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 2026

Number	Title	Instructor	Time
DSCC 000-01 Data Science Colloquium – F 11:00AM - 3:30PM
Reserved for weekly data science (GIDS) colloquiums Location (F 11:00AM - 3:30PM)
DSCC 201-01 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 (MW 9:00AM - 10:15AM)
DSCC 202-01 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. PREREQUSITE: DSCC 201/401 or instructor permission Location (MW 4:50PM - 6:05PM)
DSCC 210-01 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 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 (TR 11:05AM - 12:20PM)
DSCC 240-01 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. Prerequisites will be strictly enforced: CSC 171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC 262; MATH 165. Location (TR 9:40AM - 10:55AM)
DSCC 242-01 Artificial Intelligence Adam Purtee TR 4:50PM - 6:05PM
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. This course is available to majors only during the registration period. Location (TR 4:50PM - 6:05PM)
DSCC 261-01 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. Prerequisites: CSC 172; CSC 173 and CSC 252 recommended. Location (MW 2:00PM - 3:15PM)
DSCC 263-01 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. Prerequisites:CSC 173 and CSC 252 (or CSC 261) Location (MW 9:00AM - 10:15AM)
DSCC 265-02 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 383W-01 Data Science Capstone Ajay Anand 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 STAT 180 or STAT 190 or equivalent. DSCC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. GRADUATING SENIORS 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 391-01 Independent Study Caitlin Dreisbach 7:00PM - 7:00PM
This course provides undergraduate students the opportunity to pursue in-depth, independent exploration of a topic not regularly offered in the curriculum, under the supervision of a faculty member in the form of independent study, practicum, internship or research. The objectives and content are determined in consultation between students and full-time members of the teaching faculty. Responsibilities and expectations vary by course and department. Registration for Independent Study courses needs to be completed through the Independent Study Registration form (https://secure1.rochester.edu/registrar/forms/independent-study-form.php) Location ( 7:00PM - 7:00PM)
DSCC 391W-01 Independent Study – 7:00PM - 7:00PM
This course provides undergraduate students the opportunity to pursue in-depth, independent exploration of a topic not regularly offered in the curriculum, under the supervision of a faculty member in the form of independent study, practicum, internship or research. The objectives and content are determined in consultation between students and full-time members of the teaching faculty. Responsibilities and expectations vary by course and department. Registration for Independent Study courses needs to be completed through the Independent Study Registration form (https://secure1.rochester.edu/registrar/forms/independent-study-form.php) Location ( 7:00PM - 7:00PM)
DSCC 395-02 Independent Research Dongmei Li 7:00PM - 7:00PM
This course provides undergraduate students the opportunity to pursue in-depth, independent exploration of a topic not regularly offered in the curriculum, under the supervision of a faculty member in the form of independent study, practicum, internship or research. The objectives and content are determined in consultation between students and full-time members of the teaching faculty. Responsibilities and expectations vary by course and department. Registration for Independent Study courses needs to be completed through the Independent Study Registration form (https://secure1.rochester.edu/registrar/forms/independent-study-form.php) Location ( 7:00PM - 7:00PM)
DSCC 395-07 Independent Research Sullafa Kadura 7:00PM - 7:00PM
This course provides undergraduate students the opportunity to pursue in-depth, independent exploration of a topic not regularly offered in the curriculum, under the supervision of a faculty member in the form of independent study, practicum, internship or research. The objectives and content are determined in consultation between students and full-time members of the teaching faculty. Responsibilities and expectations vary by course and department. Registration for Independent Study courses needs to be completed through the Independent Study Registration form (https://secure1.rochester.edu/registrar/forms/independent-study-form.php) Location ( 7:00PM - 7:00PM)
DSCC 395W-01 Independent Research Cantay Caliskan 7:00PM - 7:00PM
This course provides undergraduate students the opportunity to pursue in-depth, independent exploration of a topic not regularly offered in the curriculum, under the supervision of a faculty member in the form of independent study, practicum, internship or research. The objectives and content are determined in consultation between students and full-time members of the teaching faculty. Responsibilities and expectations vary by course and department. Registration for Independent Study courses needs to be completed through the Independent Study Registration form (https://secure1.rochester.edu/registrar/forms/independent-study-form.php) Location ( 7:00PM - 7:00PM)

Number	Title	Instructor	Time
Monday and Wednesday
DSCC 201-01 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 263-01 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. Prerequisites:CSC 173 and CSC 252 (or CSC 261)
DSCC 383W-01 Data Science Capstone Ajay Anand
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 STAT 180 or STAT 190 or equivalent. DSCC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. GRADUATING SENIORS 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 261-01 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. Prerequisites: CSC 172; CSC 173 and CSC 252 recommended.
DSCC 202-01 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. PREREQUSITE: DSCC 201/401 or instructor permission
Tuesday
Tuesday and Thursday
DSCC 240-01 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. Prerequisites will be strictly enforced: CSC 171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC 262; MATH 165.
DSCC 210-01 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 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 265-02 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.
DSCC 242-01 Artificial Intelligence Adam Purtee
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. This course is available to majors only during the registration period.
Thursday
Friday
DSCC 000-01 Data Science Colloquium –
Reserved for weekly data science (GIDS) colloquiums