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)