Undergraduate Program

Spring Term Schedule

Only courses with a DSCC course number are listed on this page. See BA and BS degree requirements for all of the required and elective courses for the major.

Sortable | Group by Weekday

Spring 2025

Number	Title	Instructor	Time
DSCC 000-1 Data Science Colloquium – F 11:00AM - 3:30PM
Reserved for weekly data science (GIDS) colloquiums Location Wegmans Room 1400 (F 11:00AM - 3:30PM)
DSCC 201-1 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 programming experience strongly recommended. Location Bausch & Lomb Room 109 (MW 9:00AM - 10:15AM)
DSCC 202-1 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 210-1 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 229-01 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 240-1 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. Prerequisites will be strictly enforced: CSC171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC262; MATH165. Location Dewey Room 1101 (TR 4:50PM - 6:05PM)
DSCC 242-1 Introduction to Artificial Intelligence Yukang Yan TR 9:40AM - 10:55AM
Introduces fundamental principles and techniques from Artificial Intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning, to prepare students for advanced AI courses. Prerequisites: CSC 172 and MTH 150; CSC 173 STRONGLY Recommended. Open to CSC & DSCC majors only during registration week. Restriction will be lifted Monday, November 18. Location Wegmans Room 1400 (TR 9:40AM - 10:55AM)
DSCC 261-1 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. Prerequisites: CSC 172; CSC 173 and CSC 252 recommended. Course is only open to CSC & DSCC majors during registration week. Restriction will be lifted Monday, November 18. Location Dewey Room 2162 (MW 10:25AM - 11:40AM)
DSCC 263-1 Data Management Systems Fatemeh Nargesian MW 9:00AM - 10:15AM
This course explores the relational data model, the theory of database design, the use of databases in applications, and the internals of relational database engines. Topics covered will include the relational model and SQL; relational database design principles based on dependencies and normal forms; database topics from the application-building perspective, including indexes, views, transaction, and integrity constraints; query evaluation and optimization. Prerequisites: CSC 173 and CSC 252 (or CSC 261) Note: Restricted to CSC & DSCC majors only during registration week. Restriction will be lifted Monday, November 18th. Location Gavett Hall Room 312 (MW 9:00AM - 10:15AM)
DSCC 265-2 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: DSCC/CSC/STAT 262 or STAT 212 or STAT 213 (or equivalent introductory statistics) background AND DSCC240 (or equivalent data mining course) or permission of instructor. Location Wegmans Room 1400 (MW 2:00PM - 3:15PM)
DSCC 294-01 Nonlinear Optimization Jiaming Liang TR 2:00PM - 3:15PM
This course serves as a modern introduction to the field of optimization. It covers important topics such as convexity, optimality conditions, duality, gradient methods, and Newton's method. The objective is to provide the foundations of theory and algorithms of nonlinear optimization, as well as to present a variety of applications from diverse areas. Prerequisites: The course is intended for advanced undergraduate students with mathematical maturity in multivariate calculus (MATH 164) and linear algebra (MATH 165). Prior knowledge of optimization (MATH 208) is helpful but not required. Students should have good MATLAB or Python programming skills. Location Harkness Room 210 (TR 2:00PM - 3:15PM)
DSCC 383W-1 Data Science Capstone + Practicum Ajay Anand; Lisa Altman 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. ONLY GRADUATING SENIORS and MS CANDIDATES allowed this semester. 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 390A-2 Supervised Teaching DSCC 383W Ajay Anand 7:00PM - 7:00PM
Departmental Permission Required. https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf Location ( 7:00PM - 7:00PM)
DSCC 390A-3 Supervised Teaching DSCC 265 Cantay Caliskan 7:00PM - 7:00PM
Departmental Permission Required. https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf Location ( 7:00PM - 7:00PM)
DSCC 390A-4 Supervised Teaching DSCC 201 Brendan Mort 7:00PM - 7:00PM
Departmental Permission Required. To request eligibility, see directions: https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf Location ( 7:00PM - 7:00PM)
DSCC 390A-5 Supervised Teaching DSCC 202 Lloyd Palum 7:00PM - 7:00PM
Departmental Permission Required. For eligibility, see: https://tech.rochester.edu/wp-content/uploads/QRC-Requesting-Permission-to-Register_UofR-_0200227_cmf.pdf Location ( 7:00PM - 7:00PM)

Number	Title	Instructor	Time
Monday and Wednesday
DSCC 201-1 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 programming experience strongly recommended.
DSCC 263-1 Data Management Systems Fatemeh Nargesian
This course explores the relational data model, the theory of database design, the use of databases in applications, and the internals of relational database engines. Topics covered will include the relational model and SQL; relational database design principles based on dependencies and normal forms; database topics from the application-building perspective, including indexes, views, transaction, and integrity constraints; query evaluation and optimization. Prerequisites: CSC 173 and CSC 252 (or CSC 261) Note: Restricted to CSC & DSCC majors only during registration week. Restriction will be lifted Monday, November 18th.
DSCC 261-1 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. Prerequisites: CSC 172; CSC 173 and CSC 252 recommended. Course is only open to CSC & DSCC majors during registration week. Restriction will be lifted Monday, November 18.
DSCC 383W-1 Data Science Capstone + Practicum Ajay Anand; Lisa Altman
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. ONLY GRADUATING SENIORS and MS CANDIDATES allowed this semester. 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 265-2 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: DSCC/CSC/STAT 262 or STAT 212 or STAT 213 (or equivalent introductory statistics) background AND DSCC240 (or equivalent data mining course) or permission of instructor.
DSCC 202-1 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 242-1 Introduction to Artificial Intelligence Yukang Yan
Introduces fundamental principles and techniques from Artificial Intelligence, including heuristic search, automated reasoning, handling uncertainty, and machine learning, to prepare students for advanced AI courses. Prerequisites: CSC 172 and MTH 150; CSC 173 STRONGLY Recommended. Open to CSC & DSCC majors only during registration week. Restriction will be lifted Monday, November 18.
DSCC 210-1 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 229-01 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 294-01 Nonlinear Optimization Jiaming Liang
This course serves as a modern introduction to the field of optimization. It covers important topics such as convexity, optimality conditions, duality, gradient methods, and Newton's method. The objective is to provide the foundations of theory and algorithms of nonlinear optimization, as well as to present a variety of applications from diverse areas. Prerequisites: The course is intended for advanced undergraduate students with mathematical maturity in multivariate calculus (MATH 164) and linear algebra (MATH 165). Prior knowledge of optimization (MATH 208) is helpful but not required. Students should have good MATLAB or Python programming skills.
DSCC 240-1 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. Prerequisites will be strictly enforced: CSC171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC262; MATH165.
Friday
DSCC 000-1 Data Science Colloquium –
Reserved for weekly data science (GIDS) colloquiums