Syllabus

Recommended References: (1) Mark Lutz, “Learning Python” 5th ed. O’Reilly 2013. (2) Frank Jensen, “Introduction to Computational Chemistry” 3rd ed. Wiley 2016

Time: 10:30-11:45 am, T/R

Place: Room 130, Hamilton Hall

Schedule

This may change as the course progresses. I will email out anytime it changes.

Lecture	Date	Description
Lecture 01	Aug 27	How to Python, Python basics I: number and strings, intro to control statements
Lecture 02	Aug 29	Python basics II: lists, dictionaries and looping
Lecture 03	Sep 3	Python basics III: functions, how to comment code
Lecture 04	Sep 5	Python basics IV: file IO and modules
Lecture 05	Sep 10	Python basics V: objects and classes
Lecture 06	Sep 12	Python basics VI: misc topics
Lecture 07	Sep 17	maintaining code with github, using new python packages
Lecture 08	Sep 22	testing code with unittests
Lecture 09	Sep 24	Scientific Python packages: Numpy and Scipy part I
Lecture 10	Sep 26	Scientific Python packages: Numpy and Scipy part II
Lecture 11	Oct 1	Pandas package: data organization and processing part I
Lecture 12	Oct 3	Pandas package: data organization and processing part II
Lecture 13	Oct 8	Plotting with Python Matplotlib and Seaborn part I
Lecture 14	Oct 10	Plotting with Python Matplotlib and Seaborn part II
Lecture 15	Oct 15	Other useful python packages part I
Lecture 16	Oct 17	Other useful python packages part II
Lecture 17	Oct 24	Simulation Techniques Overview
Lecture 18	Oct 31	Molecular dynamics (MD) and setting up gromacs jobs
Lecture 19	Nov 5	Analyze MD runs
Lecture 20	Nov 7	Density functional theory (DFT) and setting up guassian jobs
Lecture 21	Nov 12	Analyze DFT runs
Lecture 22	Nov 14	Molecular docking and setting up autodock jobs
Lecture 23	Nov 19	Analyze docking runs
Lecture 24	Nov 21	Protein folding nd Rosetta protein design
Lecture 25	Nov 26	Analyze Rosetta protein design runs
Lecture 26	Dec 3	Work / plan final project
Lecture 27	Dec 5	Work / plan final project
Lecture 28	Dec 12	Presenting final project (10 min presentation)
Lecture 29	Dec 14	Presenting final project (10 min presentation), and free pizza

Grading

Grades will be based on a combination of attendance, class participation (asking questions, demonstrating reading the material), class assignments, final project presentation, and the final project. Most days will have an in-class assignment where we work through a problem together. It is required that you do the reading before hand! Bringing a computer is required, if you do not have a laptop please contact me.

Class assignments will be given in class, everyone will have until the next class to turn it in. The goal is to have everyone finish in class, if I see this is not happening I will adjust the difficulty. Everyone will be paired into groups, only one assignment is required to be turned in for both people.

Class assignments will be graded in the following scheme

Assessment	Points (out of 100)
Does the code work	60
variable/function names	10
commenting	10
efficiency	10
testing	10

Everyone is required to read the material before class, if I see you are not doing this you will lose points in participation.

It is okay to use code you find online as part of your projects and assignments but you must cite where you got it from (web adress is fine), and you still have to explain how it works. If you do not cite it you will recieve 0 for the assignment.

example of citing code

# opens and writes text to a file and was taken from the below address:
# https://stackoverflow.com/questions/48959098/how-to-create-a-new-text-file-using-python/48964410
file = open("copy.txt", "w") 
file.write("Your text goes here") 
file.close() 

Grading scheme

Grading type	Percent of grade
Attendance	15 %
Participation	10 %
Class Assignments	45 %
Final Project	20 %
Final Prject Presentation	10 %

Grades

There is no curve, everyone can get an A.

Grade	Percent out of 100 required
A	> 80
B	70
C	60
D	50
F	< 50