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

Error Type	Cause	Recovery
ImportError	Missing package	pip/conda install package
SyntaxError	Invalid code	Check syntax, use linter
MemoryError	Data too large	Use chunking or dask
TypeError	Wrong data type	Explicit type conversion
FileNotFoundError	Missing file	Verify path, check permissions

Related Skills

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