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Data Science for Public Health I

East Baltimore
3rd term
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Class Times:
  • Tu Th,  8:30 - 9:50am
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Brian Caffo

Prior programming experience, pre-calculus mathematics


Presents the basics of data science primarily using the python programming language. Teaches basic unix, version control, graphing and plotting techniques, creating interactive graphics, web app development, reproducible research tools and practices, resampling based statistics and artificial intelligence via deep learning, focusing on practical implementation specifically tied to computational tools and core fundamentals necessary for practical implementation. Culminates with a web app development project chosen by student (who will come out of this course sequence well-equipped to tackle many of the data science problems that they will see in their research).

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Demonstrate proficiency in data-oriented python programming
  2. Implement and demonstrate proficiency in data software including pandas, sql and the tidyverse
  3. Implement plotting and interactive graphics tools on novel data sets
  4. Implement artificial intelligence programs on novel data sets
  5. Create a web application
  6. Implement resampling-based statistics
  7. Synthesize concepts of machine learning overfitting
  8. Synthesize concepts of probabilistic inference
Methods of Assessment:

This course is evaluated as follows:

  • 66% Homeworks/coding projects
  • 33% Final capstone project

Instructor Consent:

No consent required

Special Comments:

Please note: This is the in-person section of a course that is also offered virtually/online. Students will need to commit to the modality for which they register.