Graduate Program

Spring Term Schedule

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.

Sortable | Group by Weekday

Spring 2025

Number	Title	Instructor	Time
DSCC 401-1 Tools for Data Science Brendan Mort MW 9:00AM - 10:15AM
This course provides a hands-on introduction to widely-used tools for data science. Topics include computational hardware and Linux; languages and packages for statistical analysis and visualization; parallel computing and Spark; libraries for machine learning and deep learning; databases including NoSQL; and cloud services. PREREQUISITES: CSC 161, CSC 171 or some equivalent introductory programming experience strongly recommended. Location Bausch & Lomb Room 109 (MW 9:00AM - 10:15AM)
DSCC 402-1 Data Science at Scale Lloyd Palum; Brendan Mort MW 4:50PM - 6:05PM
Data intensive applications (DIA) are an important part of many valuable services that we rely on in our day to day lives. These applications in most cases are built by performing data engineering and data science at scale. Scale in this case implies data volume and compute capacity far outside of what is available on a single machine and its narrow connection to the internet. This course will focus on how to develop data intensive applications at scale in the Cloud. The course will be structured with lecture content and programming labs using Python and SQL on Databricks Unified Analytics Platform. Grading will be based on programming homework and a final project that demonstrates clear understanding of how to orchestrate the complete DIA pipeline to deliver business value in a commercial transportation application. PREREQUSITE: DSCC 201/401 or instructor permission Location Gavett Hall Room 202 (MW 4:50PM - 6:05PM)
DSCC 410-1 Digital Imaging Gregory Heyworth TR 11:05AM - 12:20PM
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, photogrammetry, and Reflectance Transformation Imaging, and spectral image processing using ENVI and Photoshop. These skills will be applied to data from the ongoing research of the Lazarus Project as well as to local cultural heritage collections. Location Hylan Building Room 303 (TR 11:05AM - 12:20PM)
DSCC 422-1 Nanoelectonic Devices Stephen Wu MW 2:00PM - 3:15PM
Topics in semiconductor device physics, electronic band structure, materials science, and magnetism with a focus on applications to new and emerging electronic device technologies. This background will serve as a jumping off point to discuss potential future electronic devices with novel properties beyond the current status quo. Looking beyond just next-generation technology, the course will explore what electronics could look like on the 25+ year timescale. Basic trends from condensed matter physics, materials science and electrical engineering will be discussed. Topics include: 2D electronic materials/transistors, magnetic memory, spintronics, multiferroic memory, topological matter/devices. Prerequisites: ECE 223/423 or instructors approval Location Gavett Hall Room 208 (MW 2:00PM - 3:15PM)
DSCC 440-1 Data Mining Jian Kang TR 4:50PM - 6:05PM
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. Location Dewey Room 1101 (TR 4:50PM - 6:05PM)
DSCC 442-1 Network Science Analytics Gonzalo Mateos Buckstein MW 3:25PM - 4:40PM
The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks. Prerequisites: Some mathematical maturity, comfortable with linear algebra, probability, and analysis (e.g., MTH164-165). Exposure to programming and Matlab useful, but not required. For more information, please visit the class website: https://www.hajim.rochester.edu/ece/sites/gmateos/ECE442.html Location Computer Studies Room 601 (MW 3:25PM - 4:40PM)
DSCC 449-01 Computer Models of Perception & Cognition Robert Jacobs TR 11:05AM - 12:20PM
How can computer models help us understand how people perceive and reason about their environments? This course addresses this question, with emphasis placed on how people use probabilistic reasoning in order to represent and manage ambiguity and uncertainty for the purpose of making intelligent decisions. The course is relevant to students with interests in computational studies of human perception and cognition, and to students with interests in artificial intelligence. Homework assignments will require students to write computer programs using the Python programming language. Prerequisites: MATH 161, MATH 162, and CSC 161 (or equivalent proficiency in Python programming) required. MATH 164, MATH 165, and/or STAT 213 are helpful but not required. Location Gavett Hall Room 312 (TR 11:05AM - 12:20PM)
DSCC 461-1 Database Systems Eustrat Zhupa MW 10:25AM - 11:40AM
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. Note: Open to CSC and DSCC students only during registration week. Restriction will be lifted Monday, November 18. Location Dewey Room 2162 (MW 10:25AM - 11:40AM)
DSCC 463-1 Data Management Systems Fatemeh Nargesian MW 9:00AM - 10:15AM
This course explores the internals of data engines. Topics covered will include the relational model; relational database design principles based on dependencies and normal forms; query execution; transactions; recovery; query optimization; parallel query processing; NoSQL. Location Gavett Hall Room 312 (MW 9:00AM - 10:15AM)
DSCC 465-2 Introduction to Statistical Machine Learning Cantay Caliskan MW 2:00PM - 3:15PM
The course provides an introduction to modern machine learning concepts, techniques, and algorithms. Topics discussed include regression, clustering and classification, kernels, support vector machines, feature selection, goodness of fit, neural networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets. Students will be expected to work with Python programming environment to complete the assignments. PRE-REQUISITES: 1) DSCC/CSC/TCS 462 or STAT 212 or STAT 213 or equivalent introductory statistics background. 2) Introductory programming in Python or equivalent background in another programming language. 3) Knowledge of data mining/machine learning. Location Wegmans Room 1400 (MW 2:00PM - 3:15PM)
DSCC 483-1 Data Science Capstone + Practicum Ajay Anand; Cantay Caliskan MW 10:25AM - 11:40AM
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: DSCC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 462 or equivalent; DSCC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. FOR GRADUATING MS Candidate ONLY. PERMISSION REQUEST: To seek instructor permission/eligibility, follow directions on UR Student. https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf Location Bausch & Lomb Room 106 (MW 10:25AM - 11:40AM)
DSCC 491-1 Master's Reading Course Ajay Anand 7:00PM - 7:00PM
To register for Independent Study, contact program advisor before registering. Location ( 7:00PM - 7:00PM)
DSCC 495-1 Master's Research Ajay Anand 7:00PM - 7:00PM
Notify advisor before enrolling Location ( 7:00PM - 7:00PM)
DSCC 495-2 Master's Research – 7:00PM - 7:00PM
Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-3 Master's Research – 7:00PM - 7:00PM
Using All of Us data to explore Social Determinants of Health among Black Women: Execute a research study examining the relationship between social determinants of health and key demographic features for participants who identify as women using the All of Us Research Program data. All analyses will be conducted in the Research Workbench. This is a 2-hour credit class. The student is expected to work 6-8 hours per week to complete this class. Course Evaluation: Required meetings once per week to detail progress and determine next steps. Result of the semester will be a publishable manuscript. Location ( 7:00PM - 7:00PM)
DSCC 495-5 Master's Research – 7:00PM - 7:00PM
Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-6 Master's Research Ray Dorsey 7:00PM - 7:00PM
Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-7 Master's Research – 7:00PM - 7:00PM
Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 895-1 Cont of Master's Enrollment – 7:00PM - 7:00PM
Blank Description Location ( 7:00PM - 7:00PM)
DSCC 897-1 Masters Dissertation Ajay Anand 7:00PM - 7:00PM
Please see advisor before enrolling. Location ( 7:00PM - 7:00PM)
DSCC 899-1 Master's Dissertation Ajay Anand 7:00PM - 7:00PM
see advisor before enrolling Location ( 7:00PM - 7:00PM)

