cohort definition, query or code, the retention triangle) and explains how to read it. Use to see whether newer customers stick around better.
- Classify text records
Classifies free-text records such as tickets, feedback or expenses into a given set of categories, with a confidence level and an explicit Other bucket, and returns a table.
- Clean a raw survey export with a decision log
Cleans a raw survey export in the project files with reproducible code, checking speeders, straight-lining, duplicates and attention checks, recoding scales and logging every decision.
- Design a clean data collection form
Designs a form or sheet that collects data cleanly at the source, with field types, validation, IDs, required fields and a test entry. Use before launching a form whose answers you will analyse.
- Data analyst
Acts as a data analyst who starts from the decision, sanity-checks data before trusting it and states uncertainty plainly. Use as a standing analyst persona or subagent for data questions.
- Data journalist
Data journalist who checks where a dataset came from before trusting it, distrusts round numbers, finds the human story in the figures and explains methods and limits plainly to readers.
- Data scientist
Acts as a data scientist who frames the decision first, uses the simplest valid method, validates out of sample and communicates uncertainty plainly. Use for modelling, prediction and experiment work.
- Clean a dataset with a scripted, auditable pipeline
Cleans a dataset with a repeatable script in gated steps, profiling, proposing rules, applying and validating them, and exporting with an auditable cleaning log. Use for data that will be reused.
- Decompose a revenue change
Breaks a revenue or sales change into price, volume and mix effects, and into new, lost and retained customers, with the arithmetic shown and reconciled. Use to explain why revenue moved.
- Decompose a time series into trend and seasonality
Decomposes a time series into trend, seasonality and residual, explains each in plain words and shows what a fair year-on-year comparison looks like. Use before reading too much into a monthly change.
- Deduplicate messy records
Plans and writes matching logic to deduplicate people, companies or products across messy records, with normalisation, blocking, fuzzy thresholds, merge rules and a review queue. Use for CRM cleanup.
- Detect anomalies in data
Finds anomalies in a metric or dataset with methods that fit its shape (thresholds, seasonality, robust z-scores), ranks them, and separates data errors from real events. Use when monitoring data.
- Practise pandas in a simulated session
Simulates a pandas session on a dataset you describe, printing DataFrame output, aggregations and errors faithfully so analysts practise cleaning and reshaping without setup.
- Practise R in a simulated console
Simulates an R console with a small synthetic data frame, printing results, summaries, warnings and errors as R would, for learners practising base R or tidyverse verbs.
- Explore a dataset
Runs a first-pass exploratory analysis of a dataset (column profiles, missingness, distributions, outliers) and lists the questions worth asking next. Use when you get new data.
- Extract fields from documents into a table
Extracts named fields such as dates, amounts, names and IDs from emails, invoices or letters into a table, leaving blanks where a value is absent rather than guessing. Use to turn paperwork into data.
- Find churn drivers
Finds which behaviours and attributes predict churn in customer data, simple comparisons first and a model only if justified, with an action and a test per driver. Use at subscription businesses.
- Find the story in a public dataset
Finds the story in a public dataset by checking provenance and definitions, computing rates rather than raw counts, and testing the headline before it is published. Use for data journalism or reports.
- Solve a mystery by querying a database
Runs an original detective case solved by querying a fictional database of witnesses, access logs and transactions, answering every query consistently until the player names the culprit with evidence.
- Review analytical SQL
Reviews an analytical SQL query for logic errors that give wrong numbers, such as join fan-out, misplaced filters, NULLs, double counting and date or time-zone boundaries. Use before sharing results.
- Run an analysis loop together, one query at a time
Works through a business question in a loop where the assistant proposes the next query or chart, the user runs it and pastes the result, and the assistant interprets it and picks the next step.
- Run a market basket analysis
Runs market basket analysis on transactions to find products bought together, explains support, confidence and lift, and suggests bundles or placement to test. Use for retail and e-commerce.
- Run a Pareto (80/20) analysis
Runs a Pareto analysis on products, customers, defects or causes, with the cumulative table, chart instructions and which vital few to act on. Use to find where effort will pay off most.
- Segment customers
Proposes and builds a customer segmentation (RFM, rules or clustering) with interpretable segment profiles and a suggested action for each. Use to target retention, pricing or marketing work.
- Translate a spreadsheet workflow to pandas
Translates a spreadsheet workflow of filters, lookups, pivots and formulas into a pandas script that produces the same outputs, with checks that totals match. Use to automate a manual routine.
- Write an analysis plan
Writes an analysis plan before touching data, covering the decision, questions, metrics, data, method, comparisons, pitfalls and the result that would change the decision. Use when scoping a request.
- Write a data request brief
Turns a vague stakeholder ask into a clear data request with the decision, exact metric definitions, filters, time range, format and deadline, plus open questions. Use when a data ask arrives vague.
Not: building pipelines or tuning queries (data).