Statistical computing
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
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
STEM / applied
Applied computing projects
- End-to-end analysis pipelines
- Visualization with ggplot2 or matplotlib
- Parallel computing for simulation (intro)
- Working with APIs and web-scraped data
- Automated reporting for stakeholders
- Performance tuning for large datasets
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Lab-oriented course typically taught in R or Python. Bridges introductory statistics and upper-division methods courses.