Bitlysis — Data Science & Analysis

When to Use

• Writing or reviewing R scripts, R Markdown, or Quarto documents
• Building Python data analysis pipelines
• Working in Jupyter notebooks (*.ipynb)
• Designing data pipeline architecture (raw → processed)
• Statistical modeling, hypothesis testing, or report generation
• Working with SPSS/Stata files (.sav, .dta)

Instructions

Step 1: Ensure Reproducibility