Welcome to FOR 128!

Practical Computing and Data Science Tools

Agenda

  • Introductions

  • Course goals

  • Course expectations + syllabus

  • Explore course website

  • Preparing for Thursday

Engagement

  • It has become harder and harder to stay focused over the last decade given the massive rise in wearable and mobile technology.

  • If you’ll be using a laptop to take notes, please avoid distractions to yourself and classmates by silencing notifications and avoiding checking emails, news, social media, etc.

  • Please leave your phone on silent and put it somewhere where it won’t distract you.

Introductions

In groups of ~3, introduce yourselves to each other.

(name, hometown, major, favorite food, etc.)

03:00

About me

  • I’m Grayson, a PhD student in Forestry.

  • Before coming to Michigan State, I worked as a Data Scientist contracting for the USDA Forest Service.

  • Before that, I majored in Mathematics & Statistics at Reed College.

About me

  • I ❤️ teaching, especially about Data Science + Forestry.

  • In the past, I’ve co-directed a Forestry Data Science lab for undergraduates.

About me

  • I ❤️ teaching, especially about Data Science + Forestry.

  • In the past, I’ve co-directed a Forestry Data Science lab for undergraduates.

What is (Forestry) Data Science?

Okay, but really, what is (Forestry) Data Science?

  • Creating R packages, like saeczi, which an undergraduate wrote while doing Forestry Data Science research with me

Path to Forestry Data Science

  • Take this class and engage thoughtfully with the material. ✅

  • Take FOR 372 (offered this spring) to gain more domain-specific statistical expertise.

  • A wide variety of knowledge in statistics, computer science, and your domain field of expertise (forestry) is important.

  • Engage in undergraduate research opportunities!

Course goals

  • Getting to know your computer and using it thoughtfully
    • operating systems,
    • files,
    • directories,
    • good naming conventions,
    • file organization

Course goals

  • Learn how to use a variety of data science tools with forestry applications
    • The R programming language,
    • The RStudio IDE,
    • Quarto (for reproducible reports)
  • Learn extensive R programming skills
    • base R and tidyverse approaches
    • data structures
    • custom functions
    • data wrangling & reshaping
    • beautiful plots with ggplot2

Course expectations: let’s review the syllabus

02:00

Syllabus: Materials

Textbook: IFDAR

Introduction to Forestry Data Analysis with R, by Andrew O. Finley and Jeffrey W. Doser. Available free, online: www.finley-lab.com/files/ifdar/

Technologies: R, RStudio, a laptop

R is a free and open source programming language, and RStudio is an Integrated Development Environment (IDE) which allows for streamlined use of the R programming language. Both are free to install, and installation instructions will be provided in this course. A laptop that can run R and RStudio is required for this course.

Syllabus: Meetings

Syllabus: Assessments

  • Lab reports (40%)
    • Lab reports are assigned on Thursdays during lab time, and due the following Wednesday at 5pm on D2L. We will have a lab each week (except for the week of the midterm exam).
  • Midterm Exams (20%)
    • There will be two midterm exams, one on week 6 and one on week 12. Each midterm is worth 10% of the final grade.
  • In-class quizzes (15%)
    • In-class quizzes do not have a regular schedule and will occur based on the material we get through.

Syllabus: Assessments

  • Lecture tickets (10%)
    • Lecture tickets are due at the beginning of every lecture, handed in in-person, by you.
  • Final Project (15%)
    • The details of the final project will be discussed as the semester goes on.

Syllabus: Collaboration

  • Working with classmates on labs and lecture tickets is perfectly acceptable.
  • However, please cite your collaborator(s) at the top of your assignment.
  • Collaboration on exams and quizzes is strictly prohibited.
  • But what is collaboration?

Artificial Intelligence (AI)

  • “…a key goal of this course is for you to learn how to thoughtfully, ethically, and independently write code and extract knowledge from data”

  • AI tools are being used by others to write code, but as Data Scientists, we must write code responsibly

  • At this stage of learning how to code, AI tools inhibit learning and understanding.

Syllabus: Questions?

www.for128.org

Next time:

  • Come to class with a laptop

  • Complete Lecture Ticket 1 before lecture on Thursday.