Alfa Heryudono, PhD

Application of knowledge discovery and data mining tools and techniques to large data repositories or data streams. This project-based capstone course provides students with a framework in which students gain both understanding and insight into the application of knowledge discovery tools and principles on data within the student's cognate area. This course is intended for data science majors only.

Application of knowledge discovery and data mining tools and techniques to large data repositories or data streams. This project-based capstone course provides students with a framework in which students gain both understanding and insight into the application of knowledge discovery tools and principles on data within the student's cognate area. This course is intended for data science majors only.

Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.

Topics in high performance computing (HPC). Topics will be selected from the following: parallel processing, computer arithmetic, processes and operating systems, memory hierarchies, compilers, run time environment, memory allocation, preprocessors, multi-cores, clusters, and message passing. Introduction to the design, analysis, and implementation, of high-performance computational science and engineering applications.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources. Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system. Oral and written reports and ethical aspects are highlighted.

Written presentation of an original research topic in Data Science which demonstrates the knowledge & capability to conduct independent research. The thesis shall be completed under the supervision of a faculty advisor. An oral examination in defense is required.