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 2026

Number	Title	Instructor	Time
DSCC 401-01 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 Linux; languages and packages for statistical analysis and visualization; cluster and parallel computing including GPUs; Hadoop and Spark; libraries for machine learning; NoSQL databases; and cloud services. PREREQUISITES: Programming experience strongly recommended. Location (MW 9:00AM - 10:15AM)
DSCC 402-01 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. Location (MW 4:50PM - 6:05PM)
DSCC 410-01 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 (TR 11:05AM - 12:20PM)
DSCC 440-01 Data Mining Monika Polak TR 9:40AM - 10:55AM
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 (TR 9:40AM - 10:55AM)
DSCC 442-01 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. Location (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 (TR 11:05AM - 12:20PM)
DSCC 461-01 Database Systems Eustrat Zhupa MW 2:00PM - 3:15PM
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. Location (MW 2:00PM - 3:15PM)
DSCC 463-01 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 (MW 9:00AM - 10:15AM)
DSCC 465-02 Introduction to Statistical Machine Learning Cantay Caliskan TR 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. Location (TR 2:00PM - 3:15PM)
DSCC 483-01 Data Science Capstone + Practicum Ajay Anand; Cantay Caliskan (Private) 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. 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 (MW 10:25AM - 11:40AM)
DSCC 486-01 Applied Machine Learning and Healthcare Data Mining Monika Polak T 6:15PM - 8:55PM
Foundational course focusing on the understanding, application, and evaluation of machine learning and data mining approaches in data-intensive scenarios. Imbalanced data, outlier detection, text mining, introduction to natural language processing (NLP). Supervised, unsupervised learning. Emerging methods such as semi-supervised and self-supervised learning. Introduction to neural network-based models. Location (T 6:15PM - 8:55PM)
DSCC 487-01 Inferential Statistics for Data Science Barney Ricca R 6:15PM - 8:55PM
Fundamental concepts in probability and statistics from a data science perspective; rigorous probabilistic reasoning and problem-solving; statistical methods used in data science. Topics to be covered include data exploration through descriptive statistics (with an emphasis on using R for such analyses), random variables, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Location (R 6:15PM - 8:55PM)
DSCC 491-01 Master's Reading Ajay Anand 7:00PM - 7:00PM
This course is for master's students that have made arrangements with a faculty member to complete readings and discussion in a particular subject in their field of study. Location ( 7:00PM - 7:00PM)
DSCC 495-01 Master's Research Ajay Anand 7:00PM - 7:00PM
This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 495-02 Master's Research Dongmei Li 7:00PM - 7:00PM
This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 495-03 Master's Research – 7:00PM - 7:00PM
This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 495-04 Master's Research – 7:00PM - 7:00PM
This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 495-05 Master's Research – 7:00PM - 7:00PM
This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 495-06 Master's Research Ray Dorsey 7:00PM - 7:00PM
This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 495-07 Master's Research Florian Jaeger 7:00PM - 7:00PM
This course provides master’s students with the opportunity to conduct, develop, and refine their research projects. Students will engage in research relevant to their field of study and make progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 895-01 Continuation of Master’s Enrollment – 7:00PM - 7:00PM
This course is designed for master's degree students who have completed all required coursework but still need to finalize specific degree requirements under less than half-time enrollment. Location ( 7:00PM - 7:00PM)
DSCC 897-01 Masters Dissertation Ajay Anand 7:00PM - 7:00PM
This course provides master's students who are currently completing their final required coursework, or with special circumstances like an approved reduced courseload, with the opportunity to work full-time on their degrees. Students will make significant progress toward completing their degrees. Location ( 7:00PM - 7:00PM)
DSCC 899-01 Masters Dissertation Ajay Anand 7:00PM - 7:00PM
This course provides master’s students who have completed or are currently completing all course requirements with the opportunity to work full-time on their thesis. Students will make significant progress toward completing their degrees. Location ( 7:00PM - 7:00PM)

Number	Title	Instructor	Time
Monday and Wednesday
DSCC 401-01 Tools for Data Science Brendan Mort
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 including GPUs; Hadoop and Spark; libraries for machine learning; NoSQL databases; and cloud services. PREREQUISITES: Programming experience strongly recommended.
DSCC 463-01 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 483-01 Data Science Capstone + Practicum Ajay Anand; Cantay Caliskan (Private)
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. 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 461-01 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.
DSCC 442-01 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.
DSCC 402-01 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.
Tuesday
DSCC 486-01 Applied Machine Learning and Healthcare Data Mining Monika Polak
Foundational course focusing on the understanding, application, and evaluation of machine learning and data mining approaches in data-intensive scenarios. Imbalanced data, outlier detection, text mining, introduction to natural language processing (NLP). Supervised, unsupervised learning. Emerging methods such as semi-supervised and self-supervised learning. Introduction to neural network-based models.
Tuesday and Thursday
DSCC 440-01 Data Mining Monika Polak
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.
DSCC 410-01 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 465-02 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.
Thursday
DSCC 487-01 Inferential Statistics for Data Science Barney Ricca
Fundamental concepts in probability and statistics from a data science perspective; rigorous probabilistic reasoning and problem-solving; statistical methods used in data science. Topics to be covered include data exploration through descriptive statistics (with an emphasis on using R for such analyses), random variables, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Non-parametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods.
Friday