585.771.81 - Biomedical Data Science

Applied Biomedical Engineering
Fall 2024

Description

Biomedical Data Science is a course designed to equip students with the basics of data science tools for data engineering, data analysis, and building artificial intelligence models in python.

Expanded Course Description

Biomedical Data Science is a course designed to equip students with the basics of data science tools for data engineering, data analysis, and building artificial intelligence models in python.

The core book files can be found here: https://smart-stats.github.io/ds4bio_book/book/ and a new, but under progress, version of the book can be found here: https://smart-stats.github.io/ds4bio_book/qbook/_book/index.html The latter version includes both introductory and advanced material not covered in the class.


The slides can be found here: https://github.com/smart-stats/ds4bio_book/tree/main/slides/ds4ph.


All assignments are due Tuesdays at 11:59PM. Assignments are to be handed in using github classroom (we'll cover git in the class). Projects are all equally rated. In addition students are expected to prepare a final project of their choosing where they create a web app and 5 minute video. The grade breakdown is

70% weekly projects
30% final project and video

Instructors

Default placeholder image. No profile image found for Brian Caffo.

Brian Caffo

Default placeholder image. No profile image found for Ahmed Hassoon.

Ahmed Hassoon

Default placeholder image. No profile image found for Jason Lee.

Jason Lee

Default placeholder image. No profile image found for Babak Moghadas.

Babak Moghadas

bmoghad1@jhu.edu

Course Topics

The course will cover the following topics:

  1. Git and github
  2. Basic python programming
  3. Modern python usage
  4. Data cleaning in python
  5. Exploratory data analysis
  6. SQL via sqlite
  7. Basic R, calling R from python, calling python from R, Tidyverse
  8. Interactive graphics
  9. Webscraping
  10. App development with streamlit
  11. Binary classification
  12. Regression
  13. Logistic Regression
  14. Basic neural networks with pytorch
  15. Convolutional neural networks.
  16. Advanced neural networks

Required Software

Students will need to
1. Create a google account and use google colab 
2. Obtain a local copy of python and use vscode or jupyter lab
3. Use another jupyter lab cloud provider, like paperspace

In addition, students will need a web browser and a github account.

Grading Policy

All assignments are due Tuesdays at 11:59PM. Assignments are to be handed in using github classroom (we'll cover git in the class). Projects are all equally rated. In addition students are expected to prepare a final project of their choosing where they create a web app and 5 minute video. The grade breakdown is

70% weekly projects
30% final project and video

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.