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140.628.01
Data Science for Public Health I

Location:
East Baltimore
Term:
3rd term
Department:
Biostatistics
Credits:
4 credits
Academic Year:
2021 - 2022
Instruction Method:
Synchronous Online
Class Times:
  • Tu Th,  8:30 - 9:50am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Contact:
Brian Caffo
Resources:
Prerequisite:

Prior programming experience, pre-calculus mathematics

Description:

Presents the basics of data science using the R 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 R programming
  2. Practice basic data cleaning in R
  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
  9. 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