Basin pipelines
Analytics workflows with Basin Pipelines and SQL.
Analytics workflows with Basin Pipelines and SQL.
Postgres performance guidance across query performance, connections, schema design and security.
A safe routine to profile, clean and validate a dataset, with a log of every change.
Cleans messy datasets safely: finds problems, applies reversible fixes and logs every change. Use before analysis.
Answers questions by writing and running read-only SQL, explaining the results. Use for ad-hoc data questions.
Builds a report from data and notes: charts, key numbers and a written summary. Use for recurring reports.
Audits spreadsheets for formula errors, hardcoded values, broken references and inconsistencies. Use before sharing models.
Writes and explains spreadsheet formulas for Excel or Google Sheets from a plain description, with examples. Use when stuck on a formula.
Specifies a dashboard: audience, questions it answers, metrics, charts and filters. Use before building reports.
Reads A/B test results correctly: sample size, significance, practical effect and a clear decision. Use after an experiment ends.
Analyses survey responses: cleaning, summarising closed questions, theming open answers and reporting what matters. Use after a survey closes.
Defines business metrics precisely: formula, source, inclusions, exclusions and owner, so everyone means the same thing. Use for a metrics glossary.
Builds a data dictionary from tables or files: each field, type, meaning, example and constraints. Use to document a database or dataset.
Build Gradio apps and demos.
Fine-tune language models with Hugging Face tooling.
Create and manage Spaces to host demos.
Find and read research papers on the Hub.
Run machine learning models in JavaScript with Transformers.js.
Train models with reinforcement learning and preference methods using TRL.
Use the Hugging Face command line to work with models, datasets and Spaces.
Find, load and work with datasets from the Hugging Face Hub.
Analyses datasets and answers questions with numbers, charts and caveats. Use for CSV, spreadsheets and SQL results.
Writes and explains SQL queries from a plain-language question and a table description. Use when asked for a query or to fix…
Inspects a messy CSV and proposes cleaning steps: types, missing values, duplicates and inconsistent labels. Use before analysing a dataset.
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