Results for “data”

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 don't fit in memory.

Validates NVIDIA TAO DAFT datasets for structure, schema, and cross-reference errors using the `tao-daft validate` CLI tool.

Create clear, effective data visualizations with chart selection, color theory, and annotation best practices using the inference.sh CLI.

Analiza datasets con Pandas y NumPy: explora distribuciones, correlaciones y patrones, y aplica tests de hipótesis para extraer conocimiento no obvio.

Optimize SQL queries, design database schemas, and tune performance across cloud-native and hybrid OLTP/OLAP environments.

Write, review, debug, test, or optimize pandas Series, DataFrame, Index, groupby, merge, reshape, dtype, missing-value, and time-series code.

DataFrame operations with pandas and polars — groupby, joins, reshaping, performance. Use when manipulating tabular data, choosing between pandas and polars, optimizing DataFrame code, or translating between the two libraries.

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

Enables the Google Analytics Data API, authenticates via gcloud, and creates customized reports using the v1beta client library.

Load sharded datasets (npy, Parquet, HDF5, raw binary) into distributed cuPyNumeric arrays using manual partitioning and Legate task launches.

Generate production-ready Python code using the Dataverse SDK with error handling, retry logic, OData optimization, and logging.

Design reliable, scalable, and maintainable data systems by applying principles from storage engines, replication, partitioning, transactions, and consistency models.

Write, optimize, and debug Apache Spark jobs for high-performance distributed data processing, ETL pipelines, and big data workloads.

Process and analyze tabular datasets larger than RAM using lazy, out-of-core DataFrames, with fast aggregations, visualization, and machine learning integration.

Extracts structured JSON data from complex multi-page websites using an AI agent that navigates pages and returns results matching a schema.

Create data-driven charts with Vega-Lite and Vega, covering bar, line, scatter, heatmap, area, radar, and word cloud visualizations from structured data arrays.

Creating Excel spreadsheets programmatically with openpyxl — worksheets, data, formulas, charts, formatting, and named ranges. Use when generating financial models, data reports, dashboards, or any structured spreadsheet from data or templates.

Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.

Use when working in Go and you need DataList/DataTable-style data wrangling, quick previews, parallel transforms, file I/O (CSV/Excel/Parquet), Excel-like column formulas (CCL), or charts; also use when data analysis is requested without a specified stack, defaulting to Go + Insyra over Python + pandas.

Drive data strategy with governance frameworks, analytics platforms, AI/ML initiatives, and privacy compliance.

Data Analyst IA — Expert en analyse de données (SQL, BI dashboards, Metabase, Superset, reporting, KPIs)

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

Extract public web data, search engine results, and structured data from platforms like Google, Amazon, and Zillow using HasData APIs.

Provides guidance for Oracle Database administration, SQL and PL/SQL development, performance tuning, security, ORDS, SQLcl, migrations, frameworks, and agent-safe database workflows.

Recover deleted records, analyze freelist pages, WAL files, and unallocated space in SQLite databases for digital forensics and incident response.

Fetches real-time EPA air quality data and HUD foreclosure listings through an MCP server, enabling environmental monitoring and housing research.

Process in-memory tabular data with a fast, expression-based DataFrame library that supports lazy evaluation, parallel execution, and Apache Arrow semantics.

Use when content analytics, website performance data analysis, audience insights, or comprehensive data-driven reporting is needed. This agent specializes in content and website analytics within the ContentForge AI ecosystem.

Clean and preprocess datasets by handling missing values, removing duplicates, correcting types, resolving outliers, and enforcing validation schemas. Use when the user requests data cleaning or provides relevant inputs for this workflow.

Access and manage NocoDB databases, tables, and records via REST API. Use when the user wants to view bases, list tables, inspect column schemas, query or filter row data, or insert new records into a self-hosted NocoDB instance. Also use for spreadsheet-style database lookups and data entry.

Query, inspect, and manage Redis databases from the command line using redis-cli, covering connection, data operations, scanning, and server diagnostics.

Automates Big Data Cloud operations through Composio's toolkit via Rube MCP, with dynamic tool discovery and connection management.

Query documented public database APIs with explicit endpoints, filters, pagination, and provenance for reproducible retrieval of scientific, regulatory, or financial facts.

Optimizes database queries and improves performance across PostgreSQL and MySQL systems by analyzing execution plans, designing index strategies, and tuning configurations.

Guide through a structured data analysis workflow: define the question, validate data quality, select the appropriate analytical method, and produce decision-ready findings with caveats.

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.