EDS 220: Working with Environmental Data

Course logistics

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.

Banner by NASA’s Your Name in Landsat

The teaching team

Instructor

  • Carmen Galaz García (she/her/hers)
  • E-mail: c_galazgarcia@ucsb.edu
  • Student hours: Thursday 2-3 @ Bren Hall 4424

Co-Instructor

  • Annie Adams (she/her/hers)
  • E-mail: aradams@ucsb.edu
  • Student hours: Tuesday 2-3 @ Bren Hall 3418

Carmen: 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!
  • MEDS capstone courses
  • Math and data science initiatives @ Bren

Introductions

In the next few minutes, talk with a person next to you and ask them what parts of Santa Barbara have they 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

Class snapshot 1

Class snapshot 2

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.


Everyone is expected to read and adhere to the Bren School Code of Conduct and the UCSB Code of Conduct.

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.

Attendance

In-person attendance to classes and discussion sections is crucial!

If you miss a class you are expected to:

  • 📩 Be proactive: Notify the instructor before it happens or within a day and provide a brief explanation.

  • 🔄 Catch up: Work with the instructors to review any missed material.

  • 🤒 Stay home when you are sick! Prioritize your wellbeing.

Attendance does not count towards your grade, but it will be tracked and absences without communication will be addressed.

UCSB courses are taught in person, so absences for two or more weeks may require a Leave of Absence.

There will not be no option for remote attendance to class except for the class before Thanksgiving break.

Evaluation & 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.

One-time, 4-day extension

  • Every student may use one 4-day extension during the quarter, no questions asked.
  • To request it, you will need to send an e-mail to the co-instructor by the homework due date. Only requests by the due date will be accepted.
  • May be used for any homework assignment. Does not apply to any of the portfolio deadlines.
  • If you use the extension, you will still be able to submit your improved work by the resubmission due date (~1 day turnaround).
  • Beyond this extension, late work will only be accepted at the resubmission deadline (worth 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.

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 practice 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 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
  • we don’t expect perfection, we expect learning and improvement through collaboration!

Please adhere to these guidelines:

  • ✅ Cultivate understanding: You should be able to fully understand, justify, and explain all the work you submit.
  • 🤔 Question AI outputs: The default should be to assume the answers you get from generative AI are incorrect and you must verify any information the platform generates.
  • 🚫 Academic integrity: Submitting work you don’t understand or can’t explain or justify will be considered plagiarism, regardless of whether you have disclosed the use of generative AI or not.
  • 📄 Document any AI use: If you do end up using generative AI in your work, you will need to complete and submit a “Generative AI Use Documentation” form and include it with your assignment.

If there are concerns about AI use in your work, your instructor will ask you to meet and talk it through.

If understanding is clearly lacking and this is the first time this happens, you’ll have the chance to revise and resubmit your work for 50% of the original maximum grade within two days.

Please read the full policy on the course syllabus

Your questions (1)

Will we have more information about what the homework entails in advance of them being assigned?

Each assignment is posted the specified Friday and covers the materials seen in the last ~2 weeks. Assignments do build on all the previous course content!

What grading criteria do you use for the homeworks, presentations, and portfolios? Do you use a rubric or specs grading?

We provide detailed rubrics for the homeworks and portfolios. The presentations that count for student participation are just completed/not completed.

The EDS 296-1F course is not offered. How do we make sure to achieve this necessary portfolio creation?

Thanks for catching this! You should plan to attend these three workshops to create your personal website:

  • Creating personal websites using Quarto (Live session on 10/1)
  • Customizing Quarto Websites using CSS & Sass (Live session on 10/15)
  • Adding a blog to your existing Quarto website (Live session on 11/12)

Your questions (2)

For the discussion section, when presenting will we be taught the material before hand or are we using the material from class to give an example?

The discussion section materials are independent of the class materials and will be available about a day in advance, but you are not exepcted to complete them before section. There are sections listed on the website but discussion sections will be updated this year.

I see on the presentation calendar that I’m scheduled to present during week 3, but the syllabus says we each present twice. Should I sign up for my second presentation somewhere, or will we be assigned a second date later?

You will only have to present once! This has been fixed on the syllabus. Thanks for bringing this to my attention.

Student Resources