The Geodata Science for Professionals (GDSP) is a non-thesis Professional Master’s program. Students enrolled in the program can not receive teaching, research, or other graduate assistantships with fee remissions from Purdue by university regulation. Instead, students are anticipated to be supported by their employers, governments, endowed scholarships, or themselves the whole time in the program. We try to match our program fees and tuition to the Purdue standard Graduate/Professional tuition published by the Office of the Bursar, which is subject to change.
The program aims to train a highly competitive workforce that can harness geoscience (such as weather, climate, geophysical, and environmental) data for decision-support in public and private sectors. Targeting audience with STEM background, the program offers core courses that teach analysis and computing methods with geoscience data, foundational geoscience content knowledge, and data-driven applications in geoscience applications complementary to the technical areas of data science (statistical theories and models, statistical and machine-learning methods, algorithms for statistical and machine-learning methods as well as optimization, and computational systems for data analysis).
In order to acquire sufficient trainings in these areas while gaining relevant work experience, GDSP students need to complete a minimum total of 31 credit hours, including 27 credits of coursework, 1 credit of seminar, and 3 credits of internship in industry or an applied research experience. The estimated time for completion for the fulltime MS students is 1.5 years. It is possible for Purdue EAPS students with BS degrees to finish this program in 1 year (Fall, Spring, and Summer semesters). The maximum allowable time for completion is four (4) years.
To be admitted to the program, students must satisfy all current EAPS Graduate Application Requirements. In addition, they are required to show on their transcripts that they have completed coursework equivalent to 3 semesters of calculus up through vector calculus, a class in linear algebra/differential equations, one semester of programming (C, Java, Python, and/or Fortran), and a class in statistical methods. Students slightly short of pre-requisites can make up for no more than 3 credits in the first year.
To graduate, students are required to take a minimum of 31 credits of courses in following Categories 1–6.
Category 1. Data-science Core Courses. Take minimum 2 (6 credits) in Table 1.
Purpose: Enable students to start constructing their own data-driven models for EAPS problems by teaching them what lies behind the methods.
|Course #||Course Title|
|EAPS 50700||Introduction to Analysis and Computing with Geoscience Data|
|EAPS 50801||Geographic Information Systems|
|EAPS 51000||Time Series Analysis for Geosciences|
|EAPS 51500||Geodata Science|
|EAPS 65700||Geophysical Inverse Theory|
Category 2. Foundational Core Courses. Take minimum three courses (9 credits) in Table 2.
Purpose: Ensure students’ knowledge proficiency in the domain science and train their ability to incorporate data science with other scientific paradigms such as theories, experiments, and computation in their specific area of specialization in the Earth, Atmospheric and Planetary sciences. A data-science core course not used to fulfill the Category 1 core requirement could be used to fulfill the requirement here.
|Course #||Course Title|
|EAPS 50701||Geospatial Data Analytics|
|EAPS 52000||Theory of Climate|
|EAPS 52100||Atmospheric Chemistry|
|EAPS 52300||Radar Meteorology|
|EAPS 52500||Boundary Layer Meteorology|
|EAPS 52600||Intro Geofluid Dynamics|
|EAPS 52700||Ecosystem Ecology|
|EAPS 52900||Modeling Ecosystems and Biogeochemical Cycles|
|EAPS 53200||Atmospheric Physics I|
|EAPS 53300||Atmospheric Physics II|
|EAPS 53600||Introduction to General Circulation|
|EAPS 53900||Mesoscale Meteorology|
|EAPS 55700||Introduction to Seismology|
|EAPS 57700||Geologic Remote Sensing of the Planets|
|EAPS 59100||Integr. Global System Modeling|
|EAPS 59100||Isotope Hydrology|
|EAPS 59100||Karst Geomorphology|
|EAPS 59100||Stable Isotopes|
|EAPS 59100||Physics and Mechanics of Earth Materials|
|EAPS 59100||Planetary Habitability|
|EAPS 63000||Atmospheric Remote Sensing|
|EAPS 68000||Contaminant Hydrogeology|
|AAE 52300||Introduction to Remote Sensing|
|CE 571||Earthquake Engineering|
Additional/alternative courses as approved by the GDSP organizers on an individual basis. EAPS offers qualified 500-600 level courses every year.
