To learn more about the course, please refer to the latest course syllabus:
Course Description
This course provides experience in experimental design, data management, data exploration, and statistical methods. Students learn best management practices for collecting, storing, and sharing data, including metadata. This course introduces students to the R programming language in the context of performing exploratory data analysis, visualization, and statistical methods commonly used in the field.
Course Format
During a typical week, this course follows a regular format, with a seminar meeting on Monday of each week and a data activity on Wednesday of each week. In practice, data analysis often requires researching new ideas and asking advice of colleagues. Therefore, there are no exams in this class. Grading consists entirely of participation in in-class discussions and activities, completion of online assignments, and successful completion of a semester-long group project. (Graduate students enrolled in ENV 522 complete an individual project.) Class time is set aside throughout the semester for students to work with their groups on the term project.
Course Goals
By the end of the semester students will:
- Test a hypothesis in the natural sciences.
- Use existing data sources to answer research questions.
- Collect and manage data using best practices.
- Understand and evaluate statistical results.
- Select appropriate statistical methods to analyze complex data sets.
- Be proficient in conducting statistical analysis and data visualization in R.
Selected final project posters - Spring 2024
Selected final project posters - Spring 2023
Final project posters - Spring 2022
R Workshops
All R workshops from this class are available at the link below. Material in many these workshops borrows heavily from the excellent tutorials in "Data Analysis and Visualisation in R for Ecologists," developed by the Data Carpentry team, also linked below. Please feel free to reach out to request any data used for these workshops!
What students say about the course...
“Very helpful discussions on data collection, analysis and presentation that I feel is very important information for anyone going into the field.”
“I like the way the labs are set up and we can go our own speed. I learned a lot and can say this was one of the best classes I've taken here. This course should have a follow up.”
"I think this course should be mandatory for people who want to go to grad school."
"Dr. Alldred is so extremely knowledgeable on this topic. She is always happy to help with labs and is so kind and patient when trouble shooting. She never made me feel less intelligent, even though the subject is not my strength."
"I was really nervous about R labs before this class but Mary makes learning R easy and fun."
"Mary helped me understand and definitely prefer using R-studio more to work on datasets."
"I definitely feel more confident with the program R, and data analysis in general."
"Fantastic, really helped me understand the nitty-gritty of coding with no stress or pressure."
"Mary gave great feedback on all assignments, but I especially liked that I got to strengthen my thesis proposal in this class and her feedback definitely helped me do that."
"Mary explained concepts well in lecture and discussions so as to make them more easily understandable. She created all of the tutorials and assignments herself that we worked on in class, so she knew how to solve any possible issues"
"Mary made modules to teach us the program R for each week, and she was very good at answering any questions or issues with code people were facing."
"The workload throughout the semester felt like a relatively constant amount, never too much or too little, and we were always given a sufficient amount of time to complete assignments."
"This course has definitely increased my knowledge about the subject matter. I like how Mary gave us assignments each week so we wouldn't feel overwhelmed with the things we have to learn and can take in the material so much easier."
"Mary is great and is always willing to help without making you feel self-conscious for having trouble. She is super knowledgeable and understanding, and she did a great job of getting everything switched over to being online this semester"