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name: programming description: Python and R programming for data analysis, automation, and reproducible analytics version: "2.0.0" sasmp_version: "2.0.0" bonded_agent: 05-programming-expert bond_type: PRIMARY_BOND

Skill Configuration

config: atomic: true retry_enabled: true max_retries: 3 backoff_strategy: exponential code_execution: sandboxed

Parameter Validation

parameters: language_focus: type: string required: true enum: [python, r, both] default: python skill_level: type: string required: true enum: [beginner, intermediate, advanced, expert] default: beginner focus_area: type: string required: false enum: [fundamentals, pandas, visualization, automation, ml, all] default: all

Observability

observability: logging_level: info metrics: [code_execution_success, test_pass_rate, performance_score]

Programming for Data Analytics Skill

Overview

Master Python and R programming for data analysis, from basic syntax to advanced data manipulation and automation.

Core Topics

Python for Data Analysis

• Python fundamentals and syntax
• Pandas for data manipulation
• NumPy for numerical computing
• Data cleaning and preprocessing

R for Statistics

• R fundamentals and tidyverse
• dplyr for data transformation
• ggplot2 for visualization
• Statistical modeling in R

Data Wrangling

• Reading various file formats (CSV, JSON, Excel, Parquet)
• Handling missing data
• Data type conversions
• Merging and reshaping datasets

Automation & Reproducibility

• Jupyter notebooks and R Markdown
• Script automation and scheduling
• Version control with Git
• Environment management (conda, venv)

Learning Objectives

• Write efficient Python code for data analysis
• Use R for statistical computing
• Automate repetitive data tasks
• Create reproducible analysis workflows

Error Handling

Related Skills

• databases-sql (for data extraction)
• statistics (for statistical programming)
• advanced (for machine learning implementation)