Category 3. Applied Courses. Take minimum two courses (6 credits) in Table 3.
Purpose: These are domain-relevant courses aimed to build concepts and procedural knowledge in specific and practical application areas and teach state-of-the-art computational and/or data analysis techniques, in order for students to acquire expertise in evaluating the results of analysis for decision-support.
The applied areas include (but not limited to) weather and climate risk assessments, environmental remote-sensing applications, Geographical Information System (GIS) applications, etc. Courses not listed in this table may be approved by the program director, especially if they are already in the GIS Certificate program.
|Course #||Course Title|
|EAPS 53000||Extreme Weather and Climate: Science and Risk|
|EAPS 55900||3D Seismic Interpretation and Visualization|
|EAPS 59100||Introductino to Reflection Seismology|
|EAPS 59100||Environmental Data Model Assimilation|
|EAPS 59100||Laboratory Analysis|
|EAPS 59100||Space & Planetary Instrument|
|EAPS 59100/CE 59700||Geospatial Modeling and Analysis|
|EAPS 59100||Weather Simulation & Forecast with Cloud Computing|
|AAE 57500||Introduction to Satellite Navigation and Positioning|
|ABE 65100||Environmental Informatics|
|AGRY 54500||Remote Sensing of Land Resources|
|ASM 54000||GIS Applications|
|CE 54900||Computational Watershed Hydrology|
|FNR 55800||Digital Remote Sensing and GIS|
|FNR 57400||Big Data, AI, and Forests|
|ILS 59500||Geospatial Data Science with ArcGIS and Python|
|PHYS 567||Observational Techniques in Astronomy|
Additional/alternative courses as approved by the GDSP steering committee.
Category 4. Computational and Statistical Elective Courses. Take minimum two (6 credits) from Table 4.
Purpose: Approved elective courses can help broaden and enhance students’ practical knowledge base and skill sets in data science. Students can consider getting a Computational Science & Engineering Certificate with these electives and others.
The CS&E faculty representative at EAPS is Dr. Wen-wen Tung (contact: wwtung at purdue dot edu).
Alternatively, students can consider further study and receive a Graduate Certificate in Applied Statistics after consulting with a GDSP advisor.
|Course #||Course Title|
Introduction to Computational Science
|CS, STAT, &
MA Data Science
Five-week Online 1-Credit Modules for Data Science Foundational Topics
|ECE 53800||Digital Signal Processing I|
|MA 51100||Linear Algebra|
|MA 527/52800||Advanced Mathematics for Engineers and Physicists I/II|
|MA 59800||Machine Learning and Uncertainty Quantification for Data Science|
|ME 57900||Fourier Methods in Digital Signal Processing|
|PHYS 58000||Computational Physics|
|STAT 51100||Statistical Methods|
|STAT 51200||Applied Regression Analysis|
|STAT 51300||Stat Quality Control|
|STAT 51400||Design of Experiment|
|STAT 51600||Basic Probability Appl|
|STAT 51700||Statistical Inference|
|STAT 52700||Introduction to Computing for Statistics|
|STAT 69500||D&R with DeltaRho for Big Data & High Computational Complexity (cannot be take with EAPS 51500)|
Additional courses as approved by the GDSP steering committee.
Category 5. Internship Experiences
Students register in 3 credits of EAPS 59100 (Advanced Topics in Earth and Atmospheric Sciences) to fulfill the internship requirement, completed with a written MS Project Report for each internship. The credit hours can be broken into 1 credit of off-campus internship in a semester and 2 credits of written MS Project Report in the following semester.
Students are encouraged to seek internship opportunities through Purdue Office of Professional Development, Purdue Weather and Climate Risk Internship Program, and Purdue Data Mine "Data Science in Industry I and II".
Category 6. EAPS 59100 Geodata Science (GDS) Seminar
Students are required to register for one (1) credit of the GDS Seminar and orally present the results of their internship or applied research experience. (This is in addition to their written MS Project Report).
Responsible Conduct of Research
Graduate students who meet the definition of Researcher must complete the general online CITI RCR training within 60 days of enrollment.