- Instructor: Bradley McDonnell ([email protected])
- Time: Tuesday 9-10am (meets twice a month)
- Location: Phonetics Lab (Moore 162)
This course is designed to equip students working with a broad range of linguistic data to use cutting edge research methodologies in data science. It will be held in conjunction with the Data Science Group at the University of Hawai‘i at Mānoa, which is currently run by the Department of Linguistics. Students enrolled in the course will be expected to participate in the Data Science Group meetings below.
Please follow the Sotware installation guide to install all necessary software. Please install all software before our first meeting. (Note that we won't begin using R or R Studio until meeting 4.)
Below is a tentative course schedule.
Date | Topic | Facilitator | |
---|---|---|---|
1 | 2/5/19 | Intro to git (Part 1) | Brad |
2 | 2/12/19 | Intro to git (Part 2) | Brad |
3 | 2/19/19 | Linking git & GitHub | Brad |
4 | 3/5/19 | Data visualization with ggplot (part 1) | Leah, Ivan |
5 | 3/12/19 | Data transformation with tidyverse (part 1) | Noella |
6 | 4/2/19 | Data transformation with tidyverse (part 2) | Christina, Dan |
7 | 4/16/19 | Exploritory Data Analysis | Amber |
8 | 4/30/19 | Dynamic documents | Brad |
This course will draw on two valuable resources:
- R for Data Science by Garrett Grolemund and Hadley Wickham
- Lessons from Software Carpentry
Students are required to participate in the 8 meetings listed above and will be asked to be a facilitator for at least one of these meetings. In addition, students are required to either participate in 4 LAE lab workshops throughout the semester or demonstrate equivilent progress on a project that relates to this course. Students are also required to complete a final project.
Students in the course will be required to create a GitHub repository that contains:
- README.md file that explains the content of the repository
- RStudio project with...
- Dataset (saved as a .csv file)
- Code that transforms the data with tidyverse
- Plots that display the data in a meaningfull way with ggplot.