Graduate Program

Fall 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

Fall 2025

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 420-01 Introduction to Random Processes Gonzalo Mateos Buckstein MW 4:50PM - 6:05PM
The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes. Location (MW 4:50PM - 6:05PM)
DSCC 435-01 Optimization for Machine Learning Jiaming Liang TR 9:40AM - 10:55AM
This course primarily focuses on algorithms for large-scale optimization problems arising in machine learning and data science applications. The first part will cover first-order methods including gradient and subgradient methods, mirror descent, proximal gradient method, accelerated gradient method, Frank-Wolfe method, and inexact proximal point methods. The second part will introduce algorithms for nonconvex optimization, stochastic optimization, distributed optimization, manifold optimization, reinforcement learning, and those beyond first-order. Location (TR 9:40AM - 10:55AM)
DSCC 440-02 Data Mining Monika Polak TR 2:00PM - 3:15PM
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 2:00PM - 3:15PM)
DSCC 461-01 Database Systems Eustrat Zhupa MW 12:30PM - 1:45PM
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 12:30PM - 1:45PM)
DSCC 462-02 Computational Introduction to Statistics Anson Kahng TR 4:50PM - 6:05PM
This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, 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. Students will be introduced to the R statistical computing environment. Location (TR 4:50PM - 6:05PM)
DSCC 465-01 Introduction to Statistical Machine Learning Yukun Ma MW 3:25PM - 4:40PM
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: DSCC/CSC 462 or equivalent introductory statistics background. 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 3:25PM - 4:40PM)
DSCC 475-1 Time Series Analysis & Forecasting in Data Science Ajay Anand TR 11:05AM - 12:20PM
Description: 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 applied to time series data. Deep learning models including feedforward, recurrent, gated and convolutional architectures will also be studied. Students shall work on projects with time-series data sets using modeling tools in Python. Location (TR 11:05AM - 12:20PM)
DSCC 481-01 Python Programming and Tools for Data Science Barney Ricca W 6:15PM - 8:45PM
Topics include introduction to Python programming and data structures relevant to healthcare data. The course will also provide a hands-on introduction to widely used tools for data science, languages and packages used for statistical analysis and visualization; parallel computing and Spark; libraries for machine learning and deep learning; databases including NoSQL; and cloud services. Location (W 6:15PM - 8:45PM)
DSCC 482-01 Statistical Foundations and Data Visualization Barney Ricca M 6:15PM - 8:45PM
The course covers the essentials of the statistical foundations for interpretation and visualization of data. Using statistical tools, gain an understanding of how to interpret data quantitatively and to explore data-oriented structures. The primary focus is on descriptive statistics used to present and summarize numerical information. The course also emphasizes the design of systems for data visualization and related best practices for use in a healthcare setting. Course projects and assignments will involve accessing public healthcare data sources (e.g. MIMIC-III) or a deidentified data repository to extract relevant patient level attributes and create dashboards and visualizations to practice data storytelling for relevant healthcare problems. Students will be introduced to the R programming language which is a mainstay in statistical computing. Meeting Pattern TBA Location (M 6:15PM - 8:45PM)
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. PREREQUISITES: DSCC 440 (Data Mining) AND an introductory statistics course such as DSCC 462. DSC 461 (Database Systems) strongly recommended prior but may be taken concurrently. FOR DSC GRADUATING SENIORS and MS CANDIDATES. GRADUATING STUDENTS this semester have priority for eligibility/instructor permission. 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 491-02 Master's Reading Heather Reyes 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. The Digital Health Innovation (DHI) Program is an elective experience designed for those looking to understand collaborative innovation in digital healthcare. It is currently supported by the Department of Pediatrics and the UR Health Lab, and includes learners with nursing, physician, and data science backgrounds. The DHI has two phases. The first involves two weeks of interactive sessions focused on three questions: 1) What is Digital Health? 2) How does Digital Health Exist in Academia? 3) How Can One Become an Entrepreneur? Sessions are presented in a variety of formats by expert faculty from across the medical center, university, and industry partners (i.e., anesthesiology, pediatrics, psychiatry, nursing, data & computer science, business, ethics). Covered topics are displayed in the below figure. This experience is followed by a longitudinal mentored project in one area of interest. The program allows for projects to take one of several forms: traditional research project, clinically focused quality improvement, or entrepreneurial focused proof of concept. Technical and clinical mentors are paired with learners during the first 2-4 months of the project based on their skills and interest. This year’s program will begin in September 2025. Interested graduate students should get a signed departmental research contract with Heather Reyes prior to registering for the course. Location ( 7:00PM - 7:00PM)
