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 first course introducing students to the fundamentals of computational problem solving. Students will learn a systematic approach to problem solving, including how to frame a problem in computational terms, how to decompose larger problems into smaller components, how to implement innovative software solutions using a contemporary programming language, how to critically debug their solutions, and how to assess the adequacy of the software solution. Additional topics include an introduction to object-oriented programming and data structures such as arrays and stacks. Students will complete both in-class and out-of-class assignments.

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.

A presentation of the fundamental concepts and theories used in organizing and structuring data. Coverage includes the data modeling process, basic relational model, normalization theory, relational algebra, and mapping a data model into a database schema. Structured Query Language is used to illustrate the translation of a data model to physical data organization. Modeling and programming assignments will be required. Note: students should have one course in object-oriented programming.

This course provides students with exposure to foundational concepts and technologies for non-relational data management. Topics include an overview of data types, structuring and processing data and knowledge, data transformation, and data storage. Students will work with non-traditional (NoSQL) data stores to manage large datasets in the context of specific problem scenarios, gaining practical experience with data modeling, querying, and scalability considerations. Web application development exercises are required.