'data processing' 검색 결과

Develop workflows for satellite imagery and geographic data, using guidance and code examples to calculate vegetation indicators, classify land cover, analyze terrain, and find road routes. Covers GIS, software for working with location-based information, and spatial machine learning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracking vegetation changes in satellite imagery
• Classifying land cover with machine learning
• Aggregating location data by region

Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Best practices for polars data processing with dataframely. Covers definitions of Schema and Collection, usage of .validate() and .filter(), type hints, and testing. Use when writing or modifying code involving dataframely or polars data frames.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GDPR-compliant Data Processing Agreement drafting per Article 28(3). Covers all 8 mandatory provisions including subject matter, duration, nature and purpose, data types, categories of data subjects, controller and processor obligations, and sub-processor cascade requirements.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides the creation and review of data processing agreements under GDPR Article 28(3), covering all eight mandatory clauses. References the 2021 Standard Contractual Clauses and provides a compliance checklist for processor contracts. Activate when onboarding processors, reviewing DPAs, or auditing processor compliance. Keywords: DPA, data processing agreement, Article 28, processor, mandatory clauses, standard contractual clauses.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Governs employee health data processing for fitness-for-work assessments, occupational health surveillance, COVID testing legacy programmes, and absence management. Applies Art. 9(2)(b) employment obligations and Art. 9(2)(h) health professional exceptions. Covers data minimisation, occupational health provider relationships, and return-to-work procedures. Keywords: health data, Art. 9, occupational health, fitness-for-work, special category, employment, sickness absence.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Process vehicle insurance Excel data using Pandas - file handling, data cleaning, merging, validation. Use when processing Excel/CSV files, handling data imports, implementing business rules (negative premiums, zero commissions), debugging data pipelines, or optimizing Pandas performance. Keywords: data_processor.py, Excel, CSV, Pandas, merge, deduplication, date normalization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Converts documents into clean, chunked datasets suitable for embeddings and vector search. Produces chunked JSONL files with metadata, deduplication logic, and quality checks. Use when preparing "training data", "vector datasets", "document processing", or "embedding data".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze large scientific tables with Vaex, including datasets that exceed available memory. Build calculated columns, filter rows, compute statistics, visualize aggregated data, and convert files while identifying operations that may still require substantial RAM.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Computing statistics on tables larger than available memory
• Creating calculated features without storing full columns
• Visualizing large datasets with aggregated grids

Process molecular datasets in Python through a simpler interface to RDKit, a chemistry toolkit. Convert and standardize structures, calculate molecular properties, compare and cluster compounds, generate 3D conformations, and visualize results for drug discovery workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load and standardize molecular datasets
• Calculate molecular properties and similarities
• Cluster compounds and select diverse subsets

Guide cross-platform app development with Avalonia UI and Zafiro using consistent architecture and coding conventions. Keep screen logic separate from UI dependencies, handle errors explicitly, and reuse existing helpers with ReactiveUI and DynamicData.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Align design conventions for an Avalonia UI app
• Keep ViewModels independent of Avalonia
• Review error handling with Result types

Export data from Google Sheets as CSV for local backups or further processing. The recipe covers checking spreadsheet details, exporting through Google Drive, and reading a specified sheet range directly in CSV format.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Keeping a local CSV backup of spreadsheet data
• Exporting spreadsheet data for further processing
• Reading a selected sheet range as CSV

Build table data processing with TanStack Table v9 while keeping data and column definitions stable. The skill guides type inference, which determines types automatically, row identity, optional features, and checks for missing APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up the foundation for table data processing
• Troubleshoot column type inference
• Investigate missing APIs or optional features

Prepare an internal Privacy Impact Assessment, a review of how a feature uses personal data. Check whether assessment is needed, examine legal grounds and policy commitments, and document specific risks, required actions, and owners in the team’s format.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing personal data use in a new feature
• Checking whether a privacy assessment is needed
• Comparing processing plans with privacy policy commitments

Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learn how Pipeline, PCollection, and PTransform fit together
• Understand grouping, aggregation, merging, and splitting
• Study time windows, output timing, and late data

Develop and verify changes to Apache Beam's Python SDK. Set up a virtual environment, run tests, build installable packages or containers, and run data processing pipelines with the modified SDK.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up an Apache Beam Python SDK development environment
• Run unit and integration tests for SDK changes
• Build SDK packages and container images

Process spreadsheets with Python after checking source files, required sheets and columns, and external data access. The workflow includes backup source checks and clear failure reports when the required data cannot be reached.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check required files and columns before processing Excel
• Verify external data access before updating a spreadsheet
• Check backup data when the primary source is unavailable

Validate spreadsheet sources before running Python scripts to transform data, update multiple sheets, or apply formatting. Check required data first, try backup sources when needed, and report errors clearly if processing cannot proceed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking required columns before processing Excel files
• Transforming data and saving a new workbook
• Updating formatting across multiple sheets