DSCC 491-1 Master's Reading – 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 495-02 Master's Research Dillon Dzikowicz 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. Contact program coordinator and faculty before registering research for credit. A Text- and Acoustic- Based Model to Better Triage 911 Calls for Non-Traumatic Chest Pain This project addresses the inefficient triage of 911 calls for non-traumatic chest pain, where current symptom-based protocols lead to significant over-triage. Proposing an innovative solution, the researchers will leverage machine learning to analyze both the caller's reported symptoms and subtle acoustic biomarkers in their voice. Based on preliminary findings that callers with confirmed cardiac emergencies speak more slowly and with reduced voice quality, the team aims to develop and validate a deep learning model that integrates these textual and vocal features. The goal is to create a highly sensitive tool that can estimate the real-time probability of a cardiovascular emergency, thereby improving the accuracy of dispatch triage, optimizing the use of limited EMS resources, and ensuring patients with life-threatening conditions receive more timely and appropriate care. Course Evaluation based on: Weekly team meetings and progress reports, final progress report and presentation, peer and instructor reviews Location ( 7:00PM - 7:00PM)
DSCC 495-03 Master's Research Heather Reyes 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. Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-04 Master's Research Ram Haddas 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. Contact program coordinator and faculty before registering research for credit. TITLE: URMC Motion Labs Description: * 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. * Understand the process of combining laboratory data with patient clinical outcomes (i.e. clinical standard of care, questionnaires, radiographic measurements, psychological factors, and medicate 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 Evaluation based on: * 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 495-05 Master's Research Gonzalo Mateos Buckstein 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. Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-06 Master's Research Ehsan Hoque 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. Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-07 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. Contact program coordinator and faculty before registering research for credit. PROJECT TITLE: Association of smoking with commorbidity and COVID-19 severity PROJECT DESCRIPTION:Recent studies have shown COVID-19 patients with comorbidities have an increased risk for severe illness of COVID-19. Numerous studies have showed the link between smoking and comorbidities as smoking is a well-known risk factor for common comorbidities. However, the association of smoking and severity of COVID-19 is still unclear with inclusive results from recent studies. Given the association of comorbidities with both smoking and COVID-19, we propose to investigate the moderation effects of smoking in the association of comorbidities and COVID-19 using de-identified data (level 2) from the National COVID Cohort Collaborative (N3C). Our research question is whether smoking has moderation effects in the association of comorbidities with COVID-19 outcomes such as whether a patient is hospitalized, admitted to the ICU, and dead due to COVID-19. The proposed study will contribute to the literature on the direct and indirect association of both smoking/vaping and comorbidities with COVID-19 outcomes. Further we also plan to determine the health disparity relationship with smoking/vaping in COVID-19. EVALUATION: The student will read related papers and meet with the mentor weekly through Zoom. We expect to generate a manuscript from the study results. (1credit) Location ( 7:00PM - 7:00PM)
DSCC 495-08 Master's Research Sukardi Suba 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. Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-09 Master's Research Timothy Dye 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. Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-10 Master's Research Tolulope Olugboji 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 495-18 Master's Research Mujdat Cetin 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. Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 495-19 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. Contact program coordinator and faculty before registering research for credit. Location ( 7:00PM - 7:00PM)
DSCC 511-02 Large Language Models Hangfeng He MW 9:00AM - 10:15AM
