Standard syllabus
Statistical computing · Undergraduate · Statistics
Topics
Programming fundamentals
- R or Python for data manipulation
- Functions, control flow, and vectorization
- Reading and writing data files
- Data frames, tibbles, and tidy data principles
- Version control with Git (introduction)
Simulation and numerics
- Monte Carlo simulation for probability and inference
- Bootstrap resampling
- Numerical optimization for MLE
- Random number generation and seeding
- Matrix computations for statistics (intro)
Reproducible workflow
- R Markdown or Quarto / Jupyter notebooks
- Package management and project structure
- Debugging and profiling (introduction)
- Ethics of data handling and privacy
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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