EDS 220: Working with Environmental Data
Course logistics & syllabus
Week 1
Welcome to EDS 220!
This course focuses on hands-on exploration of widely-used environmental data formats and Python libraries. Together, we’ll work with real-world datasets, giving you the skills to analyze and understand the environment around us.
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The basics
Instructor
- Carmen Galaz García (she/her/hers)
- E-mail: galazgarcia@bren.ucsb.edu
- Student hours: Wednesday 4-5 @ Bren Hall 4424
TA
- Annie Adams (she/her/hers)
- E-mail: aradams@ucsb.edu
- Student hours: Thursday 12 pm - 1 pm @ Bren Hall 3022
Class Schedule: Monday and Wednesday, 9:30 - 10:45 AM, Bren Hall 1424
Discussion Sections: Thursday, 1:00 - 1:50 PM, Bren Hall 3022
About me
- Assistant Teaching Professor @ Bren
Before that:
- Data Scientist @ NCEAS
- Ph.D. in Mathematics @ UCSB
Research:
- Image analysis for invasive plant species detection
Teaching:
- Developing our MEDS Python curriculum!
Introductions
In the next few minutes, talk with a person next to you and ask them what parts of Santa Barbara have you enjoyed exploring.
You’ll get to introduce your partner at the end.
Learning Objectives
By the end of this course, you will be able to:
- Write Python code from scratch following best practices and adapt code others write.
- Manipulate various types of environmental data, including tabular, vector, and raster data, using established Python libraries.
- Find and access datasets from major public environmental databases.
- Produce effective reports that combine text and code to share their data analyses with colleagues.
Tentative Schedule
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Class snapshot 1
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Class snapshot 2
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Code of Conduct
We expect all course participants (including instructors, guests, and students) to be committed to actively creating, modeling, and maintaining an inclusive climate and supportive learning environment for all.
We expect everyone to treat every member of our learning community with respect.
Harassment of any kind will not be tolerated.
Access & Accommodations
If you have any kind of disability, whether apparent or non-apparent, learning, emotional, physical, or cognitive, you may be eligible to use formal accessibility services on campus.
To arrange class-related accommodations, please contact the Disabled Students Program (DSP). DSP will initiate communication about accommodations with faculty.
By making a plan through DSP, appropriate accommodations can be implemented without disclosing your specific condition or diagnosis to course instructors.
Evaluation and Grading
Grading Breakdown:
- Homework: 75% (4 assignments)
- Portfolio: 20%
- Participation: 5%
Grade Cutoffs:
- A+ (≥ 97%), A (≥ 92%), A- (≥ 90%),
- B+ (≥ 87%) , B (≥ 82%), B- (≥ 80%),
- C+ (≥ 77%), C (≥ 72%), C-(≥ 70%),
- D+ (≥ 67%), D (≥ 62%), D-(≥ 60%),
- (60>) F.
Homework Assignments
- There will be 4 homework assignments.
- Assignments are assigned every other Friday starting on week 1 and should be submitted by 11:59 pm on next week’s Saturday.
- Working together and collaborating with peers on homework is highly encouraged!
- Submissions are individual so make sure you understand everything you are turning in.
Regrading
You can resubmit your assignments three days after they have received initial feedback.
- In this second submission, you may recover up to 50% of the points not obtained during the initial submission.
Why regrades? Revisions, corrections, and improvements are crucial in the learning process! We greatly encourage you to resubmit your revised assignments.
Example: You submitted your homework on time on the due date and got a 6/10 in the assignment the coming Wednesday. You may build on the feedback received to correct your work and resubmit to improve your grade up to 8/10.
Except for extenuating circumstances, there will be no extension for any assignment. Late submissions will be accepted at the resubmission date and can obtain up to 50% of the assignment points.
Portfolio Project
The final assignment for the course will be creating data science materials for the students’ online professional portfolio.
Final Assignment:
The 20% grade for the portfolio is divided as follows:
- 13% Data analysis + GitHub repository: a presentation-ready GitHub repository containing a finalized Jupyter Notebook and associated files for the data analysis,
- 7% blog post: a blog post in the student’s professional portfolio based on previous assignments and discussion sections
Both a submission and a revised submission addressing all the feedback from the first revision will be needed for these two tasks.
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Participation Requirements
To obtain full participation credit:
- Answer two short surveys about their course experiences, one at the beginning and one at the end of the course.
- Share coding solutions for exercises or homework during lecture or discussion sections at least once during the course.
- A presentation date during the discussion section has been randomly assigned to each student.
- You can trade dates with others. Please notify the TA or instructor about any presentation updates.
- Time for presentation during class time may also be available.
Why come up to present your solutions? Many reasons! To ractice public speaking, get comfortable with technical vocabulary, practice explaining a step-by-step solution, practice the material by teaching others, have a taste of live-coding, among others.
Policy on Generative AI (GenAI)
GenAI tools (such as ChatGPT) are strongly discouraged for the following reasons:
- becoming proficient in core programming skills comes through practice
- building your own programming proficiency will help you engage with GenAI tools more efficiently and responsibly
- subscription versions of GenAI tools may induce an inequitable learning environment
Please adhere to these guidelines:
- you may use spell / grammar check and / or synonym identification tools
- be prepared to explain each line of code in your assignments and exercises
- assignments that make a low-energy or unreflective use of GenAI will be heavily penalized.
