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: GAVET | ROOM: 206

PREREQUISITES: Restricted to DSC majors and graduate students.

The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes.

BUILDING: GRGEN | ROOM: 109

PREREQUISITES: ECE242 or equivalent

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: GRGEN | ROOM: 101

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: GAVET | ROOM: 312

PREREQUISITES: CSC172; CSC173 and CSC 252 recommended

This course will cover foundational concepts in probability and statistical inference, with an emphasis on topics of interest to computer scientists. Following an introduction to elementary probability theory, topics will include applications of combinatorics; Markov chains; principles of statistical classification (Bayes' rule, sensitivity and specificity, ROC curves) and random number generation. The theory of statistical estimation and hypothesis testing will be introduced, and applied to one and two sample inference for population means, proportions, variances and correlations. Nonparametric procedures will be discussed. Topics also include statistical modeling (ANOVA, simple and multiple regression), and computational methods. Students will be introduced to the R statistical computing environment.

BUILDING: LCHAS | ROOM: 141

PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171

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: MOREY | ROOM: 501

PREREQUISITES: DSC 240/440; DSC 261/461 recommended prior or concurrently

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This course provides a hand-on introduction to experimental and analytical methods in cognitive science and artificial intelligence. Each year, it offers three modules from a rotating list, including topics such as brain imaging, computational linguistics, and computer vision. The course is open to graduate students in any discipline. The course is recommended for who intend to pursue research in the the intersection of cognitive science and computer science, but prior experience in those fields is not required. It is required for students supported by the BCS/CS NRT graduate training grant. The modules are on Bayesian neuroscience, computational models in cognitive science, and natural language dialog systems.

BUILDING: MEL | ROOM: 269

PREREQUISITES: none

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