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Graduate 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 graduate courses that have been offered. Only courses with a DSC course number are listed on this page. See MS program for all of the required and elective courses for the degree.

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

DSC 401 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: Spring 2020

DSC 410 DIGITAL IMAGING

No description

Last Offered: Spring 2020

DSC 420 INTRO TO RANDOM PROCESSES

No description

Last Offered: Fall 2019

DSC 422 NETWORK SCIENCE ANALYTICS

No description

Last Offered: Spring 2020

DSC 440 DATA MINING

No description

Last Offered: Spring 2020

DSC 450 DATA SCIENCE PRACTICUM

Students are expected to work on a large scale data analysis and mining project. An existing large database is used. Project categories include medical data analysis & social media data analysis. Data mining algorithms are applied to data that is contained in these databases to predict health hazards or social behavior.

Prerequisites: This course is only open to data science MS students. CSC 440 and CSC 461 OR permission of instructor.
Last Offered: Spring 2018

DSC 461 DATABASE SYSTEMS

No description

Last Offered: Spring 2020

DSC 462 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.

Last Offered: Fall 2019

DSC 463 DATA MANAGEMENT SYSTEMS

No description

Last Offered: Spring 2020

DSC 465 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: DSC 462
Last Offered: Spring 2020

DSC 475 TIME SERIES ANALYSIS

No description

Last Offered: Fall 2019

DSC 481 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; CSC161 or CSC171 or equivalent introductory programming
Last Offered: Fall 2019

DSC 483 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; DSC 261/461 recommended prior or concurrently
Last Offered: Spring 2020

DSC 491 MASTER'S READING COURSE

No description

Last Offered: Summer 2020

DSC 494 INTERNSHIP

No description

Last Offered: Summer 2020

DSC 495 MASTER'S RESEARCH

No description

Last Offered: Spring 2020

DSC 530 METHODS IN DATA-ENABLED RESEARCH INTO HUMAN BEHAVIOR AND ITS COGNITIVE AND NEURAL MECHANISMS

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. For 2015, the modules are imaging and interpreting brain activity, large scale text corpus analysis, and sensing in the wild.

Prerequisites: none
Last Offered: Fall 2019

DSC 531 PRACTICUM IN DATA-EANABLED RESEARCH INTO HUMAN BEHAVIOR AND ITS COGNITIVE & NEURAL MECHANISMS

In this interdisciplinary project course, graduate students will work in mixed teams to develop an artifact that addresses a research question and/or infrastructure need in the intersection of cognitive science and artificial intelligence. Students will learn principles of design by participating in the stages of brainstorming, specification, initial design, prototyping, refinement, and evaluation. The artifacts created by this course could include online showcases, demonstrations, tutorials, blogs, scientific papers, and software components to support further research. The course is required for students supported by the BCS/CS NRT graduate training grant, and should be taken the semester after the corresponding methods course.

Prerequisites: DSC 530
Last Offered: Spring 2020

DSC 890 SUMMER IN RESIDENCE - MA

No description

Last Offered: Summer 2020

DSC 895 CONT OF MASTER'S ENROLLMENT

No description

Last Offered: Spring 2020

DSC 897 MASTERS DISSERTATION

No description

Last Offered: Summer 2020

DSC 897A MASTERS IN-ABSENTIA

No description

Last Offered: Fall 2019

DSC 897B MASTER'S IN-ABSENTIA ABROAD

No description

Last Offered: Fall 2019

DSC 899 MASTER'S DISSERTATION

No description

Last Offered: Fall 2019

DSC 985 LEAVE OF ABSENCE

No description

Last Offered: Spring 2020