Ecosystem Science and Sustainability 330

Quantitative Reasoning for Ecosystem Science

Instructor
Mike Johnson
Geospatial Science &Technology Lead, NOAA
webpage

Office Hours
M,W 11-11:45 (as needed)
W 9-10
Lobby by classroom

Lecture
January 21st - May 9th, 2025
Monday, Wednesday
10:00am - 10:50am
NR 113

Teaching Assistants

Jaque McVey
PhD Student, CSU
Office Hours: T 12:45-1:30
NESB B247

Alan Cai
PhD Student, CSU
linkedin
Office Hours: Th 12:00-2:00
NESB A105

Labs
Section 1: Th 8:00am–9:50am (McVey)
Section 2: Th 2:00pm–3:50pm (McVey)
Section 3: Th 4:00pm–5:50pm (Cai)
Section 4: F 12:00pm–1:50am (Cai)
NR 232

About this course:

Quantitative reasoning involves analyzing, interpreting, and solving problems using numerical and logical reasoning. This course equips students with essential data science and quantitative skills, focusing on applying these methods to problems in ecosystem science and sustainability.

Students will engage in practical data-driven approaches through lectures, live coding demonstrations, discussions, and hands-on lab sessions. The course emphasizes the use of open-source technologies (R, Quarto), statistical methods, geospatial data analysis, and machine learning within reproducible workflows.

Course Objectives:

By the end of this course, students will:

  • Assess ecosystem science challenges using quantitative tools.
  • Apply data-driven methods to real-world sustainability issues.
  • Use these skills in scientific research, decision-making, and environmental data analysis.

Prerequisites: MATH 155/160; STAT 301/307/315; ESS 211 or LIFE 320
Credits: 3
Course Website: Github Pages

Course Structure:

This face-to-face course consists of two 50-minute lectures and one 2-hour lab session each week. Labs will be conducted in the NR computer lab, where students will work through applied problems or learning new skills. It is recommended to bring their own laptops to lab sessions, but computers will be available for those without one.

The course itself is divided into 4 units each adding to the previous one in an attempt to provide you with the background needed to think, write, process and model data related to the enviroment.

Course Units:

An approximate weekly schedule is available on the course schedule. While it is subject to change, we will be guided by four primary units:

  1. Open Science and Tools (Weeks 1-3)
    • Introduction to reproducible workflows and software (R, Rmd, Git)
    • Learning efficient interaction with computers whether you love them or hate them
  2. Working with Data (Weeks 4-7)
    • Cleaning, refining, and analyzing messy data sets
    • Techniques for effective data interpretation
  3. Modeling (Weeks 8-12)
    • Statistical, predictive, and classification models
    • Introduction to Machine Learning concepts
    • Understand that “all” problems are either prediction or classification
  4. Geospatial Data (Weeks 13-16)
    • Applying spatial information to data analysis and problem-solving

Grading & Evaluation:

Final grades will be based on points earned out throughout the semester based on the following opportunities. Regular participation and consistent practice will set you up for success.

Component Points Percentage
Daily Exercises 250 20%
Lab Activities 120 10%
Labs 620 50%
Final Project 250 20%
Extra Credit Opportunities 130 10%
Total Assigned Points 1,240
Total Possible Points 1,370

Grading Scale:

  • A+: 100 % to 96.67%
  • A : < 96.67 % to 93.33%
  • A-: < 93.33 % to 90.0%
  • B+: < 90.0 % to 86.67%
  • B : < 86.67 % to 83.33%
  • B-: < 83.33 % to 80.0%
  • C+: < 80.0 % to 76.67%
  • C : < 76.67 % to 70.0%
  • D : < 70.0 % to 60.0%
  • F : < 60.0 % to 0.0%

Evaluation Components

Daily Exercises (20%)

Short coding or reading assignments graded pass/fail. Complete 25 out of 29 assignments, with 4 extra credit opportunities.

Lab Activities (10%)

Weekly in-class activities reinforcing lecture topics. Complete 12 out of 13 activities, with 1 extra credit opportunity.

Labs (50%)

Approximately 9 hands-on labs will provide applied learning experiences, focusing on solving real-world problems through data analysis.

Final project (20%)

Students will design and choose a final project to demonstrate their quantitative reasoning skills, mixing ideas from lectures and labs to answer a question of interest to them. Final projects will be presented in the final lab section - lightning talk style.

Extra Credit Opportunities (10%)

Course Material

  • All readings and resources are free and avialable online

  • It is highly encouraged that you do all work on you own laptop. You’ll need a machine with a full OS (not Chromebook) to install the software and process the data we’ll be working with. If this is not possible for you, please work with me or your TA to find a good solution.

  • This class borrows from the R community and the data science world at large - particualry from UCSB. It seeks to provide a curreated set of the best material along with careful guidance through it. If someone has made exceptional resources, we want to share that with you rather then reinvnet the wheel (a core tenet of data science at large - borrow, share, contirbute)

How to Approach This Course:

This course is designed to build practical, cumulative skills in environmental data science. Each week’s material builds on the previous one, so consistent participation and regular practice are crucial. Missing early concepts will make it harder to grasp later topics, but staying engaged will set you up for success.

Attendance is highly encouraged, as active participation in class discussions and assignments will reinforce your learning. If you need to miss a session, please inform us in advance whenever possible.

The workload is manageable, and by keeping up with daily and weekly assignments, you’ll gain valuable skills and perform well. Historically, students who have committed to the course have succeeded—not just with strong grades but also in building a foundation for future opportunities.

While this course may feel challenging at times, persistence pays off. No student who has consistently participated and completed the work has finished with less than a B. Remember, we’re here to support you every step of the way. With effort and engagement, you’ll leave this course prepared to excel as an environmental data scientist.

Resources

Software

In this course we will use R which is an open source programming environment. You will interact with R through the RStudio IDE. Your projects and code will be turned into through GitHub as Quarto reports. Through the course you will build an online portfolio that can be taken with you later on. If you haven’t used these tools before, don’t worry - we will get up and running with them during the first week.

References

I recommend the following reference if a topic peeks your interest. Versions of all of them are available for free at the following links: