GEOG1230A-W25 crn:11371

Instructors:

Times

Monday Tuesday Wednesday Thursday Friday
10:00-11:30 Lecture 10:00-11:30 Lecture 10:00-11:30 Lecture 10:00-11:30 Lecture  
12:00-2:00 Office Hrs 1:30-4:00 Workshop 12:00-1:00 Office Hrs 1:30-4:00 Workshop  
5:00 Individual Assignment Due     Individual Assignment Given 12:00 Computational Notebook Due

Places

  • Morning Lecture: BiHall 104
  • Afternoon Workshop: BiHall 632
  • Holler’s Office Hours: BiHall 632 or 634
  • Virtual Discussion Forum

Course Description

In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning, large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Biology, Geography, History, Mathematics/Statistics and Sociology. This course will use the R programming language. No prior experience with R is necessary.

GEOG 1230 Section: In this section, we will investigate human vulnerability to natural hazards in the United States using location-based text data about hurricane and flood disasters from social media. We will analyze data qualitatively, temporally, and spatially to gain insights into the human experience of previous disasters and disaster response. We will present findings using spatial data visualizations with the aim of informing future disaster preparedness and resilience.

Learning Goals

  • Learn fundamentals of data science using the R langauge.
  • Investigate and describe the characteristics, strengths, and weaknesses of geographic social media data.
  • Answer geographic questions about disasters by analyzing and visualizing geographic data from social media and the US Census.
  • Communicate informative geographic stories about disasters with (interactive) data visualization.

Required Materials and Resources

There are no required book purchases.
Three or four digital articles will be provided and assigned to inform afternoon activities, assignments, and the final projects.

You will need a laptop computer (MacOS, Windows, or Linux) and the following free software and accounts:

Morning lecture materials will be posted to a course Google Drive Folder (Middlebury access only)

Expectations and Evaluation

Participation 25%: Full attendance and participation is required for success in the course. The participation grade is to be composed of attendance (10%) and completion of useful and tidy computational notebooks (15%) checked at noon EST on Friday of each week. Chronic tardiness and each unexcused absence will result in loss of 10% of attendance credit.
Students have the responsibility to make up content and work from absences and use office hours for this when necessary.

Individual Assignments 45%: Three individual assignments at the end of teh first three weeks will provide a chance to practice spatial data science and prepare for final projects. The assignments will be given at the end of Thursday workshops and due at 5:00 pm EST on Monday.

Group Projects 30%: A final small group project will develop a single interactive spatial data science story about a disaster in the United States. The project will be presented live on one of the final two days of class (10%) and due with working code on GitHub by noon EST of the final day of class (20%). The project will be accompanied by a short reflection on the overall learning experience and on the dynamics of small group collaboration.

Numerical grades are translated into letter grades with the following break values: {95, 90, 87, 83, 80, 77, 73, 70, 67, 60}.

Learning and Accomodation

Successful and active learning is expected and supported! The Center for Teaching, Learning, and Research go/ctlr has plentiful resource guides and tutors for organization, study skills, writing, and STEM. For support with a documented disability, please communicate with the Disability Resource Center go/ada and myself as soon as possible. This is confidential. Contacts at DRC include: ada@middlebury.edu, Jodi Litchfield at litchfie@middlebury.edu or 802-443-5936, Peter Ploegman at pploegman@middlebury.edu or 802-443-2382, or Deirdre Kelly at dkelly@middlebury.edu or 802-443-2746.

Late policy

It is imperative to keep up with content in cumulative courses!
Late assignments will lose 10% of full credit per day. Very short extensions may be granted with notice more than two days in advance, or in reponse to urgent and unexpected circumstances.

Honor Code

The Middlebury College Honor Code requires students to neither give nor receive unauthorized aid on any assignment. Unless otherwise noted, in Data Science Across Disciplines you are always authorized to use class notes, consulation with peers, R software documentation, books, tutorials, troubleshooting forums (e.g. Stack Overflow, ResearchGate, GitHub Discussion, etc.). You may also use internet searches and AI-supported chatbots to help debug your code. You may not copy blocks of code from generative AI or from other data scientists. In special cases, you may request permission to do so from the professor, and you are required to attribute the source. If you submit any work from elsewhere without authorization and attribution, it will be considered a violation of the Honor Code. If you don’t understand how the Middlebury Honor Code might apply to a particular assignment or assessment, please ask!