Class Meetings
To be arranged. We will meet for 2.5 hrs each week
Instructor
Matthew Toomey
Office: 305 Oliphant Hall, enter OH 304 then first door on right (305)
Email: mbt6332@utulsa.edu
Phone: (918) 631-2206
Office Hours: By appointment, in-person meetings or video conferences
Course description
The aim of of this course is to introduce students to a variety of tools, mostly centered around R, to handle computational and data presentation tasks that we commonly encounter in biology. The aim of this seminar is develop an approach to handling data and analyses in efficient ways that can easily be shared with others in your field.
Pre-Requisites
- A personal laptop computer that you bring to all lecture and lab meetings
- Mac or Windows, with battery power to last 2.5 hours
- all required software packages installed and working
- wireless access and an active TU mail account
- Graduate standing as a PhD or MS student at TU
- Undergraduates by invitation only after consultation with the instructor
- Knowledge of basic statistics (p-values, means, variances, hypothesis testing)
Course Learning Outcomes
At the conclusion of this seminar students will::
- Understand how to use freely available tools to make their research analyses and communication more efficient and attractive.
- Establish repeatable workflows for data and computationally intensive analyses.
- Consider design principles and advantages and limitations different types of data visualization.
- Develop skills in critical reading of primary literature and the effective presentation of primary research.
Readings (recommended)
Seminar Structure
We will meet for one long session each week. For the first half of the semester, I will start each session with an introduction and walk through of a tool with the group. I expect you to follow along and run the processes/code on your own computer. Once this introduction is complete the remainder of the session will be available to complete homework assignments and work on class projects. The second half of the semester will be student led and in each session two members of the class will present their lessons/projects to the group.
Assignments
Participation Attendance is expected at our weekly meetings and I expect you to follow along with the class presentation running processes/code on your own computer.
Homework There will be short assignments to give you practice using these tools introduced in the seminar. You will “turn in” assignments by posting them in in html format to your public repository and website on GitHub.
Lesson Each student will develop a lesson and teach the class about an R package or specifc data visualization that they have or will apply to their own research. This will include an introductory lecture/walk through, a homework exercise for the class, and a brief presentation using the tools to analyze and interpret their own data (or publically avialable data).
Grading
| Item | Total points | |
|---|---|---|
| Participation | - | 100 |
| Homework | 10 x 10 points | 100 |
| Lesson | 1 x 100 points | 100 |
| Grand total | - | 300 |
The grading scale will be as follows: 89.50-100% - A, 79.50-89.49% - B, 69.50-79.49% - C, 59.50-69.49% - D, < 59.50% - F. No exceptions.
Class Schedule
Please refer to the course website for an up-to-date schedule
Masking
The class masking policy will follow the CDC recommendations published here: https://www.cdc.gov/coronavirus/2019-ncov/your-health/covid-by-county.html
If the CDC recommends indoor masking we will follow this recommendation and you will be expected to wear a mask in class. If you have a medical condition that prevents you from wearing mask, please reach out to Student Access to seek a medical waiver. Students who refuse to wear masks will be referred to the Dean of Students.
Academic misconduct
All instances of academic misconduct will follow the Policies and Procedures Relating to Academic Misconduct of Undergraduates for the College of Engineering and Natural Sciences for graduate students
Any evidence of misconduct will result in a score of zero, but students may also be dismissed from the course and automatically assigned a grade of F.
Student Access and Success
All students are encouraged to familiarize themselves with and take advantage of services provided by The Student Success Team, including Student Access, Student Success Coaching, and tutoring. To request a student success coach to improve study skills, email successcoaches@utulsa.edu. To request a tutor, email tutoring@utulsa.edu.
Students who have or believe they may have a disability and would like to set up accommodations should contact Student Access within the Student Success Team to discuss their needs and facilitate their rights under the Americans with Disabilities Act and related laws.
Know your Title IX
Sexual misconduct is prohibited by Title IX of the Educational Amendments of 1972 (“Title IX”) and will not be tolerated within the TU community. For more information about your rights under Title IX, please visit our Policies and Laws page https://utulsa.edu/sexual-violence-prevention-education/policies-laws/ on the TU website or contact the Title IX Coordinator at 918-631-2321.