Curriculum

Micro-Credential Course Information

Course Sequence

Each course combines instruction with case study application in preparation for the industry workforce in advanced computing. Completion of all courses is mandatory to be considered for the paid internship.

Course Descriptions

Please note that instructors may adjust their course descriptions.

FUNDAMENTALS OF SPATIAL COMPUTING  

1. Introduction to Spatial Data Science (M.L. Shyu or G. Scott) 

This is the first COESC course. The objective of the aspects of data science through a data science project lifecycle, such as accessing, cleansing, modeling, visualizing, and interpreting data. Students will perform hands-on learning of data analytic topics, using technologies such as Python, R, and open-source analytic tools.  

2. Geospatial Data Engineering (G. Scott) 

This course provides an overview of theoretical and practical issues encountered when working with geospatial data within the data science lifecycle. Data access, indexing, retrieval, and other technical concepts are investigated. Core issues in geospatial data storage, management, exploitation, and multi-data set entity resolution / correlation are examined. 

HUMAN-COMPUTER INTERACTIONS  

The AI-AR/VR Nexus: Unity Programming for Spatial Computing (Y. Lee) 

The objective of this course is to provide students with a foundational understanding of how Artificial Intelligence (AI) and Machine Learning (ML) can be applied in spatial computing with Augmented Reality (AR) and Virtual Reality (VR) environments. Students will also gain hands-on experience with Unity programming for AR/VR applications, integrating AI/ML techniques to enhance spatial computing experiences.  

SPATIAL COMPUTING DESIGN  

Advanced AI/ML Techniques for Spatial Computing with AR/VR (P. Calyam) 

This micro-credential course explores advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques for spatial computing in Augmented Reality (AR) and Virtual Reality (VR) environments. Participants will delve into advanced AI/ML algorithms, including deep learning and reinforcement learning, and learn how to apply them in AR/VR applications. The course emphasizes practical implementation and provides hands-on experience with AI/ML tools and frameworks.  

SPATIAL DATA ANALYSIS  

Spatial and Geostatistical Analysis (C. Shyu) 

This course will provide a practical overview of key issues encountered when working with and analyzing spatial data as well as an overview of major spatial analysis approaches. Discussions and laboratory work will focus on implementation, analysis, and interpretive issues given constraining factors that commonly arise in practice. 

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