Computer programming is a critical skill for students interested in analyzing complex datasets. This course is part of a series of courses designed to cross-train life science graduate students in computational techniques for genomics data analysis.

Upon successful completion of this course students will:

• Understand basic programming constructs and their implementation in Python
• Know Python data structures (lists, dictionaries, tuples)
• Write simple programs for analysis and visualization of genomic data

Lectures: Tuesdays and Thursdays, 10-11:50 am in Yates 306. Because of physical distancing measures in place due to COVID-19, students will be split into two sections and each section will receive in-person instruction on one of the two days.

Linux as a computational platform (DSCI 510) or a familiarity with UNIX/Linux and working from the command line.

We will use the free textbook Python for everybody (py4e).

Your grade for this course will be based on weekly assignments and a final exam. The percentages are as follows:

• Assignments: 75%
• Final exam: 25%

The calculation of the final letter grade will be made as follows:

• A 90 - 100%
• B 80 - 89.9%
• C 70 - 79.9%
• D 60 - 69.9%
• F below 60%

NOTE: You are welcome to work with others on the problem sets but all assignments and the exam are to be done independently.

CSU is committed to advancing an inclusive university culture. Visit the Diversity Resources Page for services and support available to you. To learn more about diversity at CSU, visit The Office of the Vice President for Diversity .

Mental health has a tremendous impact on your success and wellbeing. There are several resources available at CSU to help you cope with mental and emotional health issues. Please visit CSU Health Network. If you have needs that hold you back from achieving your full potential in this course, please reach out to the instructor.

You are expected to be familiar with the CSU Student Code of Conduct. This course will adhere to the CSU Academic Integrity Policy.