STEM / applied
Statistical computing · Undergraduate · Math
Learning objectives from the Statistical computing syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Statistical computing — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Programming fundamentals
- R or Python for data manipulation — R or Python for data manipulation
- Functions and control flow — Functions and control flow
- Vectorization — Vectorization
- Reading and writing data files — Reading and writing data files
- Data frames and tibbles — Data frames and tibbles
- Tidy data principles — Tidy data principles
- Version control with Git (introduction) — Version control with Git (introduction)
Simulation and numerics
- Monte Carlo simulation for probability — Monte Carlo simulation for probability
- Inference — Inference
- Bootstrap resampling — Bootstrap resampling
- Numerical optimization for MLE — Numerical optimization for MLE
- Random number generation — Random number generation
- Seeding — Seeding
- Matrix computations for statistics (intro) — Matrix computations for statistics (intro)
Reproducible workflow
- R Markdown or Quarto / Jupyter notebooks — R Markdown or Quarto / Jupyter notebooks
- Package management — Package management
- Project structure — Project structure
- Debugging — Debugging
- Profiling (introduction) — Profiling (introduction)
- Ethics of data handling and privacy — Ethics of data handling and privacy
Learning objectives
Click an objective for study materials.
Programming fundamentals
- R or Python for data manipulation — R or Python for data manipulation
- Functions and control flow — Functions and control flow
- Vectorization — Vectorization
- Reading and writing data files — Reading and writing data files
- Data frames and tibbles — Data frames and tibbles
- Tidy data principles — Tidy data principles
- Version control with Git (introduction) — Version control with Git (introduction)
Simulation and numerics
- Monte Carlo simulation for probability — Monte Carlo simulation for probability
- Inference — Inference
- Bootstrap resampling — Bootstrap resampling
- Numerical optimization for MLE — Numerical optimization for MLE
- Random number generation — Random number generation
- Seeding — Seeding
- Matrix computations for statistics (intro) — Matrix computations for statistics (intro)
Reproducible workflow
- R Markdown or Quarto / Jupyter notebooks — R Markdown or Quarto / Jupyter notebooks
- Package management — Package management
- Project structure — Project structure
- Debugging — Debugging
- Profiling (introduction) — Profiling (introduction)
- Ethics of data handling and privacy — Ethics of data handling and privacy
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Programming fundamentals
R or Python for data manipulation
Coming soon - Simulation and numerics
Monte Carlo simulation for probability
Coming soon - Reproducible workflow
R Markdown or Quarto / Jupyter notebooks
Coming soon
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$1,162 · Statistical computing · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.