Data Science Workflows
Course ID
DSCI 522
Campus
UBC Vancouver
Semester
Fall
Course Block
3
Course Description
Full lifecycle of data analysis by integrating interactive and scripted methods, ensuring reproducibility, clarity, and collaboration. Through hands-on practice, students will fluidly transition between REPL-driven exploration (e.g., RStudio, IPython) and automated scripting, producing dynamic, literate documents that blend narrative, code, data, results, and visuals via tools like Quarto, R Markdown, and Jupyter Notebooks. The course emphasizes effective project structure; enforcing naming conventions, path and dependency management; function writing; and version control systems (e.g., Git with GitHub) to track and share work. To streamline and scale workflows, students will also learn how to use Make to automatically build reliable, reproducible data science pipelines.