CAP-0395 · Engineering practice
CAPABILITY SUBJECT 61
SQL, Python, R, and Data Automation
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Complete Subject Index
CAP-0396 · Engineering practice
Work with SQL Server/SQLite, Schemas, Keys, Views, Staging Tables, Exception Queries, ETL/ELT Concepts, and Warehousing Fundamentals ↗
CAP-0397 · Engineering practice
Use Intermediate Python, Pandas, NumPy, Jupyter, CSV Processing, Data Wrangling, and Spreadsheets Automation ↗
CAP-0398 · Engineering practice
Develop API Data Pulls, Statistical/Time-Series/Regression Scripts, Visualization, and Process Automation ↗
CAP-0399 · Engineering practice
Use R/RStudio at a Working Proficiency Level for Exploratory Analysis, Statistical Modeling, and Visualization ↗
CAP-0400 · Engineering practice
Use PowerShell and Command-Line Tools for Controlled Scripting, Inspection, Administration, and Repeatable Workflows ↗
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All capability subjects ↗All portfolio connections (6)
- SQL, Python, R, and Data AutomationcontainsExtract and Reconcile Data with SQL
- SQL, Python, R, and Data AutomationcontainsDevelop Structured Data and Reporting Models
- SQL, Python, R, and Data AutomationcontainsAutomate Data Preparation and Analysis with Python
- SQL, Python, R, and Data AutomationcontainsDevelop Analytical Scripts and Data Interfaces
- SQL, Python, R, and Data AutomationcontainsExplore and Model Data with R
- SQL, Python, R, and Data AutomationcontainsBuild Repeatable Command-Line Workflows
Analytical Methods & Quantitative Decision Support
The analytical work includes data preparation, exploration, transformation and baseline analysis for production-rate studies. ANOVA, regression and diagnostic checks connect candidate drivers with measured performance. The purpose is to identify useful operating decisions from the evidence and explain the assumptions behind them.