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 2024

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: CSC 161, CSC 171 or some equivalent programming experience strongly recommended. Location Wegmans Room 1400 (MW 9:00AM - 10:15AM)
DSCC 420-01 Intro 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. Prerequisites: ECE 242 or equivalent Location Gavett Hall Room 202 (MW 4:50PM - 6:05PM)
DSCC 435-1 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 Hylan Building Room 203 (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 Bausch & Lomb Room 106 (TR 2:00PM - 3:15PM)
DSCC 461-1 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 Harkness Room 115 (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. PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 (or equivalent discrete math and calculus coursework) Location Gavett Hall Room 206 (TR 4:50PM - 6:05PM)
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. PREREQUISITES: Introductory Statistics (DSC 262/STT212/STT213 or equivalent), Linear Algebra and Differential equations (MTH 165 or equivalent), and applied Python programming (CSC161 or equivalent) Location Dewey Room 2162 (TR 11:05AM - 12:20PM)
DSCC 483-01 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: DSC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 262/462, STT212 or STT213 or equivalent. DSC 261/461 (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 Gavett Hall Room 206 (MW 10:25AM - 11:40AM)
DSCC 491-1 Master's Reading Course – 7:00PM - 7:00PM
To register for Independent Study, contact program advisor before registering. Location ( 7:00PM - 7:00PM)
DSCC 491-2 Master's Reading Course Joseph Ciminelli 7:00PM - 7:00PM
Bibliometric Analysis: The project entails an exploration of the Web of Science (WOS) data from Clarivate Analytics. It is a bibliometric study with several potential directions that will be determined after an initial analysis of the change in metadata coverage over time. Possible topics include studying trends in author collaborations by affiliation and discipline, semantic analysis of keywords, and/or looking at factors that contribute to a journal’s inclusion in WOS’s Core Collection from the Emerging Sources Citation Index. Course Evaluations: Weekly meetings with progress reports culminating into a project output that could be built upon in future. Location ( 7:00PM - 7:00PM)
DSCC 495-01 Master's Research – 7:00PM - 7:00PM
Contact program coordinator and faculty before registering research for credit. Must complete a research contract. Location ( 7:00PM - 7:00PM)
DSCC 495-05 Master's Research Caitlin Dreisbach 7:00PM - 7:00PM
Execute a research study examining the risk profiles of alcohol use among sexual minority groups and how key demographic features impact their risk profiles based upon the Minority Stress Model, using the All of Us Research Program data. All analyses will be conducted in the Research Workbench. 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 511-01 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 Hylan Building Room 306 (MW 9:00AM - 10:15AM)
DSCC 895-1 Cont of Master's Enrollment – 7:00PM - 7:00PM
Blank Description Location ( 7:00PM - 7:00PM)
DSCC 897-1 Masters Dissertation – 7:00PM - 7:00PM
Please see advisor before enrolling. Location ( 7:00PM - 7:00PM)
DSCC 899-1 Master's Dissertation – 7:00PM - 7:00PM
see advisor before enrolling 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: CSC 161, CSC 171 or some equivalent programming experience strongly recommended.
DSCC 511-01 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. 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.
DSCC 483-01 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: DSC 240/440 (Data Mining) AND an introductory statistics course such as DSCC 262/462, STT212 or STT213 or equivalent. DSC 261/461 (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 461-1 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.
DSCC 420-01 Intro 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. Prerequisites: ECE 242 or equivalent
Tuesday and Thursday
DSCC 435-1 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. PREREQUISITES: Introductory Statistics (DSC 262/STT212/STT213 or equivalent), Linear Algebra and Differential equations (MTH 165 or equivalent), and applied Python programming (CSC161 or equivalent)
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. PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 (or equivalent discrete math and calculus coursework)
Friday