Jai Kang Headshot

Jai Kang

Associate Professor

School of Information
Golisano College of Computing and Information Sciences

585-475-5362
Office Hours
Monday & Wednesday 2-4 PM Register in advance for this meeting: https://rit.zoom.us/meeting/register/tJcucOmsqjstHNFzmfC0Yfsl8CVLP_cC-cTI
Office Location

Jai Kang

Associate Professor

School of Information
Golisano College of Computing and Information Sciences

Education

BS, Seoul National University (South Korea); MA, Kent State University; MS, Georgia Institute of Technology; Ph.D., State University of New York at Buffalo

585-475-5362

Areas of Expertise

Select Scholarship

Published Conference Proceedings
Kang, Jai W., et al. "Analytics Prevalent Undergraduate IT Program." Proceedings of the ACM Special Interest Group in IT Education Conference (SIGITE’20), October 7-9, 2020, Virtual Event, USA. ACM, New York, NY, USA. Ed. Sheridan Communications. New York, NY: Association for Computing Machinery, 2020. Web.
Kang, Jai W., et al. "Complementing Course Contents Between IT/CS: A Case Study on Database Courses." Proceedings of the ACM Special Interest Group in IT Education Conference (SIGITE’19), ), October 3-5, 2019. Ed. George Grispos and Daniel Bogaard. Tacoma, WA: ACM (Association for Computing Machinery), 2019. Web.
Kang, Jai W., et al. "IT Curriculum: Coping with Technology Trends & Industry Demands." Proceedings of the SIGITE '18 The 19th Annual SIG Conference on Information Technology Education , October 3–6, 2018, Fort Lauderdale, FL, USA. Ed. Bryan S. Goda and Dan Bogaard. New York, NY: ACM, Web.
Kang, Jai W., Qi Yu, and Erik Golen. "Teaching IoT (Internet of Things) Analytics." Proceedings of the ACM Conference on IT Education/IT Research SIGITE\'17, October 4—7, 2017, Rochester, NY, USA. Ed. Dan Bogaard and Tom Ayers. New York, NY: ACM (Association for Computing Machinery), 2017. Web.
Kang, Jai W., et al. "Security Requirements Embedded in MS Programs in Information Sciences and Technologies." Proceedings of the ACM SIGITE’16 (the 17th Annual Conference on Information Technology Education). Ed. Cindy Edwards. New York, NY: Sheridan Printing, Web.
Kang, Jai W., et al. "Web-Based Implementation of Data Warehouse Schema Evolution." Proceedings of the Asian Conference on Computer and Information Science and Engineering Kuala Lumpur, Malaysia December 18-19, 2014. Ed. Dariusz Barbucha, Ngoc Thanh Nguyen, John Batubara. Kuala Lumpur, Malaysia: Springer International Publishing, 2015. Print.
Kang, Jai W., Edward P. Holden, and Qi Yu. "Pillars of Analytics Applied in MS Degree in Information Sciences and Technologies." Proceedings of the ACM SIGITE 2015 16th Annual Conference on IT Education. September 30-October 3, 2015. Chicago, IL. Ed. Amber Settle & Terry Steibach. New York, NY: ACM, 2015. Print.
Kang, Jai W., Edward P. Holden, and Qi Yu. "Design of an Analytic Centric MS Degree in Information Sciences and Technologies." Proceedings of the 15th Annual Conference on Information Technology Education and The 3rd Annual Conference on Research in Information Technology, October 15-18, 2014, Atlanta, Georgia. Ed. Amber Settle and Terry Steinbach. New York, NY: ACM, 2014. Print.

Currently Teaching

ISTE-434
3 Credits
This course covers the purpose, scope, capabilities, and processes used in data warehousing technologies for the management and analysis of data. Students will be introduced to the theory of data warehousing, dimensional data modeling, the extract/transform/load process, warehouse implementation, and summary-data management. The basics of data mining and importance of data security will also be discussed. Hands-on exercises include implementing a small-scale data warehouse.
ISTE-724
3 Credits
This course covers the purpose, scope, capabilities, and processes used in data warehousing technologies for the management and analysis of data. Students will be introduced to the theory of data warehousing, dimensional data modeling, the extract/transform/load process, warehouse implementation, dimensional data analysis, and summary data management. The basics of data mining and importance of data security will also be discussed. Hands-on exercises include implementing a data warehouse.
ISTE-612
3 Credits
This is the second course in a two-course sequence that provides students with exposure to foundational information sciences and technologies. Topics include internet middleware technologies, data and text analytics, and information visualization. Note: One year of programming in an object-oriented language, a database theory course, a course in Web development, and a statistics course is needed.
ISTE-600
3 Credits
This course provides students with exposure to foundational data mining techniques. Topics include analytical thinking techniques and methods, data/exploring data, classification algorithms, association rule mining, cluster analysis and anomaly detection. Students will work individually and in groups on assignments and case study analyses.
DSCI-633
3 Credits
A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semisupervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course.