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 2024

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; Ajay Anand 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 440-1 Data Mining Thaddeus Pawlicki 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 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: Course is only open to undergrad CSC and DSCC students during registration week. Restriction will be lifted Monday, 11/20. 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 Nebojsa Duric 7:00PM - 7:00PM
Signal Processing Research: Delay-and-sum (DAS) beamforming relies on the time of flight (TOF) from each transducer element to each image point to focus recorded ultrasound signals. Most ultrasound systems assume a constant speed of sound (SOS) when calculating TOF; however, this ignores spatial variations in SOS. Ideal TOF may be determined based on the exact SOS profile in tissue; however, in practice, SOS is not known in advance. Recent works show that iterative SOS estimation and TOF correction can significantly improve image quality. The goal of this work is to improve SOS estimation and ultrasound image quality based on automatic differentiation (e.g., Google JAX). Weekly meetings reporting results will be used to gauge progress and determine final grade. Location ( 7:00PM - 7:00PM)
DSCC 495-3 Master's Research Caitlin Dreisbach 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 Ram Haddas 7:00PM - 7:00PM
URMC Motion Analysis Labs • Recognize common human motion laboratory tools (i.e. human motion capture, force plate, EMG, etc.) and the types of data that are output from those devices. • Become knowledgeable of how human motion laboratory data is processed and analyzed for clinical and research purposes. • Understanding the process of combining laboratory data with patient clinical outcomes (i.e. clinical standard of care, questionnaires, radiographic measurements, psychological factors, and medication intake) into a large collective database. • Experience recalling subsets of data from the database for use by laboratory personnel and UR Orthopaedic clinical staff. • Provide and prepare a subset of data statistical analysis. The final evaluation for this course will consist of the following: • Development of a large-scale database to store all of the clinical and research data collected from patients/subjects in the human motion laboratory and clinical standard of care. • Development of an efficient process to recall a subset of the database to be used in clinical reporting and research purposes. • Development of an efficient process to store and recall control patient/subject data to be used in clinical reporting and publication purposes. Location ( 7:00PM - 7:00PM)
DSCC 495-6 Master's Research Earl Dorsey 7:00PM - 7:00PM
Longitudinal analysis of motor symptoms in Parkinson's Disease - A UDALL Super-PD study: The aim of this project is to perform a longitudinal analysis, across a span of 2 years, of motor symptoms in Parkinson's disease participants. Particularly, analysis of rhythmic tremor, activity/gait, and arrhythmic motor symptoms form the main goals of the analysis. Also the project will be focused on detailed descriptive analysis on 10 participants (2 control, 8 Parkinson's). Course Evaluation based on: Successful completion will be marked by completing an analysis of sensor data from PD and non-PD participants, attending group meetings, presentation of findings, creation of tables and/or figures and contribution to the methods, results, and discussion sections of a manuscript. Location ( 7:00PM - 7:00PM)
DSCC 495-7 Master's Research Jiebo Luo 7:00PM - 7:00PM
Developing explainable predictive models for free-text notes in electronic health record data using large language models. Evaluation will be based on project report and/or manuscript. Location ( 7:00PM - 7:00PM)
DSCC 574-1 Continuous Algorithms for Optimization and Sampling Jiaming Liang TR 11:05AM - 12:20PM
Traditional algorithms in computer science are designed in a discrete manner. Nonetheless, recent years have witnessed great advances from a continuous perspective, particularly in the design of optimization and sampling algorithms. There is a deep connection between optimization and sampling, either through optimization as the limit of sampling, or through sampling as optimization in the space of probability measures. Motivated by this viewpoint, this course aims to develop a systematic way to design and analyze algorithms for both areas from the continuous perspective. More particularly, this course starts from continuous optimization, discusses stochastic optimization in detail, introduces optimal transport as a bridge connecting optimization and sampling, and finally delves into sampling. Prerequisites: Students should be familiar with multivariate calculus, linear algebra, basic probability and statistics, and have good MATLAB or Python programming skills. Prior knowledge of optimization and sampling is helpful but not required. Location Meliora Room 209 (TR 11:05AM - 12:20PM)
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 897A-1 Masters In-Absentia Ajay Anand 7:00PM - 7:00PM
No description Location ( 7:00PM - 7:00PM)
DSCC 897B-1 Master's In-Absentia Abroad Ajay Anand 7:00PM - 7:00PM
No description 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: Course is only open to undergrad CSC and DSCC students during registration week. Restriction will be lifted Monday, 11/20.
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 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; Ajay Anand
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 574-1 Continuous Algorithms for Optimization and Sampling Jiaming Liang
Traditional algorithms in computer science are designed in a discrete manner. Nonetheless, recent years have witnessed great advances from a continuous perspective, particularly in the design of optimization and sampling algorithms. There is a deep connection between optimization and sampling, either through optimization as the limit of sampling, or through sampling as optimization in the space of probability measures. Motivated by this viewpoint, this course aims to develop a systematic way to design and analyze algorithms for both areas from the continuous perspective. More particularly, this course starts from continuous optimization, discusses stochastic optimization in detail, introduces optimal transport as a bridge connecting optimization and sampling, and finally delves into sampling. Prerequisites: Students should be familiar with multivariate calculus, linear algebra, basic probability and statistics, and have good MATLAB or Python programming skills. Prior knowledge of optimization and sampling is helpful but not required.
DSCC 440-1 Data Mining Thaddeus Pawlicki
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.