This seminar offers an introduction to Large Language Models (LLMs), covering essential concepts such as Transformers, BERT, GPT-3, InstructGPT, prompting & decoding, and emergent abilities. Students will engage with a range of topics through paper presentations on themes such as Tool-Augmented LLMs, Multimodal Learning, LLMs for Science, Social and Ethical Concerns, Superintelligence Concerns, and Democratizing LLMs. Participants are required to present and discuss papers, write critical literature reviews, reproduce paper results, and collaborate on team projects. This seminar aims to provide a thorough understanding of LLMs, exploring their origins, opportunities, and concerns to enhance professional expertise in the field. Prerequisites: Students have completed at least one course in Natural Language Processing (NLP), Deep Learning (DL), or Machine Learning (ML), such as CSC 2/447 (Natural Language Processing), CSC 2/445 (Deep Learning), CSC 2/466 (Frontiers in Deep Learning), CSC 2/477 (End-to-End Deep Learning), and CSC 2/446 (Machine Learning). Other relevant courses in NLP, DL, or ML are also acceptable. Location (MW 9:00AM - 10:15AM)
DSCC 895-1 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 897-1 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 899-1 Masters Dissertation – 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
DSCC 482-01 Statistical Foundations and Data Visualization Barney Ricca
The course covers the essentials of the statistical foundations for interpretation and visualization of data. Using statistical tools, gain an understanding of how to interpret data quantitatively and to explore data-oriented structures. The primary focus is on descriptive statistics used to present and summarize numerical information. The course also emphasizes the design of systems for data visualization and related best practices for use in a healthcare setting. Course projects and assignments will involve accessing public healthcare data sources (e.g. MIMIC-III) or a deidentified data repository to extract relevant patient level attributes and create dashboards and visualizations to practice data storytelling for relevant healthcare problems. Students will be introduced to the R programming language which is a mainstay in statistical computing.
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.
DSCC 511-02 Large Language Models Hangfeng He
This seminar offers an introduction to Large Language Models (LLMs), covering essential concepts such as Transformers, BERT, GPT-3, InstructGPT, prompting & decoding, and emergent abilities. Students will engage with a range of topics through paper presentations on themes such as Tool-Augmented LLMs, Multimodal Learning, LLMs for Science, Social and Ethical Concerns, Superintelligence Concerns, and Democratizing LLMs. Participants are required to present and discuss papers, write critical literature reviews, reproduce paper results, and collaborate on team projects. This seminar aims to provide a thorough understanding of LLMs, exploring their origins, opportunities, and concerns to enhance professional expertise in the field.
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. PREREQUISITES: DSCC 440 (Data Mining) AND an introductory statistics course such as DSCC 462. DSC 461 (Database Systems) strongly recommended prior but may be taken concurrently. FOR DSC GRADUATING SENIORS and MS CANDIDATES. GRADUATING STUDENTS this semester have priority for eligibility/instructor permission. 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 465-01 Introduction to Statistical Machine Learning Yukun Ma
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: DSCC/CSC 462 or equivalent introductory statistics background.
DSCC 420-01 Introduction to Random Processes Gonzalo Mateos Buckstein
The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes.
Tuesday and Thursday
DSCC 435-01 Optimization for Machine Learning Jiaming Liang
This course primarily focuses on algorithms for large-scale optimization problems arising in machine learning and data science applications. The first part will cover first-order methods including gradient and subgradient methods, mirror descent, proximal gradient method, accelerated gradient method, Frank-Wolfe method, and inexact proximal point methods. The second part will introduce algorithms for nonconvex optimization, stochastic optimization, distributed optimization, manifold optimization, reinforcement learning, and those beyond first-order.
DSCC 475-1 Time Series Analysis & Forecasting in Data Science Ajay Anand
Description: 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 applied to time series data. Deep learning models including feedforward, recurrent, gated and convolutional architectures will also be studied. Students shall work on projects with time-series data sets using modeling tools in Python.
DSCC 440-02 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 462-02 Computational Introduction to Statistics Anson Kahng
This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, 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. Students will be introduced to the R statistical computing environment.
Wednesday
DSCC 481-01 Python Programming and Tools for Data Science Barney Ricca
Topics include introduction to Python programming and data structures relevant to healthcare data. The course will also provide a hands-on introduction to widely used tools for data science, languages and packages used for statistical analysis and visualization; parallel computing and Spark; libraries for machine learning and deep learning; databases including NoSQL; and cloud services.
Friday