Fall Term Schedule : Undergraduate Program : Goergen Institute for Data Science and Artificial Intelligence : �鶹��ý

Undergraduate Program

Fall 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

Fall 2025

Number	Title	Instructor	Time
DSCC 000-1 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 240-01 Data Mining Cantay Caliskan TR 3:25PM - 4:40PM
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 3:25PM - 4:40PM)
DSCC 242-01 Artificial Intelligence Ted Pawlicki TR 3:25PM - 4:40PM
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. Prerequisites: CSC 172 and MTH 150; CSC 173 STRONGLY Recommended. Location (TR 3:25PM - 4:40PM)
DSCC 261-01 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. Prerequisites: CSC 172; CSC 173 and CSC 252 recommended. Location (MW 12:30PM - 1:45PM)
DSCC 265-01 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. Location (MW 3:25PM - 4:40PM)
DSCC 275-1 Time Series Analysis & Forecasting in Data Science Ajay Anand TR 11:05AM - 12:20PM
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 (STAT 180 or STAT 190), Linear Algebra and Differential equations (MATH 165 or equivalent), and applied Python programming (CSC161 or DSCC201 or equivalent) Location (TR 11:05AM - 12:20PM)
DSCC 383W-01 Data Science Capstone 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: 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 390A-01 Supervised Teaching Joseph Ciminelli 7:00PM - 7:00PM
This course offers undergraduate students a structured, credit-bearing opportunity to gain experience in supervised teaching within a college-level classroom setting. Under the mentorship of a faculty member, students assist in course delivery, lead discussions or labs, support instructional design, and participate in pedagogical reflection. Responsibilities and expectations vary by course and department. Location ( 7:00PM - 7:00PM)
DSCC 390A-02 Supervised Teaching Brendan Mort 7:00PM - 7:00PM
This course offers undergraduate students a structured, credit-bearing opportunity to gain experience in supervised teaching within a college-level classroom setting. Under the mentorship of a faculty member, students assist in course delivery, lead discussions or labs, support instructional design, and participate in pedagogical reflection. Responsibilities and expectations vary by course and department. Location ( 7:00PM - 7:00PM)
DSCC 390A-03 Supervised Teaching Ajay Anand 7:00PM - 7:00PM
This course offers undergraduate students a structured, credit-bearing opportunity to gain experience in supervised teaching within a college-level classroom setting. Under the mentorship of a faculty member, students assist in course delivery, lead discussions or labs, support instructional design, and participate in pedagogical reflection. Responsibilities and expectations vary by course and department. Location ( 7:00PM - 7:00PM)
DSCC 391-1 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 391W-1 Independent Study 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)
DSCC 394-1 Internship Ram Haddas 7:00PM - 7:00PM
By departmental Permission Registration for Independent Study courses needs to be completed thru the��. Location ( 7:00PM - 7:00PM)
DSCC 395-1 Independent Research – 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
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 383W-01 Data Science Capstone 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: 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 265-01 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.
Tuesday and Thursday
DSCC 275-1 Time Series Analysis & Forecasting in Data Science Ajay Anand
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 (STAT 180 or STAT 190), Linear Algebra and Differential equations (MATH 165 or equivalent), and applied Python programming (CSC161 or DSCC201 or equivalent)
DSCC 240-01 Data Mining Cantay Caliskan
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 242-01 Artificial Intelligence Ted Pawlicki
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. Prerequisites: CSC 172 and MTH 150; CSC 173 STRONGLY Recommended.
Wednesday
Friday
DSCC 000-1 Data Science Colloquium –
Reserved for weekly data science (GIDS) colloquiums