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223.711.01
Application of Quantitative Data Analysis Methods

Location
East Baltimore
Term
3rd Term
Department
International Health
Credit(s)
3
Academic Year
2025 - 2026
Instruction Method
Hybrid In-person and Synchronous Online
Class Time(s)
Wednesday, 10:30 - 11:50am
Friday, 10:00 - 11:50am
Auditors Allowed
No
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Nadia Akseer
Emily Keats
Contact Name
Nadia Akseer
Frequency Schedule
Every Year
Prerequisite
140.621, 140.622, 140.623, or 140.651, 140.652, 140.653 or permission from the instructor
Enrollment Restriction
MSPH students in their second year
Description
Will you be embarking on a research project that requires quantitative data analysis? Are you eager to apply your knowledge of analysis methods to your dataset? Do you want support with undertaking descriptive or intermediate/advanced analyses in R/STATA (or another statistical package)?
Discusses data management techniques (e.g. cleaning, outliers) and implementation. Applies descriptive and intermediate quantitative data analysis using large-scale data from low-and middle-income settings or from global agencies. Examines the application of quantitative methods (including their associated strengths and limitations) with a focus on how these are influenced by varied LMIC settings and underserved populations. Supports second-year MSPH students to conduct their own analysis on test datasets in R/STATA/others.
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Describe the general steps to take and determine appropriate data analysis methods when answering different research questions
  2. Clean, manage, and undertake basic data exploration and descriptive analyses in R, STATA or another statistical package
  3. Use selected methods in R or STATA or another statistical package to create descriptive and intermediate data analyses of large-scale data from low and middle-income settings or from global health agencies to illustrate and interpret a public health priority
  4. Interpret results, formulate key messages, and construct tables and figures to display and visualize findings
  5. Critique method(s) used, in terms of strengths and limitations
Upon successfully completing this course, students will be able to:
Methods of Assessment
This course is evaluated as follows:
  • 10% Participation
  • 30% Lab Assignments
  • 15% Presentation(s)
  • 45% Final Project
Instructor Consent
Consent required for some students
Consent Note
2nd-year MSPH GDEC students do not need consent. Those from other programs who have met the prerequisites and have enough prior data analysis experience will need the instructor's consent and will only be allowed into the class if space allows.
Consent Contact
nakseer1@jhu.edu
Special Comments

This course will include a weekly 2 hour lab that will consist of brief student presentations on their progress and structured independent working time with student-instructor support