Basic information

  • Course title: SDS 390 Topics in Statistical and Data Sciences - Ecological Forecasting
  • Communication: All communication will take place via this Slack workspace. You must have the Slack Desktop or Mobile App installed, since it is too easy to miss messages when using the browser version.
  • Lectures: Remote only
    1. Scheduled time: Tue/Thu 9:20am - 10:35am Eastern
    2. Meeting times: Tue/Thu 9:45am - 10:35am Eastern
  • Office hours:
    1. Immediately after lecture: Just stick around
    2. Scheduled group office hours: TBD
    3. For individual matters, by appointment at If there aren’t any appointments, please Slack me.

Instructor work-life balance

  • I will respond to Slack messages sent during the week within 24h. I will respond to Slack messages sent during the weekend at my own discretion.
  • If possible, please only Slack me with briefer and administrative questions; I prefer having more substantive conversations over Zoom as it takes me less energy to understand where you are at.

About this course

Official course description

Ecologists are asked to respond to unprecedented environmental challenges. How can they provide the best scientific information about what will happen in the future? The goal of this seminar is to bring together the concepts and tools needed to make ecology a more predictive science. Topics include Bayesian calibration and the complexities of real-world data; uncertainty quantification, partitioning, propagation, and analysis; feedback from models to measurements; state-space models and data fusion; iterative forecasting and the forecast cycle; and decision support. A semester-long project will center on data from the Smithsonian Conservation Biology Institute (SCBI) forestry reserve.


  1. SDS 192 Introduction to Data Science
  2. SDS/MTH 291 Multiple Regression
  3. The same pre-reqs as MTH 211 Linear Algebra, which is a required course for the SDS major:
    • MTH 112 Calculus II or
    • MTH 111 Calculus I and MTH 153 Intro to Discrete Math

Learning goals

This semester you will:

  1. Learn how to collaborate remotely
    1. Slack
    2. Understandable (not just functioning) code, (repr)oducible (ex)amples
    3. GitHub: issue tracking, branches/pull requests, code reviews
  2. Learn how to build (virtual) infrastructure
    1. R packages
    2. shiny dashboards like this one.
    3. Blogposts
  3. Learn “how to learn”
    1. Acquiring just enough ecological knowledge so that you can leverage your data science skills
    2. Strategies for reading journal articles
  4. Learn to keep the big picture in mind
    1. Climate change
    2. Ugly legacies ecology has inherited and their influence on society
    3. The role that nature and the outdoors can play in improving your mental health


We’ll be using the following two textbooks:

Topic Schedule and Readings


Projects 80%

Over the course of the semester there will be several projects relating to the learning goals. They’ll involve some combination of the following data:

  1. Smithsonian Conservation Biology Institute (SCBI) ForestGEO Data (GitHub)
  2. Michigan Big Woods research plot data (Michigan Deep Blue Data)
  3. Smith College MacLeish Field Station ( and (macleish R package)

Engagement 20%

This will be measured by a combination of

  1. Staying in touch with me and your classmates
  2. Your participation in synchronous Zoom meetings
  3. Discussions on Slack

Academic Honor Code

All students are expected to adhere to the Smith College Academic Honor Code:

Smith College expects all students to be honest and committed to the principles of academic and intellectual integrity in their preparation and submission of course work and examinations. Students and faculty at Smith are part of an academic community defined by its commitment to scholarship, which depends on scrupulous and attentive acknowledgement of all sources of information, and honest and respectful use of college resources.

In the case of a suspected violation, I will follow the procedures for faculty:

  1. I will have a private discussion with the student first, where I will present student with evidence of the infraction in question.
  2. If an Academic Honor Board case is merited, I will give the student the opportunity to report themselves first at .
  3. I will then write to and to the Dean of the College .
  4. The ultimate decision will then be in the hands of the Academic Honor Board.


Smith is committed to providing support services and reasonable accommodations to all students with disabilities. To request an accommodation, please register with the Disability Services Office at the beginning of the semester. To do so, call 413.585.2071 to arrange an appointment with Laura Rauscher, Director of Disability Services.

Once you have received an accommodation letter, please provide your instructor with a copy.

Code of Conduct

As the instructor and assistants for this course, we are committed to making participation in this course a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion. Examples of unacceptable behavior by participants in this course include the use of sexual language or imagery, derogatory comments or personal attacks, deliberate misgendering, trolling, public or private harassment, insults, or other unprofessional conduct.

As the instructor and assistants we have the right and responsibility to point out and stop behavior that is not aligned to this Code of Conduct. Participants who do not follow the Code of Conduct may be reprimanded for such behavior. Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the instructor.

All students, the instructor, the lab instructor, and all assistants are expected to adhere to this Code of Conduct in all settings for this course: lectures, labs, office hours, tutoring hours, and over Slack.

This Code of Conduct is adapted from the Contributor Covenant, version 1.0.0, available here.