Total 50,510 skills, Data Processing has 2560 skills
Showing 12 of 2560 skills
Use when writing Python that processes biological sequences (DNA/RNA/protein) with the seqpro package — encoding, one-hot, k-mer shuffling, reverse complement, GC content, variable-length sequence batches, or anything involving seqpro's `Ragged` array. Covers the seqpro API surface and the conventions you need to use it correctly.
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit
SQL and Python-based employee performance analytics with KPI aggregation, departmental insights, and HR dashboard generation
End-to-end ELT pipeline using SSIS, SQL Server, and PySpark for enterprise data warehousing and analytics
End-to-end ETL pipeline and analytics application for Harvard Art Museums API with Streamlit dashboards
End-to-end data engineering pipeline for Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization
Discover and subscribe to external spatial datasets via CARTO Data Observatory and partner catalogs.
Choose and configure the data warehouse engine connection for CARTO (BigQuery, Snowflake, Redshift, Postgres, Databricks, Oracle).
Use this skill when the user wants to manage data quality in DataHub: create or run assertions, check assertion outcomes, raise or resolve incidents, create notification subscriptions, or diagnose health problems across their estate. Triggers on: "create assertion", "run assertion", "check quality", "data quality", "health check", "raise incident", "resolve incident", "subscribe to", "failing assertions", "active incidents", or any request involving data quality, assertions, incidents, or quality notifications.
Build ETL pipelines and analytics dashboards for Harvard Art Museums API data with MySQL storage and Streamlit visualization
Tongdaxin Quantitative Data Retrieval Skill. Use this skill when users mention tdxquant, Tongdaxin, TdxQuant, tqcenter, and need to obtain A-share market data (market snapshot, K-line, financial data, sector data, convertible bonds, new stocks, trading data, etc.), query trading calendars, execute Tongdaxin formulas, subscribe to market quotes, or place trading orders.
Core Power BI data modeling, source connectivity, and platform fundamentals. PROACTIVELY activate for: (1) Power BI data modeling and star-schema design, (2) relationships (active/inactive, bidirectional, USERELATIONSHIP), (3) data-source selection (DirectQuery vs Import vs Direct Lake vs composite), (4) incremental refresh setup, (5) gateway configuration (on-prem and VNet gateways), (6) streaming datasets and push-data scenarios, (7) Dataflow Gen2 basics, (8) Power BI common gotchas and pitfalls (bidirectional filtering, AutoExist, blank-row), (9) workspace identity and OAuth2 / service-principal auth, (10) semantic model architecture review. Provides: star-schema templates, mode-selection matrix, incremental refresh recipe, gateway setup steps, and a common-gotchas reference.