Courses2016-2017

For the college’s course catalog, please visit the Courses section. For courses currently offered, please visit the Schedule of Classes.

Many of the most pressing problems in the world can be addressed with data. We are awash in data and modern citizenship demands that we become literate in how to interpret data, what assumptions and processes are necessary to analyze data, as well as how we might participate in generating our own analyses and presentations of data. Consequently, data analytics is an emerging field with skills applicable to a wide variety of disciplines. This course introduces analysis, computation, and presentation concerns through the investigation of data driven puzzles in wide array of fields - political, economic, historical, social, biological, and others. No previous experience is required.
Utilizing Denison as a model of society, this practicum set in a seminar will explore questions of collective import through the analysis of new and existing sources of data at Denison. A problem-driven approach will lead to the acquisition of new, appropriate data analytic skills, set in an ethical context that carefully considers the implications of data display and policy recommendations on community members. A significant component of the course is working with policymaking and implementing professionals on campus and developing presentation skills appropriate for professional communication with the public. Though a significant learning opportunity itself, this course should also be seen as a prelude to a community internship in the post-Junior year summer. Prerequisites: CS 111/112, DA 101, and either MATH 242 or a disciplinary research methods course.
This course is designed to develop students' understanding of the cutting edge methods and algorithms of data analytics and how they can be used to answer questions about real-world problems. These methods, and the underlying models, can be used to learn from existing data to make predictions about new data. The course will examine both supervised and unsupervised methods and will include topics such as clustering, classification, and network analysis. Prerequisites: CS 181 and DA 301 or permission of instructor.
This is a capstone seminar for the Data Analytics major in which students work collaboratively on research projects. Problems may drive from internship experiences, courses of study at Denison, or other sources subject to instructor approval. Heavy emphasis will be placed on providing ongoing research reports and collective problem solving and review. Prerequisites: DA 301, CS 181, MATH 242, a disciplinary methods course, and a DA internship.