Skip to main content

Data Science for Public Health II

4th Term
Academic Year
2022 - 2023
Instruction Method
Synchronous Online
Class Time(s)
Tu, Th, 8:30 - 9:50am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year

140.628, prior programming experience, precalculus

Presents the basics of data science 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. Practice basic data cleaning in pythnon
  3. Implement and demonstrate proficiency in tidyverse commands
  4. Implement plotting and interactive graphics tools on novel data sets
  5. Implement artificial intelligence programs on novel data sets
  6. Create a web application
  7. Implement resampling-based statistics
  8. Synthesize concepts of machine learning overfitting
Methods of Assessment
This course is evaluated as follows:
  • 66% Homeworks/coding projects
  • 33% Final Capstone Project
Special Comments

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