260.706.01
Big Data Skills for Biomedical Scientists

Location
East Baltimore
Term
4th Term
Department
Molecular Microbiology and Immunology
Credit(s)
3
Academic Year
2024 - 2025
Instruction Method
In-person
Class Time(s)
W, F, 3:30 - 5:20pm
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Yes
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Ingo Ruczinski
Ilinca Ciubotariu
Maggie Bartlett
Contact Name
Ilinca Ciubotariu
Contact Email
iciubot1@jhu.edu
Frequency Schedule
Every Year
Prerequisite

It is strongly recommended that interested participants have fundamental experience with statistics including familiarity with hypothesis testing, working knowledge of data visualization and communication, and understanding of basic biological research methods. (example courses include 260.705.81, 140.611, 140.615, or equivalent).

Description
Do you find yourself in need of hands-on, lab-research applicable data science skills for a big data project that just came your way, and which you would like to begin analyzing but don't know how to get started? Then this course is for you! It provides an in-depth, enabling exploration of biomedical data science techniques, applied to the lab-research world. Using examples from infectious disease research, the course helps build capacities in big data analytics and AI/ML for researchers across the biomedical health disciplines.
Acquaints students with the history and development of big data, the end-to-end workflow from data acquisition to storage, and the application of FAIR guidelines for data management. Discusses ethical considerations, insights into reproducibility, and challenges faced in the realm of big data in infectious diseases. Explores fundamental steps for bioinformatic analysis of big data to help students plan their own analyses, visualize results, and communicate findings. Includes FAIR principles with respect to data storage and management guidelines.
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Describe the historical context and development of big data, with specific focus on infectious diseases
  2. Explain the fundamentals of AI/ML and their applications in public health
  3. Apply appropriate big data workflows, from data acquisition to de-identification as needed, curation, storage, processing, and analysis
  4. Examine challenges and fallacies related to preparation of biomedical data for downstream processing and analysis, and those associated with big data, including data privacy and security
  5. Employ FAIR (Findable, Accessible, Interoperable, Reusable) guidelines for data management and storage
  6. Discuss the importance of reproducibility and ethics through the lens of data sharing and analysis
  7. Recognize big data tools that can be used for various data sets
  8. Evaluate published research with respect to the application of big data for disease outbreaks
Methods of Assessment
This course is evaluated as follows:
  • 35% Discussion
  • 30% Peer-feedback
  • 35% Final Project
Enrollment Restriction
none
Instructor Consent
No consent required
Consent Contact
iciubot1@jhu.edu
Jointly Offered With
Biostatistics
Molecular Microbiology and Immunology
Special Comments

This course is in collaboratively co-sponsored by the Department of Molecular Microbiology & Immunology, and the Department of Biostatistics (co-instructor TBN), and offered through the BSPH R3 Center for Innovation in Science Education. Students will not be required to install additional software, nor are there limits on computing environments for this course. Participants may bring their own data to the course, yet this is not a requirement.