School of Information
Golisano College of Computing and Information Sciences
Fall 2231: Monday/Wednesday 11-12, Tuesday/Thursday 10-11
School of Information
Golisano College of Computing and Information Sciences
BS, Ph.D., Rochester Institute of Technology
Published Conference Proceedings
Kang, Jai W., et al. "Analytics Prevalent Undergraduate IT Program." Proceedings of the SIGITE '20: The 21st Annual Conference on Information Technology Education. Ed. Sheridan Communications. Virtual Event, USA: ACM, Web.
Tran, Tuan, et al. "Sentiment Analysis of Marijuana Content via Facebook Emoji-Based Reactions." Proceedings of the IEEE International Conference on Communications 2018, Kansas City, MO. Ed. IEEE. Kansas City, MO: n.p., Web.
Kotak, Chanvi, Brian Tomaszewski, and Erik Golen. "3-1-1 Calls Hot Spot Analysis During Hurricane Harvey: Preliminary Results." Proceedings of the Proceedings of the 15th ISCRAM Conference – Rochester, NY, USA May 2018. Ed. Kees Boersma and Brian Tomaszewski. Rochester, NY: n.p., Web.
Kang, Jai W., et al. "IT Curriculum: Coping with Technology Trends & Industry Demands." Proceedings of the SIGITE’18, October 3-6, 2018, Fort Lauderdale, FL, USA. Ed. ACM. New York, NY: n.p., Web.
Nozaki, Yoshihiro, Erik Golen, and Nirmala Shenoy. "A Modular Architecture for Scalable Inter-Domain Routing." Proceedings of the IEEE Computing and Communication Workshop and Conference, January 9-11, 2017. Las Vegas, NV. Ed. IEEE. New York, NY: IEEE, 2017. Web.
Herlihy, Liam, et al. "Secure Communication and Signal Processing in Inertial Navigation Systems." Proceedings of the IEEE Electronics and Nanotechnology, April 18-20, 2017. Kyiv, Ukraine. Ed. IEEE. New York, NY: IEEE, 2017. Web.
Golen, Erik F., et al. "The GENI Test Automation Framework for New Protocol Development." Proceedings of the 3rd International Conference on Future Network Systems and Security, August 31-September 2, 2017. Gainesville, FL. Ed. Springer. New York, NY: Springer, Print.
Kang, Jai, Qi Yu, and Erik Golen. "Teaching IoT (Internet of Things) Analytics." Proceedings of the ACM SIGITE 2017, October 4-7, 2017. Rochester, NY. Ed. ACM. New York, NY: ACM, Print.
Informatics is about systems that store, process, analyze, and communicate information. Information begins as data – and of particular interest today is the large data sets that are evolving in many fields. Data sets are acted upon by tools can be applied to a variety of problems across many fields. This course provides an overview of issues within informatics, and common solutions. Through hands-on examples, the course demonstrates a general problem-solving approach from problem identification, algorithm selection, data cleaning, and analysis.
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, semi-supervised 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.
This course builds on the principles of computing to introduce students to data analytics techniques commonly performed on digital data sets, using a variety of software tools. Students will learn what constitutes data and its associated social, ethical, and privacy concerns, common data acquisition and preparation techniques, and how to perform exploratory data analysis on real-world datasets from several domains. Common statistical and machine learning techniques, including regression, classification, clustering, and association rule mining will be covered. In addition, students will learn the importance of applying visualization for presenting and analyzing data. Students will be required to demonstrate oral and written communication skills through critical thinking homework assignments and both presenting and writing a detailed report for a project to analyze a data set of their choice. GCCIS majors may take this course only with the students’ home department approval, and may not apply these credits toward their degree requirements.
This course expands the student’s knowledge base of applying higher level programming concepts including data structures, algorithm development and analysis, Big-O notation, directed graphs, priority queues, performance, and a greater understanding of how complex software can more easily be designed. Programming assignments are required.
Rapidly expanding volumes of data from all areas of society are becoming available in digital form. High value information and knowledge is embedded in many of these data volumes. Unlocking this information can provide many benefits, and may also raise ethical questions in certain circumstances. This course provides students with a hands-on introduction to how interactive data exploration and data mining software can be used for data-driven knowledge discovery, including domains such as business, environmental management, healthcare, finance, and transportation. Data mining techniques and their application to large data sets will be discussed in detail, including classification, clustering, association rule mining, and anomaly detection. In addition, students will learn the importance of applying data visualization practices to facilitate exploratory data analysis.
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.
This course provides students with exposure to foundational data analytics technologies, focusing on unstructured data. Topics include unstructured data modeling, indexing, retrieval, text classification, text clustering, and information visualization.
This course introduces students to Visual Analytics, or the science of analytical reasoning facilitated by interactive visual interfaces. Course lectures, reading assignments, and practical lab experiences will cover a mix of theoretical and technical Visual Analytics topics. Topics include analytical reasoning, human cognition and perception of visual information, visual representation and interaction technologies, data representation and transformation, production, presentation, and dissemination of analytic process results, and Visual Analytic case studies and applications. Furthermore, students will learn relevant Visual Analytics research trends such as Space, Time, and Multivariate Analytics and Extreme Scale Visual Analytics.
The student will work independently, under the supervision of one or more faculty advisers, on a topic of mutual interest that is beyond the depth of or not covered in other courses.
An introduction to the Linux operating system and scripting in high-level and shell languages. The course will cover basic user-level commands to the Linux operating system, followed by basic control structures, and data structures in both high-level and shell languages of choice. Examples will include interfacing with the underlying operating system and processing structured data. Students will need one year of programming in an object-oriented language.