Number	Title	Instructor	Time
Monday and Wednesday
DSCC 401-1 Tools for Data Science Brendan Mort
This course provides a hands-on introduction to widely-used tools for data science. Topics include computational hardware and Linux; languages and packages for statistical analysis and visualization; parallel computing and Spark; libraries for machine learning and deep learning; databases including NoSQL; and cloud services. PREREQUISITES: CSC 161, CSC 171 or some equivalent introductory programming experience strongly recommended.
DSCC 463-1 Data Management Systems Fatemeh Nargesian
This course explores the internals of data engines. Topics covered will include the relational model; relational database design principles based on dependencies and normal forms; query execution; transactions; recovery; query optimization; parallel query processing; NoSQL.
DSCC 461-1 Database Systems Eustrat Zhupa
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. Note: Open to CSC and DSCC students only during registration week. Restriction will be lifted Monday, November 18.
DSCC 483-1 Data Science Capstone + Practicum Ajay Anand; Cantay Caliskan
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: DSCC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 462 or equivalent; DSCC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. FOR GRADUATING MS Candidate ONLY. PERMISSION REQUEST: To seek instructor permission/eligibility, follow directions on UR Student. https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf
DSCC 422-1 Nanoelectonic Devices Stephen Wu
Topics in semiconductor device physics, electronic band structure, materials science, and magnetism with a focus on applications to new and emerging electronic device technologies. This background will serve as a jumping off point to discuss potential future electronic devices with novel properties beyond the current status quo. Looking beyond just next-generation technology, the course will explore what electronics could look like on the 25+ year timescale. Basic trends from condensed matter physics, materials science and electrical engineering will be discussed. Topics include: 2D electronic materials/transistors, magnetic memory, spintronics, multiferroic memory, topological matter/devices. Prerequisites: ECE 223/423 or instructors approval
DSCC 465-2 Introduction to Statistical Machine Learning Cantay Caliskan
The course provides an introduction to modern machine learning concepts, techniques, and algorithms. Topics discussed include regression, clustering and classification, kernels, support vector machines, feature selection, goodness of fit, neural networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets. Students will be expected to work with Python programming environment to complete the assignments. PRE-REQUISITES: 1) DSCC/CSC/TCS 462 or STAT 212 or STAT 213 or equivalent introductory statistics background. 2) Introductory programming in Python or equivalent background in another programming language. 3) Knowledge of data mining/machine learning.
DSCC 442-1 Network Science Analytics Gonzalo Mateos Buckstein
The science of networks is an emerging discipline of great importance that combines graph theory, probability and statistics, and facets of engineering and the social sciences. This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It will introduce basic network models and structural descriptors, network dynamics and prediction of processes evolving on graphs, modern algorithms for topology inference, community and anomaly detection, as well as fundamentals of social network analysis. All concepts and theories will be illustrated with numerous applications and case studies from technological, social, biological, and information networks. Prerequisites: Some mathematical maturity, comfortable with linear algebra, probability, and analysis (e.g., MTH164-165). Exposure to programming and Matlab useful, but not required. For more information, please visit the class website: https://www.hajim.rochester.edu/ece/sites/gmateos/ECE442.html
DSCC 402-1 Data Science at Scale Lloyd Palum; Brendan Mort
Data intensive applications (DIA) are an important part of many valuable services that we rely on in our day to day lives. These applications in most cases are built by performing data engineering and data science at scale. Scale in this case implies data volume and compute capacity far outside of what is available on a single machine and its narrow connection to the internet. This course will focus on how to develop data intensive applications at scale in the Cloud. The course will be structured with lecture content and programming labs using Python and SQL on Databricks Unified Analytics Platform. Grading will be based on programming homework and a final project that demonstrates clear understanding of how to orchestrate the complete DIA pipeline to deliver business value in a commercial transportation application. PREREQUSITE: DSCC 201/401 or instructor permission
Tuesday and Thursday
DSCC 410-1 Digital Imaging Gregory Heyworth
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, photogrammetry, and Reflectance Transformation Imaging, and spectral image processing using ENVI and Photoshop. These skills will be applied to data from the ongoing research of the Lazarus Project as well as to local cultural heritage collections.
DSCC 449-01 Computer Models of Perception & Cognition Robert Jacobs
How can computer models help us understand how people perceive and reason about their environments? This course addresses this question, with emphasis placed on how people use probabilistic reasoning in order to represent and manage ambiguity and uncertainty for the purpose of making intelligent decisions. The course is relevant to students with interests in computational studies of human perception and cognition, and to students with interests in artificial intelligence. Homework assignments will require students to write computer programs using the Python programming language. Prerequisites: MATH 161, MATH 162, and CSC 161 (or equivalent proficiency in Python programming) required. MATH 164, MATH 165, and/or STAT 213 are helpful but not required.
DSCC 440-1 Data Mining Jian Kang
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.
Friday