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: DEWEY | ROOM: 2162

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

Image, Text and Technology is an interdisciplinary course in the history of the book as a textual and visual medium, an artistic object, and a technology of communication. We will treat this history not merely in the classroom, but participate in it through a series of hands-on projects. Beginning with Aristotle’s insight that we think in images, we will consider writing as bound up in a theoretical relationship with seeing (aesthetics), perceiving (phenomenology, vision, cognitive science), and historically with technologies of dissemination, both analog and digital (manuscripts, printing, photography, television, the internet). We will explore the limits and conjunctions of visual and verbal media through theoretical and scientific readings in Plato, Lessing, Benjamin, Derrida, and McLuhan, and primary texts including the Bible, the Popol Vuh, and the Precepts of Ptah Hotep. This course may be taken alone or in conjunction with Digital Imaging: Transforming Real into Virtual (Digital Media Studies).

BUILDING: RRLIB | ROOM: 416

The course will begin with an introduction to practical aspects relevant to time series data analysis such as data collection, characterization, and preprocessing. Core topics covered will include smoothing methods (moving average, exponential smoothing), trend and seasonality in regression models, autocorrelation, AR and ARIMA models. The course emphasizes an applied end-to-end analysis and forecasting approach rather specific algorithms or models. Students shall work on projects with realistic time-series data sets using visualization and modeling tools: Tableau and R.

BUILDING: | ROOM:

PREREQUISITES: MTH 165; Familiarity with programming

This course provides an experience for Data Science majors to apply the core knowledge and skills attained during their program to a tangible data science focused project. Students will work with external sponsors in small teams on a project that applies data science methods to the analysis of a real-world problem. The identified projects or problems and datasets 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 will satisfy one upper-level writing requirement for undergraduate students registered in DSC 383W.

BUILDING: WEGMN | ROOM: 1005

PREREQUISITES: This course is only open to data science MS students.

No description

BUILDING: | ROOM:

No description

BUILDING: | ROOM: