Quantitative and computational methods are increasingly essential to all sub-disciplines of modern biological research. The goal of this intensive week-long course is to equip students with the fundamental skills to apply these methods and connect them to resources to further develop their knowledge and abilities. The class starts at 9am (please arrive by 8:45am), and formal instruction ends at 3pm daily. The course demonstrates the importance of version control, documentation, testing, and other methods for enhancing reproducibility, reliability, and usability of software. This is achieved through live coding sessions and use of learning exercises, where for the majority of the class, students perform data analysis to address biological questions and reinforce core bioinformatic concepts. Upon completing the course, students will be comfortable using and writing software to work with large-scale biological data. The resulting computational and statistical competence will prepare students for courses, rotations, thesis research, and careers.
Tues, Sept 5, 2023 - Fri, Sept 8, 2023
8:45 A.M. ET - 5 P.M. ET, In Person, UTL 398
Lunch and an afternoon snack will be provided every day.
Although the course formally starts at 9 am ET, please arrive by 8:45 am.
| Instructors | |
|---|---|
| Rajiv McCoy | rajiv.mccoy@jhu.edu |
| Michael Sauria | msauria1@jhu.edu |
| Frederick Tan | tan@carnegiescience.edu |
| Teaching Assistant | |
|---|---|
| Andrew Bortvin | abortvi2@jhu.edu |
| Sara Carioscia | scarios1@jhu.edu |
| Merlin Li | mli163@jhu.edu |
| Matthew Isada | misada1@jhu.edu |
Due to the interactive nature of this course, students should not participate in any other meetings, courses, or lab work during the week. If a student foresees attendance conflicts, they should contact a TA immediately so that this policy can be communicated to the necessary parties.
Please be on time for course activities. Your attendance is expected at all times. Please let a TA know about any emergencies, family responsibilities, illness, etc. that may prevent attendance, and we will work to accommodate you. If you feel sick (any kind if illness, not just a positive COVID test), please don’t come to class ; contact a TA and we will make arrangements to support you.
http://bxlab.github.io/cmdb-quantbio/
Upon completion of this course, students will be able to:
A brief online textbook written for this course can be found here. Any lecture notes, slides, and interactive coding scripts/notebooks will be made available on the course website.
The course is broken into four main types of sessions
Students will complete a daily reflection and submit via Google Forms by 5pm ET. These reflections allow students to communicate their experience and purposefully reflect. These are meant to be check-ins for both the instructors/TAs and the students, and these will be kept confidential. The content of the reflections will not affect a student’s grade in any way.
We expect and encourage you to ask questions and request help throughout this course. Everyone is learning and regardless of your past experience we will expect you to have questions, request clarification or details on in-class information, and ask for help debugging your code. Please ask questions about whatever, whenever you need to; this course is for you, and we look forward to supporting you in your learning.
Googling is always an acceptable way to find answers or help, and we encourage you to utilize it extensively. If you adopt a solution following a Google search, make sure you understand what you incorporate, rather than just copy/paste without comprehension of the logic or code. Google is also a good way to learn more about any error messages you encounter in your code. You may be familiar with ChatGPT and other large language models.
You may be familiar with ChatGPT and other large language models. After trying each problem/assignment/task on your own, if you’re still running into issues, feel free to use ChatGPT as you would any other online resource (Google, stack overflow, etc.). Learning how to succinctly describe exactly what you want to accomplish is a skillset in itself, so this can be good practice. If you find code that seems to work (e.g., from Google) but you’re not sure how exactly it works, you can also type it into ChatGPT and ask it to explain what’s happening. As always, please do not submit any code if you are not familiar entirely with how it works; flag it and ask a TA for assistance. Be aware that ChatGPT might confidently offer an answer that is not correct; so always check the output on your own.
The grading for this course is based on reasonable completion. For each assignment, students will be told which exercises are required and which are optoinal. To demonstrate completion and get TA feedback, students will upload certain scripts and results (outputs or plots, etc.) to their personal Github repositories, qbb2023-answers.
TAs will verify that the student’s submitted work shows a reasonable level of individual effort.Students will have one week post-bootcamp to submit any leftover materials, though we encourage you to complete these assignments ASAP to ease your workload as you move on to your other courses and rotations. Letter grades will be assigned in line with the level of completion. While student presentations and the content of daily reflections will not affect grades in any way, failure to turn in daily reflections has the potential to lower the final grade.
General guidelines for letter grade assignments:
TAs and instructors will push individualized feedback to the student repositories throughout the week. This feedback is not a grade, but may be used by students to anticipate their level of completion. After receiving feedback, students will have the opportunity to revise and complete each assignment (ideally once) prior to final submission at the end of the semester.
Academic and scientific institutions and research depend on honesty and integrity. You should complete your own lunch and homework exercises, and your work should not plagiarize others – including group partners, presenters, and strangers posting to online forums or blogs. You and your partners should be working together, but both persons should be writing and turning in unique, individualized code. Note your scripts and analysis may follow the same logic steps and even have tidbits of the same code, but no one person should be writing the solution the whole group uses character for character. Additionally, you should understand every line of code you write, are given and use, or find online and incorporate. If asked to, you should be able to explain exactly what your code is doing. Another aspect is properly acknowledging the source of borrowed code. Understanding can be cultivated and acknowledgement implemented by writing both inline and multiline comments (which is a terrific practice in general). Relatedly, don’t give someone code to copy and paste. Make sure any recipient can explain back to you any gifted code. See the University’s guidelines on plagiarism for details and contact a TA with any questions.
We are committed to maintaining a welcoming, inclusive, and harassment-free environment for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), political beliefs/leanings, or technology choices. We do not tolerate harassment in any form. This code of conduct applies to all course participants, including instructors and TAs, and to all modes of interaction and communication.
All class participants agree to:
Several platforms will be utilized for communication and distribution of information. These include GitHub, Slack, Google Forms, etc. Instructions and walkthroughs are provided for students to sign up for accounts through GitHub and Slack. If for any reason you do not have access to one of these accounts, please reach out to a TA.
We provide laptops with pre-configured software and data. Students must sign a Macbook Pro Agreement Form upon pickup of the laptop. Note these laptops are to be returned to the Biology office by the end of the spring semester (date specified on the agreement form). Software issues should be communicated to TAs promptly. Hardware issues should be communicated to the Biology office ASAP, but also let a TA know so we can accommodate you while the hardware is being addressed.