140.711.01
Advanced Data Science I
Location
East Baltimore
Term
1st Term
Department
Biostatistics
Credit(s)
3
Academic Year
2022 - 2023
Instruction Method
In-person
Tu, Th, 1:30 - 2:50pm
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
The course is designed for PhD students in the Johns Hopkins Biostatistics Masters and PhD programs and assumes significant background in statistics. Specifically it is assumed you know the basics of statistics through generalized linear models, you know how to fit and interpret models, you know the basics of R and Python, and you can use version control with Github.
Teaches how to organize the components of a data analysis – statistics, data manipulation, and visualization. Teaches how to produce a complete data analysis to answer a targeted scientific question. Focuses on synthesis, communication, ethics, and interpretation of data analytic products.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Critique a data analysis and separate good from bad analysis
- Produce a complete data analysis
- Produce the components of a data analytic paper
- Produce the components of a statistical methods paper
- Produce the components of a data analytic presentation for technical and non-technical audiences
- Identify key issues in data analytic relationships
Methods of Assessment
This course is evaluated as follows:
- 99% Homework
Final grade applies to all terms
Enrollment Restriction
Enrollment restricted to Biostatistics 2nd-year PhD and 2nd-year master's students only
Please note: This is the onsite section of a course that is also offered virtually. Students will need to commit to the modality for which they register.