Typical Four-year Outline: Environmental Data Science B.S. (Environmental Statistics Focus)

The typical four-year outline for Michigan Tech's Environmental Data Science bachelors's degree with a environmental statistics focus provides a semester-by-semester guide to core courses, labs, and requirements.

The Interactive Degree Audit is the official method for tracking the completion of your specific degree requirements. Please be aware that there are multiple ways for students to progress through this major. This is just one sample and adjustments may be required due to curriculum changes. Students should work with their advisor to develop their individual plan. A full list of undergraduate course descriptions is available.

First Year

Spring Semester
Course Prerequisites Credit
CH 1150 University Chemistry I MA 1031 (Concurrent) or higher or Placement 3
CH 1151 University Chemistry I Lab CH 1150 (Concurrent) 1
CS 1122 Intro to Programming II CS 1121 3
DATA 1200 Data Science w/ Python MA 1030 (Concurrent) or higher 3
MA 1135 Calculus for Life Sciences Placement 4
Essential Education – Foundations in the Human World   3
Total   17

Second Year

Fall Semester
Course Prerequisites Credit
FW 2060 Environmental Sustainability   3
FW 3020 Forest Ecology FW 2010 (Concurrent), FW 2051 (Concurrent) 3
CS 2311 Discrete Structures CS 1121 and MA 1135 3
MA 2720 Statistical Methods MA 1030 or higher or placement 4
Essential Education – SHAPE   3
Essential Education – Activities for Well-being and Success   1
Total   17
Spring Semester
Course Prerequisites Credit
FW 3200 Biometrics & Data Analysis MA 2720 4
FW 3540 Intro to GIS FW 2051 & MA 2720 4
CS 2321 Data Structures CS 1122 3
MA 2160 Calculus w/ Technology II MA 1135 4
MA 2330 Intro to Linear Algebra MA 1135 3
Total   18

Third Year

Fall Semester
Course Prerequisites Credit
CS 3425 Intro to Database Systems CS 2321 3
DATA 2201 Foundations of Data Science DATA 1200 & CS 1122 & MA 2330 3
MA 3720 Probability MA 2160 3
Ethics Directed Elective   3
Essential Education – Arts & Culture   3
Total   15
Spring Semester
Course Prerequisites Credit
MA 3740 Statistical Programming & Analysis MA 2720 3
Essential Education – Intercultural Competency   3
Essential Education – Communication Intensive   3
Essential Education – Experience   3
Essential Education – Activities for Well-being and Success   1
Total   13

Fourth Year

Fall Semester
Course Prerequisites Credit
FW 4800 Communications for Natural Resources   2
MA 4710 Regression Analysis MA 2720 3
Statistics Directed Elective   3
Database Directed Elective or Free Elective   3-4
Essential Education – Activities for Well-being and Success   1
Total   12-13
Spring Semester
Course Prerequisites Credit
FW 4500 Env. Data Sci. Capstone   3
MA 4720 Design & Analysis of Experiments MA 2720 3
Statistics Directed Elective   3
Database Directed Elective or Free Elective   3-4
Total   12-13

Grand Total: 120 Credits

Essential Education Requirements (37 total credits)

Required courses are:

  • a Math course (3 credits),
  • a Natural and Physical Science course (3 credits),
  • STEM courses (6 credits),
  • a Foundations in the Human World course (3 credits),
  • Composition UN 1015 (3 credits),
  • a Communication Intensive course (3 credits),
  • an Intercultural Competency course (3 credits),
  • an Essential Ed Seminar course (UN 1013 or UN 2013 or major-specific, 1-3 credits),
  • an Arts and Culture course (3 credits),
  • a SHAPE elective (3 credits)
  • an Essential Education Experience course (3 credits),
  • and 3 credits of Activities for Well-being and Success.

Up to five Essential Education requirements and the Michigan Tech Seminar may be shared (double-counted) with major requirements. Students should work with their advisor to determine which major requirements may satisfy Essential Education requirements.

For specific courses, reference Essential Education on the Registrar's website.

Statistics Electives

Choose One From Each Set:

Set 1

Course Credits Semester Prerequisites
MA 4730 Nonparametric Statistics 3 Fall-odd Years MA 2720
MA 4780 Time Series Analysis and Forecasting 3 Spring MA 2720 and MA 3720

Set 2

Course Credits Semester Prerequisites
MA 4790 Predictive Modeling 3 Fall MA 3740
EET 4501 Applied Machine Learning 3 Spring SAT 4310 or SAT 4650

Database Electives

  • Choose one:
Course Credits Semester Prerequisites
CS 4801 Foundations of Machine Learning 3 Fall DATA 2201 and MA 2720
CS 4811 Artificial Intelligence 3 Fall, Spring CS 2311, CS 2321, CS 3425, and MA 3720 
CS 4821 3 Spring

CS 3425, MA 2330, and MA 2720

Additional Notes

  • Prerequisite (pre-req) course must be successfully completed PRIOR to taking the subsequent course.
    • Concurrent Prerequisites (concurrent noted by (C) or ©) may be taken at the same time, although it is not necessary if the prerequisite course is completed first.
      • Required Corequisite (co-req) courses that MUST be taken together in the same semester.
      • Class Standing: So (Sophomore Standing earned 30 credits) Jr (Junior Standing earned 60 credits), Sr (Senior Standing earned 90 credits)
      • Semester Offered: Noted under electives with F (Fall), Sp (Spring), Su (Summer) or On Demand (no anticipated offering but may show in the schedule, consult with your advisor)
  • Math: Students are placed into an initial math course based on ACT/SAT math score, the online ALEKS assessment, or a math placement exam score for credit (AP, IB, CLEP).
  • Transfer, Advanced Placement, or study abroad courses are not included in credit hours used for GPA calculations. Transfer credit is awarded for Michigan Tech equivalent coursework only if a grade of ‘C’ or better (2.00/4.00) or equivalent is earned at a transfer institution. Study abroad credit will be awarded based on passing a course according to equivalent international standards. Advanced Placement credit is awarded according to published AP Exam score standards (also IB and CLEP).

This degree plan is not an official list of degree requirements. Adjustments may be required due to curriculum changes.