DATA- Data Science
DATA 110 Data-Driven Decisions (3)
Introduction to techniques for effectively making decisions using data. Studies fundamental principles, concepts, and techniques used to organize and analyze information. Exposes students to a variety of data tools.
DATA 210 Introduction to Data Science (3)
Prerequisite: COSC-110, CIV-110, and either second-year standing or 28 completed credits. An introduction to concepts, tools, and techniques in data science including data acquisition, cleaning, analysis, modeling, and visualization. No prior familiarity with data science is assumed. (CTN)
DATA 310 Data Visualization (3)
Prerequisite: Second-year standing. Studies principles of effective visualization based on insights from many disciplines, including cartography, psychology, cognitive science, and graphic design. Students will learn to analyze visualizations based on these principles and apply the principles to create effective visualizations of their own. (CTN)
DATA 340 Applied Machine Learning (3)
Prerequisites: MATH-131, COSC-110. The course focuses on the regression, classification, and clustering tasks with the scikit-learn machine learning library in Python. The basic data structures used in machine learning such as numpy arrays and pandas data frames will be introduced in the beginning of the course. Students will spend a significant amount of time out of class designing, writing, collaborating on, and debugging Python programs.
DATA 420 Senior Project (4)
Prerequisites: CIV 210, DATA 310 or DATA 340. Students will learn mathematical research techniques and apply them to design and implement a semester project of their choice. Students are expected to present (orally and in writing) the results of their work (WOC).