Skip Navigation

Concentration in Data Science and Analytics for Environmental Health and Engineering (MSE)

This concentration emphasizes innovative computational, statistical, and “big data” tools with applications to environmental problems in air pollution, energy systems, hydrology, and climate change.

Degree Program Requirements

A minimum of 30-credits including no more than the following counting toward the degree: 
 1 credit of seminar
 1 credit of intersession course work
 1.5 credits of Center for Leadership Education (with advisor approval)
 6 credits of independent research
At least 50 percent of the required 30 credits must come from courses within EHE (WSE and JHSPH) 
Application of up to two classes with a grade of "C" (no grades of "D" or "F" may be applied) 
Five to six required courses and four to five recommended electives
depending on track. To substitute an alternate course for a recommended elective, students must receive written approval from their adviser (this should be in the form of an email sent to the Academic Program Administrator).
 
Prerequisites for the MSE program include mathematics through differential equations and computing skills. 
Advanced Academic Programs (AAP) or Engineering for Professionals (EP) courses may be taken and counted to receive a master's degree as long as your adviser agrees that there is sufficient rigor. Students must have written consent from their adviser (an email is sufficient) prior to signing up for the course.  
The Whiting School of Engineering strongly discourages Master's students from using 300-level courses to count towards the required number of Master's graduation credits. Exceptions to this rule should be reviewed on a case-by-case basis by the department. No more that two 300-level courses can be used to count toward the 30 Master's-level credits required  for graduation. Advisers must provide an email to the student and Academic Program Administrator stating:  
 Indicating the 300-level course has been reviewed and deemed to have acceptable rigor, and
 Where applicable, identify the name and course number of the class the 300-level course will replace (this will be rare).

Course Requirements

 Course NumberCourse NameSemester Offered (always confirm with course catalog)

Data Science Foundations
(2 courses)

   
The following two courses are recommended:   
 EN.570.616Data Analytics in Environmental Health and EngineeringSpring
 EN.570.654Geostatistics: Understanding Spatial DataSpring

Students can also take the following courses to fill this requirement:

   
 EN.553.620Introduction to ProbabilitySpring
 EN.553.626Introduction to Stochastic ProcessesSpring
 EN.553.630Introduction to StatisticsSpring
 AS.270.654Environmental Data Analysis 
 AS.180.334EconometricsSpring
Environmental Foundations
(3 courses)
   
Students interested in air pollution and climate should consider the following courses:   
 EN.570.657Air PollutionSpring
 PH.182.615Airborne Particles1st or 3rd term
 PH.180.607Climate Change and Public HealthSummer
 AS.270.679Atmospheric Science 
 AS.270.641Present and Future Climate 
 AS.270.618Remote Sensing of the Environment 
Students interested in hydrology and water resources should consider the following courses:   
 EN.570.351Introduction to Fluid MechanicsFall
 EN.570.653HydrologySpring
 EN.570.647Hydrologic Transport in the EnvironmentSpring
 EN.570.651Environmental Transport and DispersionSpring
 EN.570.643Aquatic and Biofluid ChemistryFall
 AS.270.618Remote Sensing of the Environment 
Students interested in energy systems should consider the following courses:   
 EN.570.607Energy Policy and Planning ModelsSpring
 EN.570.697Risk and Decision AnalysisFall
Students interested in health applications should consider the following courses:   
 PH.185.621Methods in the Exposure Sciences 
 PH.182.613Exposure Assessment Techniques for Health Risk Management3rd term

Advanced Data Science (2 courses)
Students should take two additional courses in statistics, applied math, or computing. Graduate-level courses in the following departments may fulfill this requirement:

Data Science Project (3 credits)
This requirement is waived if students are conducting master's thesis research for credit.