Math 241 is an applied statistics class that covers the full spectrum of a data analysis from data collection all the way through to communicating the results. The course is problem-driven with a focus on collecting and manipulating data, using exploratory data analysis and visualization tools, identifying statistical methods appropriate for the question at hand, and communicating the results in both written and presentation form.
Approximately the first half of the class will be dedicated to learning the basics of graphical displays, data acquisition, and data cleaning. We will complete weekly or biweekly assignments during this period.
In the second half of the class, we will break into project teams to attack substantive projects that relate to civic data. We will meet less frequently during this period, but weekly check-ins are required between each team and the faculty supervisor.
The goals of this semester are to learn the ins and outs of basic data science, tackle a set on interesting projects related to civic data, and produce a set of analytical project reports. It should be a lot of work and a lot of fun!
Andrew Bray
Library 304
Office hours: Monday 11 - 12, Thursday 11 - 12, Friday 11 - 12
We meet for lecture TuTh 6 - 7:30 pm in Eliot 314. Please bring a laptop to every class.
Modern Data Science with R Baumer et al.
Other very useful readings (free online!):
- R for Data Science by Hadley Wickham
- Happy Git with R by Jenny Bryan
Approximately every week there will be a homework assignment that is to be submitted as an .Rmd file to your repository on the course page, github.com/ds-civic-data. Many of these assignments will also include a substantive peer-feedback component.
The last 6 weeks of class will be dedicated to working on group projects. These will culminate in a well-organized repository on github, a poster displaying your work, and an oral presentation at a symposium in April.