ENV 872 - Environmental Data Analytics

ENV 872 - EDA   |   Spring 2024   |   Instructors: Luana Lima,  John Fay  |  

Course Overview

Instructors/Office hours TAs/Office hours
Luana Marangon Lima : Tue 9-10am - Gross Hall 102k
John Fay: Fri 1-2pm - 3112 Grainger Hall		 Li Gia Go: Tue 12-1pm: GH3100
Emma Kaufman: Mon 10-11am - GH3100

♦Course Description

Given the growing focus of environmental disciplines on quantitative approaches, students entering the environmental workplace have a need to face new challenges related to data. Data analytics encompasses not only statistics and data visualization but also puts those practices in context of the acquisition, exploration, processing, and reporting of data. In this course, we work through contemporary data analyses while developing skills to integrate software applications, manage data, and effectively report results. Students will develop reproducible workflows to analyze real environmental datasets from start to finish. The setting of this course is a hands-on lab, where students will work through a series of lessons, assignments, and a final course project.

♦Course Objectives

  • Gain proficiency in the language and application of R software.
  • Synthesize information from datasets, working from start to finish in the “data pipeline”.
  • Develop skills to identify and apply appropriate statistical and graphical approaches for environmental datasets, incorporating the guidelines of experimental design and interpretation of output.
  • Integrate multiple technological applications involved in contemporary data analysis, workflow, and management.

♦Course Prerequisites

Prior R programming experience is preferable but not mandatory.

Course Format

♦Course Topics

Below is a tentative schedule of topics for this class. See the Full Calendar for a complete, up-to-date calendar.

Week Module
Jan 9-15 1-Course Setup & Intro
Jan 16-22 2-Coding Basics
Jan 23-29 3-Data Exploration
Jan 30-Feb 5 4-Data Wrangling
Feb 6-12 5-Data Visualization
Feb 13-19 6-Crafting Reports
Feb 20-26 7-Linear Models
Feb 27-Mar 4 8-Time Series Analysis
Mar 5-11 9-Spatial Analysis
Mar 12-18 Spring Break
Mar 19-25 10-Data Scraping
Mar 26-Apr 1 11-Python for R Users
Apr 2-Apr 8 Class wrap up
Apr 9-15 Course Projects

♦Lectures & Assignments

This is a “flipped” course, meaning we expect you to watch a set of recorded lectures presenting concepts and exercises prior to an in-person session where we review and discuss these topics. The recordings are coordinated with coding exercises to give you practical, hands-on experience with the topics covered. We will cover additional “coding challenges” during the in-person sessions, providing you opportunity to discover and ask any remaining questions you have on the material.

The topics are organized as a set of weekly modules covering the fundamentals of data analysis as applied to various environmental datasets. (See above.) On completing the in-person section of each module, we’ll assign another coding challenge for you to complete. These assignments involve applying concepts and tools learned in class to an specific data set or problem. You are welcome to work together and help each other on these assignments. However, they are to be submitted individually (unless otherwise specified). See the Deliverables section for a list of assignments and due dates.

♦Course Project

In addition to the weekly assignments, you will execute a course project. The course project could take several forms. If you have an interesting dataset, you may choose to work with it using existing methods and software tools to run your data analysis. Students are encouraged to work in teams of two or three for a project. Students should work with students in the same lab section, but exceptions can be made.

There will be one short presentations to be held during the lab sections where you will show the class the main results obtained throughout the analysis. Aside from the presentations, you are required to submit a final report where you describe the data sets, tools used and results.

♦Grading

Homework assignments (n = 10) 85%
Course Project 15%

Course Logistics

♦Software & Communications

We will use R and RStudio to develop our codes. Recorded classes, additional resources and announcements will be posted on Sakai. We will use a Slack workspace for communication; that way your are just a text message away from instructors and TAs. We will use GitHub to share the lessons, scripts developed in lectures and lab sections as well as assignments.

