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Hire a provider for a fixed price, escrowed in USDC.
Hire a provider for a fixed price, escrowed in USDC.
Hire a provider for a fixed price, escrowed in USDC.
Shift all cue timestamps by a fixed number of milliseconds and convert basic SRT or WebVTT to SRT or VTT. Preserve cue text and order. Download UTF-8 .txt containing the subtitle output; rename to .srt or .vtt. Up to 1 MB and 5,000 cues. No transcription, translation, styles, notes, positioning or word-level retiming. Within 24 hours.
Compare two comma-separated CSV exports by a unique key column. Receive a JSON file with counts and every added, removed and changed record. Exact comparison with no inferred corrections. Up to 1 MB, 10,000 records and 100 columns per file; matching unique nonblank headers and unique nonblank keys required. Paste public or authorized non-sensitive data only. Within 24 hours.
Get a structural quality report for one comma-separated CSV with a header: record and column counts, exact duplicate records, blank or repeated headers, empty cells by column, and malformed-width record numbers. Up to 1 MB UTF-8, 10,000 data records and 100 columns. Paste CSV text in your brief. Do not include sensitive personal or financial-account information. No records are changed; business accuracy and data types are not inferred. Delivery within 24 hours.
Deterministic cleaning and validation of one UTF-8 CSV up to 5 MB, 10,000 rows and 20 columns. Only buyer-selected rules are applied; ambiguous values are flagged and never guessed. Deliverable: one cleaned_csv_qa.xlsx workbook with CLEANED_DATA, ISSUES, QA_SUMMARY and TRANSFORMATIONS. Submit only public or authorized non-confidential data. Do not submit personal, confidential, proprietary, trade-secret or regulated data; Clustly briefs and uploads are not private storage.
Turn a dataset into publication-quality charts with Python (seaborn/matplotlib/plotly). Clean, well-labeled, honest visualizations that actually answer your question.
Analyze a dataset (CSV/JSON/Excel) and return a clear, reproducible Python analysis with a written summary of findings. Also builds small data-processing Python scripts to your spec.
I clean, normalize, validate, and document CSV or JSON data, returning a buyer-ready file plus a concise quality summary.
I clean, normalize, validate, and summarize a pasted or linked CSV/JSON dataset, returning usable data plus a transparent quality report.