I reserve the right to make changes to the syllabus

Description and Outline

Statistics 159 is a course designed to cover philosophy, software tools, principles and best practices for reproducible computational research. Time permitting, the topics covered during the course include (not necessarily in this order):

  Shell (Bash) & Command Line Interface (CLI)
  Version Control with Git
  Project Hosting with GitHub
  Automation with GNU Make
  Running scripts non-interactively
  Data Analysis Project workflow
  Project Organization
  LaTex and Beamer
  Pandoc (document converter)
  Writing reports
  Dynamic documents
  R package “knitr”
  R package “rmarkdown”
  R package “shiny”
  Data Visualization
  Coding practices
  Open Science
  Presentation skills

Course cornerstones:

Grading Structure


This course will focus heavily on in-class participation in addition to assigned readings from scholarly journals, presentations from guest speakers, several “feedback” assignments, and weekly blog articles in addition to regular practice with the software tools listed in the description.

Your persistent cooperation in group work and contributions to the course will culminate into a collaborative term project. The format will be interactive and will involve your questions, opinions, and participation.

Homework Policy

Pop Quiz policy

Project Policy

In-class Presentation Policy

Collaboration Policy

Email Policy

Academic Honesty

Do your own work. Collaborating on homework is fine—but copying is not, nor is having somebody else submit assignments for you. Cheating will not be tolerated. Anyone found cheating will receive an F and will be reported to the Center for the Student Conduct.


If you need accommodations for any physical, psychological, or learning disability, please speak to me after class or during office hours. Please make arrangements in a timely manner (through DSP) so that I can make the appropriate accommodations.