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.

BUILDING: GAVET | ROOM: 206

PREREQUISITES: CSC 161, CSC 171, or some equivalent programming experience strongly recommended

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: GAVET | ROOM: 206

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. If the course is closed, DO NOT email the professor. Fill out this form to be added to the waitlist: https://goo.gl/forms/IyFoIfjrtISvJls62. Additional places, if any, will become available automatically for online registration.

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. If the course is closed, DO NOT email the professor. Fill out this form to be added to the waitlist: https://goo.gl/forms/IyFoIfjrtISvJls62. Additional places, if any, will become available automatically for online registration.

BUILDING: HARK | ROOM: 115

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: WEGMN | ROOM: 1400

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

Time series analysis is a valuable data analysis technique in a variety of industrial (e.g., prognostics and health management), business (e.g., financial data analysis) and healthcare (e.g., disease progression modeling) applications. Moreover, forecasting in time series is an essential component of predictive analytics. The course will begin with an introduction to practical aspects relevant to time series data analysis such as data collection, characterization, and preprocessing. Topics covered will include smoothing methods (moving average, exponential smoothing), trend and seasonality in regression models, autocorrelation, AR and ARIMA models, and application of neural network (including deep learning-based) models to time-series data. Students shall work on projects with time-series data sets using modeling tools in Python/R.

BUILDING: CSB | ROOM: 209

PREREQUISITES: MTH 165 and CSC161/CSC171 or equivalent intro programming coursework

This course, taught by an in-industry data scientist, will focus on how to take machine learning and apply it to healthcare. The first half of the course will cover significant medical content, such as what medical data looks like, where it comes from, and how to handle it. In addition, we will cover the basics of machine learning algorithms such as SVMs, Decision Forests, and Neural Networks, and how to specifically apply these algorithms to medical data. In the second half we will go into deep learning, specifically in the case of using CNNs to process a variety of medical images for tasks such as classification, regression, and segmentation. Throughout the course we will have several guest lectures and project walkthroughs, designed to give specific examples of how to utilize the techniques taught in this course in a real-life setting. Having prior machine learning experience will be helpful, but is not a requirement. This course will be open to undergraduate students only with instructor permission.

BUILDING: DEWEY | ROOM: 2110D

PREREQUISITES: DSC262 or equivalent statistics course; CSC161 or CSC171 or equivalent introductory programming

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: GAVET | ROOM: 310

PREREQUISITES: DSC 240/440 and an introductory statistics course such as DSC262/462, STT212 or STT 213 or equivalent; 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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