Course Syllabus
BIOL 708L/ NACS 728B: Quantitative Analysis of Biological Data
Section 0101
Fall 2021
Course Information
| Lectures | Tu & Th 2:00 - 3:15 PM, PLS 1162 |
| Lab | M 9:00 - 10:50 AM, BPS 1208 (starts 8/30!) |
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Required Text |
(none) |
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Recommended Text |
No specific book, but I highly recommend finding an introductory python book (or website) that fits your particular comfort level. |
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Required Software |
Anaconda, which includes python & jupyter notebook (your python version should be 3.7 or later). See also the course page Installing Anaconda, Jupyter, and Python. |
| Prerequisites | There are no formal prerequisites beyond calculus, but willingness to learn both mathematical techniques and some programming is essential. Basic calculus, complex numbers, and related concepts will be reviewed. |
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Course Description |
Methods of analysis for time series and other data, including spatial data. Analysis methods include signal processing, statistics, and simple modeling. Python programming is taught and used throughout the course. Topics include data smoothing, Fourier/frequency analysis, spectrograms, and bootstrap error estimation. |
Other Course Pages
Instructor Info
| Instructor | Jonathan Z. Simon, Professor | ||||||||
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Office Hours
| Day | Time | Location | |
|---|---|---|---|
| Jonathan Simon | Monday | 1-3PM PM | AVW 2145 BPS 3227 or by Zoom (please confirm Zoom first by email) |
Outline (subject to change)
Introduction & Basics Introduction & mathematical review Basic python Signal Processing Data filtering: low-pass filters / smoothing Data filtering: high-pass filters Detrending Frequency & Fourier transforms Improving signal to noise ratio Choosing and designing filters Spectrograms & time-frequency analysis Power Spectral Density (tentative) Computational Statistics Bootstrap Permutation Tests Statistics for Circular or Periodic Data (if time allows) Hands-on Data Analysis BYOD (bring your own data) Computer Skills (simultaneously with rest of course) Data analysis & simulation with python
Labs
There is a weekly lab for you to try out newly-learned modeling concepts on your computer. Lab reports should be turned in by uploading them electronically on ELMS at the end of each lab session. The labs are essentially graded Pass/Fail: 100% for a strong report, 50% for a weak report, 0% for no report.
Whenever we are done with Monday morning class in BPS 1208, we need to move the chairs and tables back to their normal positions.
Homework
Math is a “Learn it By Doing it” subject, so the homeworks are important.Typically, homework problems will be assigned every week. It is possible that only some of the problems will be graded.
Show your work. For me, your methods are more important than your results. For problems in which you use a calculator or computer, you still need to explain your methods.
All homework assignments are on ELMS, and all completed homework assignments should be turned in on paper (not uploaded), at the beginning of class on their due date.
Final Project
There are no exams in this course, only a final project. The final project is to use one or more of the concepts taught in this course and to apply them to real biological data. You are encouraged to use your own data (or data from your lab), but that is not possible we will find a different source.
Laptop Computers
It is required that you bring a computer to all class meetings (and especially lab), and that you have working versions of python and jupyter notebook (or equivalent) on that computer. Both software packages are free. It is OK to not have installed this software before the first class meeting.
Python & Jupyter Notebook
Python is a widely-used, powerful, freely available programing language (you will need version 3.7 or greater). Jupyter notebook is a way of using python interactively that makes it more pleasant and productive. If you already have these installed on your laptop, you are good to go. If not, we are setting aside time during our first lab meeting for installation & debugging (installation can be frustrating if it does not go well the first time). Most users will find that the easiest, most predictable, way to install both is via the anaconda package. See also the course page Installing Anaconda, Jupyter, and Python.
Grading
20% Homework 40% Labs 40% Final project
COVID-19 Measures
As members of the UMD community, we care about keeping each other safe and healthy—which includes reducing unintended spread of COVID19 among ourselves and those we encounter. Toward this end, Prince Georges County and UMD health guidelines currently (as of August 6, 2021) require that all campus members, regardless of their vaccination status, must wear face coverings over the nose and mouth while indoors. This is critically important when spending time indoors in classrooms. President Pines provided clear expectations to the University about wearing face coverings indoors.
To ensure community health and safety, I ask that you follow this mandate. Per UMD policy, students who arrive to class not wearing a mask will be notified and given a choice between putting on a mask and remaining in class or leaving the classroom immediately. Students who have additional issues with the mask expectation after a first warning will be referred to the Office of Student Conduct for failure to comply with a directive of University officials.
If the Prince Georges County-wide and UMD indoors mask mandates are lifted during this semester, there is still a requirement that unvaccinated students with approved exemptions must wear face masks at all times while indoors (as well as undergo COVID19 testing twice per week). This requirement is designed to protect these students’ health, and the same classroom policy will apply.
The university is hoping to track and trace cases via QR codes attached to desks or placed in small rooms. Please scan these with your phone at the start of every class. This is the way contact traces will be able to identify potential close contacts in the event of a COVID19-positive test. There will be a two stage notification process - near contacts are required to be tested; there will be general announcements to such a class to monitor their own health/symptoms even if they have not been identified as a close contact, and to be tested if they have a concern.
It is important to call the health center HEAL line (301-405-4325) to report any COVID19 positive test.
CourseEvalUM
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Academic Honesty
Discussing homework problems, labs, assignments, and other ideas, with others is encouraged,
but,
your final write-up must be your own work and cannot be a copy of anyone else's work.The University of Maryland, College Park has a nationally recognized Code of Academic Integrity, administered by the Student Honor Council. This code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student you are responsible for upholding these standards for this course. It is also important to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit https://academiccatalog.umd.edu/undergraduate/registration-academic-requirements-regulations/academic-integrity-student-conduct-codes/.
Academic dishonesty includes copying homework answers from another‘s work, from previously written solution sets, from any book, from the web, or any other related source. Instances of academic dishonesty will be referred to the Office of Judicial Programs.
Learning Assistance Service
If you experience difficulty in keeping up with the academic demands of a course, you should know about the Learning Assistance Service, 2202 Shoemaker Building, 301-314-7613, or http://www.counseling.umd.edu/LAS. The educational counselors can help with time management, reading, math learning skills, note-taking and exam preparation skills. All their services are free to UMD students.
Course Summary:
| Date | Details | Due |
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