This course provides a hands-on introduction to widely-used tools for data science.  Topics include Linux; languages and packages for statistical analysis and visualization; cluster and parallel computing using Hadoop and Spark; libraries for machine learning; no-sql data stores; and cloud services. Fall offering for data science majors and graduate students only; spring offering will be open to non-majors.

BUILDING: B&L | ROOM: 109

This course introduces students to the methods involved in turning real objects into virtual ones using cutting edge digital imaging technology and image rendering techniques. Focusing on manuscripts, paintings, maps, and 3D artifacts, students will learn the basics of Multispectral Imaging, Infrared Thermography, Reflectance Transformation Imaging, focus stacking, spectral image processing using ENVI, stitching in 2D and 3D, and Structure-from-Motion photogrammetry. These skills will be applied to data from the ongoing research of the Lazarus Project as well as to local cultural heritage collections. This course may be taken alone or in conjunction with Image, Text, and Technology (English).

BUILDING: MEL | ROOM: 206

Fundamental concepts and techniques of data mining, including data attributes, data visualization, data pre-processing, mining frequent patterns, association and correlation, classification methods, and cluster analysis. Advanced topics include outlier detection, stream mining, and social media data mining. CSC 440, a graduate-level course, requires additional readings and a course project. 

BUILDING: GAVET | ROOM: 202

PREREQUISITES: Prerequisites will be strictly enforced: CSC171, CSC 172 and MTH 161. Recommended: CSC 242 or CSC262; MTH165.

This course presents the fundamental concepts of database design and use. It provides a study of data models, data description languages, and query facilities including relational algebra and SQL, data normalization, transactions and their properties, physical data organization and indexing, security issues and object databases. It also looks at the new trends in databases. The knowledge of the above topics will be applied in the design and implementation of a database application using a target database management system as part of a semester-long group project.

BUILDING: LATT | ROOM: 201

PREREQUISITES: CSC172; CSC173 and CSC 252 recommended

This course is a continuation of CSC262, covering intermediate statistical methodology and related computational methods, with an emphasis on the R statistical computing environment.

BUILDING: WEGMN | ROOM: 1400

PREREQUISITES: CSC 262 AND MTH 165 or MTH 173 or MTH 235

The capstone/practicum provides an experience for data science majors/MS candidates to apply the core knowledge and skills attained during their program to a tangible data science focused project. Students will work in small teams on a project that applies data science methods to the analysis of a real-world problem. The instructor will guide each team in developing a topic that makes use of the knowledge the team members gained through their application area courses. The identified projects or problems and data sets will cover a range of application areas and reflect real-world needs from industry, medicine and government. Each student will be required to write a paper about their project, which satisfies one upper-level writing requirement for majors and Plan B for master's.

BUILDING: LCHAS | ROOM: 181

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