Course Structure

Through this course graduate students are eligible to receive credits for successfully completing the QCB Collaboratory Workshops.

Workshops are offered quarterly with the exception of summer. Each workshop runs for 2-3 days, 2-3 hours per day, and are held weekly. Attendance is mandatory for all days.

Course Description

Through this course graduate students are eligible to receive credits for successfully completing the QCB Collaboratory Workshops.

Workshops are offered quarterly with the exception of summer. Each workshop runs for 2-3 days, 2-3 hours per day, and are held weekly. Attendance is mandatory for all days.

To receive course credit for 275A students must register for at least 3 workshops taught during the quarter, and 3 additional workshops to also receive credits for 275B. If the course if full please request a PTE by emailing matteope@gmail.com

The workshops introduce graduate students to contemporary methods and techniques in bioinformatics that are used to analyze high-throughput genomic data. Workshop topics include Introduction to the Unix, NGS data analysis, R Programming, BS-Seq, RNA-seq, Python, Single-Cell RNA-seq Analysis among others.

For a complete list of workshops, click here: https://qcb.ucla.edu/collaboratory/schedule-of-workshops/

How to Succeed in this Course (Expectations for Students)

The students are expected to learn to use the informatics tools in the lab portion of the course. While they will support from instruction and classmates, this work is largely conducted independently.

Collaboratory Interactive Workshop Overview

Grading

How Your Learning Will Be Assessed (Grading Policy)

The final grade will be determined by participation (50%) and the final exam (50%).

Why I do not grade on a curve: In recent years, research into higher education assessment practices have shown that grading on a curve can create unnecessarily competitive environments for students and result in outcomes that disadvantage some groups of students over others. This is true in data collected and analyzed for our students at UCLA as well. For this reason, I do not grade on a curve. Your grade is therefore not based on how you did in comparison to your peers, but instead how successful you are at evidencing that you have mastered the intended learning goals for that specific assessment. However, if I do find that particular assessment questions I gave an assignment or exam were unreasonably challenging, unclear, or unfair for any reason I will  provide additional credit as appropriate. If you ever feel that an assignment or specific question is unfair or confusing please come and speak with me or your TA (ideally before it is due or during the assessment, but afterwards is okay also) so that we can address this concern as soon as possible. I am committed to making sure the assessment of your learning is comprehensive, fair, and incorporates best practices from education research on assessment design and inclusive practices.

Grading Scale: (Modify as appropriate)

Letter Grade Percentage
A+ 99-100%
A 93%-98.9%
A- 90%-92.9%
B+ 87%-89.9%
B 83%-86.9%
B- 80%-82.9%
C+ 77%-79.9%
C 73%-76.9%
C- 70%-72.9%
D 60%-69.9%
F 0%-59%


Applied Bioinformatics Lab Syllabus

For the M275A and M275B Syllabus – Fall 2020 quarter, please click M275AB-Fall-2020