Geospatial Analytics Concentration
This concentration enables MS Data Science students to develop advanced skills and expertise in geospatial science and technology. Incorporating Geographic Information Systems (GIS), remote sensing, and location-based data allows data scientists to uncover spatial patterns. The concentration provides a foundation across the breadth of geospatial technology to prepare data for analysis, perform suitability analysis, spatial predictive modeling, geostatistics, and space-time pattern mining and object detection. The concentration coursework (12 credits) incorporates advanced geovisualization and webmapping technology to also enhance cartography analytics and communications.
Admission
The requirements for admission to the Master of Science in Data Science and Analytics are as follows:
- A baccalaureate degree in computer science, electrical and/or computer engineering, mathematics, statistics, information system & technology, or a related field from a regionally-accredited institution or an equivalent institution outside the U.S.; students holding a bachelor's degree in an unrelated field will need competency in topics related to basic statistics and computer science.
- Current scores on the Test of English as a Foreign Language (TOEFL) of at least 230 on the computer-based TOEFL or 79 on the TOEFL iBT, or IELTS 6.5 overall.
Students with previously completed work at a regionally-accredited institution may submit a request for a maximum of 12 elective graduate credit hours to be transferred into the program. If approved by the admission committee, it will be added to the transcript.
Curriculum Requirements
The program requires 30 credit hours. A capstone project or thesis is required.
Geospatial Analytics Concentration
Course List
| Code |
Title |
Credit Hours |
| Introduction to Data Science and Analytics | |
| Data Analytics and Big Data | |
| Data Visualization | |
| Probability Models for Data Science and Analytics | |
| Advanced Statistical Concepts in Data Science | |
| Fundamentals of Interpretable Machine Learning and Explainable AI |
| GEOG 600 | Geospatial Data Analysis | 3 |
| GEOG 601 | Spatial Statistics and Modeling | 3 |
| 3 |
| Programming for Data Science | |
| Spatial Analysis of Coastal Environments | |
| Marine Geography | |
| Internet Geographic Information Systems | |
| Advanced GIS | |
| GIS Programming | |
| Geographic Information Systems for Emergency Management | |
| Applied Cartography/GIS | |
| Topics in Geography | |
| Geospatial Machine Learning for Environmental Applications | |
* | |
| Data Science Capstone Project (3 credits) | |
| Thesis Research (6 credits) ** | |
| Total Credit Hours | 30 |
