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Undergraduate Program

Courses

Courses currently being offered:

Fall >
Spring >

Check the course schedules/descriptions available via the Registrar's Office for the official schedules for the widest range of terms for which such information is available.


Below you will find a list of all undergraduate courses that have been offered. Only courses with a DSC course number are listed on this page. See BA and BS degree requirements for all of the required and elective courses for the major.

NOTE: Not all of these courses are offered in any given year.

DSC 201 TOOLS FOR DATA SCIENCE

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.

Prerequisites: CSC 161, CSC 171, or some equivalent programming experience strongly recommended
Last Offered: Fall 2019

DSC 210 DIGITAL IMAGING

No description

Last Offered: Spring 2019

DSC 240 DATA MINING

No description

Last Offered: Fall 2019

DSC 261 DATABASE SYSTEMS

No description

Last Offered: Fall 2019

DSC 262 COMPUTATIONAL INTRODUCTION TO STATISTICS

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.

Prerequisites: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171
Last Offered: Fall 2019

DSC 265 INTERMEDIATE STATISTICAL & COMPUTATIONAL METHODS

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

Prerequisites: CSC 262 AND MTH 165 or MTH 173 or MTH 235
Last Offered: Spring 2019

DSC 267 IMAGE, TEXT, AND TECHNOLOGY

No description

Last Offered: Spring 2018

DSC 275 TIME SERIES ANALYSIS & FORECASTING in DATA SCIENCE

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.

Prerequisites: MTH 165 and CSC161/CSC171 or equivalent intro programming coursework
Last Offered: Fall 2019

DSC 381 ARTIFICIAL INTELLIGENCE & DEEP LEARNING in HEALTHCARE

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.

Prerequisites: DSC262 or equivalent statistics course; CSC 242 and CSC/DSC 240
Last Offered: Fall 2019

DSC 383W DATA SCIENCE CAPSTONE / PRACTICUM

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.

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
Last Offered: Fall 2019

DSC 390 SUPERVISED TEACHING

No description

Last Offered: Fall 2019

DSC 391 INDEPENDENT STUDY

No description

Last Offered: Fall 2019

DSC 391W INDEPENDENT STUDY

No description

Last Offered: Spring 2019

DSC 395 INDEPENDENT RESEARCH

No description

Last Offered: Fall 2019

DSC 395W INDEPENDENT RESEARCH

No description

Last Offered: Fall 2019