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 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.

This course is one of the capstone options in the MS in Information Technology and Analytics. It provides the student with an individual opportunity to implement a solution to a substantial project in the field of Information Technology and Analytics. Students will enter the course having successfully written a proposal for a project that was chosen from a list of possible projects that were crafted by faculty members in the School of Information. Several checkpoint meetings will be held throughout the semester to ensure that students remain on track for project completion. The project culminates in a well-written and professional report documenting the results of the project as well as a high-quality presentation of the project work and its results.

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.