♦Additional Resources

Wellness

Your mental and physical wellbeing is integral to your ability to be academically successful. Below, we have compiled a list of resources around campus that are available to support you. If there is something going on in your personal life that is preventing you from participating fully in this or other courses, please feel free to speak with any of us. You are welcome to share as much or as little as you are comfortable sharing, and we are more than happy to arrange to get you the support you need.

♦Mental Health Resources

Counseling and Psychological Services: CAPS helps Duke Students enhance strengths and develop abilities to successfully live, grow and learn in their personal and academic lives. We offer many services to Duke undergraduate, graduate, and professional students, including brief individual and group counseling, couples counseling and more. CAPS staff also provide outreach to student groups, particularly programs supportive of at-risk populations, on a wide range of issues impacting them in various aspects of campus life.

Duke Reach: DukeReach directs students, faculty, staff, parents, and others to the resources available to help a student in need. DukeReach is located in the Dean of Students Office and works with departments and groups across campus and in the community, including Housing, CAPS, Student Health, community health providers, the Academic Resource Center, and more.

DuWell: DuWell helps students focus on their individual wellness by looking at the integration of many areas of their life through areas of wellness promotion and risk mitigation.We engage students through a variety of wellness experiences across campus in an effort to reduce stress and anxiety while emphasizing self-care.

We are always available if a student needs someone to listen or to connect them with resources. As employees of Duke, we are mandatory reporters, meaning that if we receive a report of sexual assault, we are required to confidentially report this to the Office of Student Conduct (OSC). The OSC will follow up with the student to provide further information, but the student is not required to respond and the conversation will not be shared beyond ourselves and the OSC. The following resources around campus are are not mandatory reporters: The Women’s Center, medical providers, campus clergy, and CAPS counselors.

Class Etiquette

You should take responsibility for your education. We expect students to attend every class and get to class on time. If you must enter the class late, please do so quietly. Refrain from using phones and tablets for social media during class. Most classes will involve coding. We expect you to focus on the assignment and refrain from any web browsing that may disrupt the progress of your work.

Your classmates deserve your respect and support. We will likely have students from many different backgrounds and countries in this class and you should all feel comfortable and make each other comfortable while participating.

Nicholas School Honor Code

All activities of Nicholas School students, including those in this course, are governed by the Duke Community Standard, which states:

Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and nonacademic endeavors, and to protect and promote a culture of integrity. To uphold the Duke Community Standard:

  • I will not lie, cheat, or steal in my academic endeavors;
  • I will conduct myself honorably in all my endeavors; and
  • I will act if the Standard is compromised.

Policy for the use of Artificial Intelligence (AI) in class work

We acknowledge that AI looks to be a powerful tool in the field of data analytics, something we don’t want to ignore. We therefore allow you to explore how AI can be used in course materials, but with the following constraints:

  • First, don’t blindly submit any AI produced script or text. AI is far from perfect, and you still absolutely need to check its work.
  • Second, in the spirit of learning and transparency, do cite where and when you used AI in generating code and include the prompts used. Failing to do this when you have used AI to supplement your work will be considered a breach of the honor code.

Land Acknowledgment

What is now Durham was originally the territory of several Native nations, including Tutelo (TOOtee-lo) and Saponi (suh-POE-nee) - speaking peoples. Many of their communities were displaced or killed through war, disease, and colonial expansion. Today, the Triangle is surrounded by contemporary Native nations, the descendants of Tutelo, Saponi, and other Indigenous peoples who survived (suh-POE-nee), and Occaneechi (oh-kuh-NEE-chee) Band of Saponi. North Carolina’s Research Triangle is also home to a thriving urban Native American community who represent Native nations from across the United States. Together, these Indigenous nations and communities contribute to North Carolina’s ranking as the state with the largest Native American population east of Oklahoma.”

Final Remarks

The instructors would like to acknowledge that a significant part of this course material was originally developed by Kateri Salk.