Skip to content

Latest commit

 

History

History
51 lines (32 loc) · 2.91 KB

index.md

File metadata and controls

51 lines (32 loc) · 2.91 KB
Raw
css.file dirs.to.publish
_fileherd/markdown7.css
.

Go back to teaching

STAT 545A Exploratory Data Analysis

Course description

This course has three intertwined goals;

  • Introduce students to R, a free software environment for statistical computing and graphics, and put them on the fast track to becoming power useRs!

  • Expose students to data analytical approaches that exemplify modern statistical practice. Expose students to statistical methods ranging from core techniques (description and exploration, linear models) to more modern and flexible techniques (resampling / bootstrap, smoothing, robust statistics).

  • Develop students' practical skills in exploring, visualizing, and analyzing datasets. Cultivate complementary skills in making analyses reproducible, reusable, and shareable. Heavy emphasis on data manipulation, making figures, and report generation.

Knowledge and skills from this course may be useful to incoming graduate students as preparation for Research Assistant work and for MSc/PhD thesis projects.

AUDIENCE. This course is open to any graduate student in the Department of Statistics. Graduate students from other departments are welcome, but please touch base with the instructor.

TEXTBOOK. None. We will use online resources and eBooks available through the UBC site licenses.

PREREQUISITE. Sufficient statistical background or permission of the instructor.

EVALUATION (subject to change)

  • Homework. Three to five small assignments, usually time-limited, marked coarsely (check minus / check / check plus). Used for minor adjustments to final mark, which is chiefly determined by ...

  • Final project. Perform an exploratory analysis of a dataset of your choosing, with an emphasis on figures. Facilitate comparisons. Identify trends. Submit a short report, data (if possible), code, and figures.

Changes planned for 2013

No guarantees that all of these changes will materialize but they are on the radar.

  • Use of the integrated development environment (IDE) RStudio.
  • Move to more, smaller homework assignments, possibly leading to elimination of the final project. Peer evaluation of homework.
  • More hands-on analysis during class time. We will meet in a computer lab.
  • Introduce the add-on graphics package ggplot2. Present along-side lattice.
  • Coverage of version control, namely, git and github a web-based hosting service for git repositories.
  • Increased emphasis on reproducibility and shareability, via dynamic report generation using the add-on package knitr.

To see content from 2012, go here.