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Statistics
Description: This course equips graduate students with the advanced computational tools and programming skills essential for modern statistics and data science. Students develop proficiency in writing robust, efficient code using industry-standard languages, including (but not limited to) Python, R, and Julia. The curriculum focuses on the practical implementation of statistical methods, covering complex data structures, data wrangling from varied sources (such as SQL and APIs), and professional-quality data visualization. Students will master algorithmic efficiency by implementing statistical algorithms--including Monte Carlo simulations, bootstrapping, and numerical optimization--from scratch. Emphasis is placed on constructing reproducible research workflows and automated reports using literate programming tools like Quarto, R Markdown, or Jupyter. Additionally, the course covers code debugging and profiling to optimize time and memory usage, alongside critical discussions on data ethics and algorithmic bias. Letter grade only.
Units: 3
Sections offered: Fall 2026
Prerequisite: Admission to Statistics and Data Science; M.S.
Statistics
Term : Fall 2026
Catalog Year : 2026-2027
STA 556 - Statistics And Data Science Computing Workflows
Description: This course equips graduate students with the advanced computational tools and programming skills essential for modern statistics and data science. Students develop proficiency in writing robust, efficient code using industry-standard languages, including (but not limited to) Python, R, and Julia. The curriculum focuses on the practical implementation of statistical methods, covering complex data structures, data wrangling from varied sources (such as SQL and APIs), and professional-quality data visualization. Students will master algorithmic efficiency by implementing statistical algorithms--including Monte Carlo simulations, bootstrapping, and numerical optimization--from scratch. Emphasis is placed on constructing reproducible research workflows and automated reports using literate programming tools like Quarto, R Markdown, or Jupyter. Additionally, the course covers code debugging and profiling to optimize time and memory usage, alongside critical discussions on data ethics and algorithmic bias. Letter grade only.
Units: 3
Sections offered: Fall 2026
Prerequisite: Admission to Statistics and Data Science; M.S.