Instructor

Timothy Warren
tim.warren AT oregonstate.edu

Course Assistants

Nate Davidson (Head) getzm AT oregonstate.edu

Vini Karumuru karumurv AT oregonstate.edu

Divyansh divyans AT oregonstate.edu

Course Information

• Class Meetings: Tuesday/Thursday 10-11:20am, ALS 4000
• Optional in-person Help Session: Wednesday 3-4:30, Peavy Forest Sciences 125
• Additional Help/Office hours on Zoom: Thursday 6-7:30, or by appointment
• Course Web site (Weekly calendar) https://https://bds311.github.io/
• Canvas site (Problem sets) https://canvas.oregonstate.edu/courses/1876848
• Zoom link: See Canvas Site.

Course Description

Explores theory and practice behind widely used computational methods for biological data analysis. Covers principles of programming for reproducible research as well as computational techniques for testing hypotheses, inferring dataset parameters, and making predictions from biological data.

Prerequisites

Some prior programming experience (BDS 310, CS 161)

Class assumes some knowledge of UNIX shell and Python numpy and matplotlib packages. There will be opportunities in first few weeks for students to catch up on these skills if necessary.

The prerequisites can be waived with instructor permission. I strive to make the class accessible and engaging for students with varied backgrounds and interests.

Course Objectives

At the completion of this course, students will be able to:

• Organize, visualize, and perform quantitative analysis of large biological data sets.
• Apply data science principles to real-world data sets
• Use Python as a tool for scientific computing
• Simulate and study random processes using resampling methods
• Infer and predict data relationships
• Understand version control and other methods for open, reproducible research
• Design and implement algorithms to address canonical problems in biology
• Solve problems in groups via active collaboration

Topics (Also see weekly calendar)

• Analyzing tabular data with the Python package Pandas
• Visualizing biological data
• Introduction to version control and reproducibility with Git repositories
• Random processes: description and simulation
• Resampling methods for hypothesis testing
• Estimating data parameters via the bootstrap
• Testing and predicting data relationships: regression and correlation
• Optimization, Classification, and Introduction to Machine Learning

Grading

• 85% Problem Sets. Each assignment will be normalized to count equally.
• 10% In-class quiz (Date TBD; Likely in week 7)
• 5% Participation. Full credit given for attendance at lecture and/or homework help sessions.

Learning Resources

Inferential Thinking online textbook
BDS 310 Course notes - Jupyter Notebook
[BDS 311 Course notes - To be added]
Python and Pandas for Data Analysis, McKinney
Weekly references at Weekly Calendar

Homework Submission; Late Policy

• Initial Homework assignnments will be submitted via the Jupyter Hub accessed through the Course Canvas Page. Launch the DataScience Hub from Canvas.
• Your homework will be in /home/hub_data_share/hw directory. Homework will be posted one week before assignment due (typically Fridays at 11:59 pm.) To complete homework, copy the specific assignment folder (e.g. hw01 to your personal directory (e.g. since my username is warrenti, I would put hw01 in /home/warrenti/hw))
• We will use Gradescope to submit assignments. Be sure to save your notebook - preserving the original name (e.g. hw01.ipynb), then run the last grader.export() cell, which will produce a zip file, which you can download onto your local machine and then upload this file at appropriate location on Gradescope, which can be accessed via Canvas, or at this link
• Each student will receive one grace period to submit homework up to 72 hours late (e.g. assignment due Thursday at 11:59 would be due Sunday.) Any other late homework submissions, or submissions beyond 72 hours after deadline, will be considered at instructor discretion and receive 50% deduction.

Course Code of Conduct (Adopted from Carpentries)

We all should strive to foster a welcoming, supportive environment. This involves:

• Using welcoming and inclusive language
• Respecting different viewpoints
• Accepting constructive criticism
• Treating each other with grace and courtesy

Collaboration and Academic Dishonesty

I encourage (and will sometimes require you) to work with others on homework problems. But the work that you submit for the homework and final project must be your individual solution, implemented by you (never directly copied from other students or other sources). Rather than copying someone else’s work, please ask for help!

Statement Regarding Students with Disabilities

Accommodations for students with disabilities are determined and approved by Disability Access Services (DAS). If you, as a student, believe you are eligible for accommodations but have not obtained approval please contact DAS immediately at 541-737-4098 or at http://ds.oregonstate.edu. DAS notifies students and faculty members of approved academic accommodations and coordinates implementation of those accommodations. While not required, students and faculty members are encouraged to discuss details of the implementation of individual accommodations.

Diversity Statement

Oregon State University strives to create an affirming climate for all students including underrepresented and marginalized individuals and groups. Diversity encompasses differences in age, color, ethnicity, national origin, gender, physical or mental ability, religion, socioeconomic background, veteran status, sexual orientation, and marginalized groups. We believe diversity is the synergy, connection, acceptance, and mutual learning fostered by the interaction of different human characteristics. Oregon State University strives to respect all religious practices. If you have religious holidays that are in conflict with any of the requirements of this class, please contact the instructor immediately so that we can make alternative arrangements.

Resources for Support

If you encounter difficulties and need assistance, it’s important to reach out. Consider discussing the situation with an instructor or academic advisor. Learn about resources that assist with wellness and academic success at https://counseling.oregonstate.edu/reach-out-success. If you are in immediate crisis, please contact the Crisis Text Line by texting OREGON to 741-741 or call the National Suicide Prevention Lifeline at 1-800-273-TALK (8255).

Opportunities for Student Evaluation

The online Student Evaluation of Teaching system opens to students the Wednesday of week 8 and closes the Sunday before Finals Week. Students will receive notification, instructions and the link through their ONID. They may also log into the system via Online Services. Course evaluation results are crucial as they help improve courses and the learning experience of future students. Responses are anonymous (unless a student chooses to “sign” their comments agreeing to relinquish anonymity) and unavailable to instructors until after grades have been posted. The results of scaled questions and signed comments go to both the instructor and their unit head/supervisor. Anonymous (unsigned) comments go to the instructor only.

Additionally, there will be amidterm course evaluation at the end of week 5.