- Course: NEU 365P (56259) Programming & Data Analaysis in Modern Neuroscience
- Semester: Fall 2022
- Location: WEL 2.306
- Time: MWF 11:00AM - 12:00PM
- Alexander Huth
- office hours: TBD
- office: NHB 3.134 / online
- email: huth@cs.utexas.edu
- Suna Guo
- office hours: TBD
- email: sguo19@utexas.edu
This course will help you learn how to build and run statistical and computational analyses of neuroscience data using Python and other general-purpose programming libraries.
Topics:
- Version control (git)
- Programming in python (basic data types, etc.)
- Visualization (matplotlib)
- Basic probability theory
- Sampling & probability distributions
- Hypothesis testing
- Confidence intervals & bootstraps
- Regression
- Classification
- Clustering
By the end of this course, you should be able to load and analyze multiple types of data, and create useful data visualizations.
Class will meet three times a week (MWF 11am-12pm) in WEL 2.306. Class will also be live-streamed on Zoom, with links posted to Canvas. Classes will contain a mix of lectures, demonstrations, and labs, during which students will work on code during class. Zoom recordings will be available via Canvas. Each Monday will feature a short in-class quiz. Homeworks and quizzes will be submitted electronically.
For this class you will need frequent access to a computer that can run Python. None of the analyses that we will be doing will be very intensive, so this does not need to be a modern or "fast" computer. Still, it will need to be running a standard operating system like Windows, Mac OS X, or Linux. Unfortunately, tablets running mobile operating systems (iOS, Android) probably won't work for this purpose. If this is an issue for you, please get in touch with the professor as soon as possible so that we can try to figure out a solution.
Course materials (lecture slides, lecture demo notebooks, lab notebooks, homework assignments, etc.) will be available on the course GitHub page (https://github.com/alexhuth/pdamn-fa2022) and Canvas.
Class Recordings: Class recordings are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.
Read the course materials. Ask questions if any topics are unclear. Be respectful of each other, the professor, and the TA. Have fun! :)
This page serves as the syllabus for this course. This syllabus is subject to change; students who miss class are responsible for learning about any changes to the syllabus.
The course has two textbooks, both of which are online and free:
Additional required readings will be made available for download from this repository.
There will be a take-home, open book final exam. There will be no midterm exam.
There will be 6 homework assignments. Assignments will be posted as the semester progresses.
There are several components to the class grade.
- Homeworks (60%): There will be 6 homework assignments. Each assignment is worth 10% of your grade.
- In-class quizzes (15%): A short (10 minute) in-class quiz will be given every Monday, covering the material from the previous week. The lowest quiz grade will be dropped, and the rest will be weighted equally.
- Final exam (25%): There will be a take-home, open book final exam.
Homework is due by the start of class on the noted due date. Homework must be turned in on the due date in order to receive full credit. Homework turned in less than 1 week late will be accepted but the score will be penalized by 10%. Homework later than 1 week will not be accepted without exceptional circumstances.
Late homework will be accepted under exceptional circumstances (e.g., medical or family emergency) and at the discretion of the instructor (i.e. exceptional denotes a rare event) with no penalty. This policy allowing for exceptional circumstances is not a right, but a privilege and courtesy to be used when needed and not abused. Should you encounter such circumstances, simply email assignment to instructor and note "late submission due to exceptional circumstances". You do not need to provide any further justification or personally revealing information regarding the details.
You are encouraged to discuss problem sets with classmates and work on them together, but all written submissions must reflect your own, original work. If you worked with other students on a problem set, please include their names in a statement like "I worked on this homework with XX and YY" on the assignment. If in doubt, ask the instructor. Acts like plagiarism represent a serious violation of UT's Honor Code and standards of conduct:
http://deanofstudents.utexas.edu/sjs/scholdis_plagiarism.php
http://deanofstudents.utexas.edu/sjs/conduct.php
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