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 2023

Number	Title	Instructor	Time
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 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 229-1 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 Hylan Building Room 201 (TR 11:05AM - 12:20PM)
DSCC 240-1 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. Prerequisites will be strictly enforced: CSC171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC262; MATH165. Only open to CSC and DSCC majors during registration week. Location Gavett Hall Room 206 (TR 3:25PM - 4:40PM)
DSCC 242-1 Introduction to Artificial Intelligence George Ferguson 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. Prerequisites: CSC 172 and MTH 150; CSC 173 STRONGLY Recommended. Audits not allowed. Open to CSC and DSCC majors only during registration week. Location Wegmans Room 1400 (TR 3:25PM - 4:40PM)
DSCC 261-1 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. Prerequisites: CSC 172; CSC 173 and CSC 252 recommended. Open to CSC and DSCC majors only during registration week. Location Harkness Room 115 (MW 12:30PM - 1:45PM)
DSCC 262-1 Computational Introduction to Statistics Bruce Blaine TR 2:00PM - 3:15PM
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 Location Wegmans Room 1400 (TR 2:00PM - 3:15PM)
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 (DSC 262/STT212/STT213 or equivalent), Linear Algebra and Differential equations (MTH 165 or equivalent), and applied Python programming (CSC161 or DSCC201 or equivalent) Location Dewey Room 2162 (TR 11:05AM - 12:20PM)
DSCC 383W-1 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: 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. 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 Gavett Hall Room 206 (MW 10:25AM - 11:40AM)
DSCC 390A-1 Supervised Teaching - DSCC 262 Joseph Ciminelli –
Departmental Permission Required
DSCC 390A-2 Supervised Teaching - DSCC 201 Brendan Mort –
Departmental Permission Required
DSCC 390A-3 Supervised Teaching - DSCC 275 Ajay Anand –
Departmental Permission Required
DSCC 391-1 Independent Study – –
https://www.rochester.edu/college/ccas/handbook/independent-studies.html Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration.
DSCC 391W-1 Independent Study – –
https://www.rochester.edu/college/ccas/handbook/independent-studies.html Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration.
DSCC 395-1 Independent Research – –
https://www.rochester.edu/college/ccas/handbook/independent-studies.html Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration.
DSCC 395W-1 Independent Research – –
https://www.rochester.edu/college/ccas/handbook/independent-studies.html Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration.

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 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-1 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: 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. 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 261-1 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. Open to CSC and DSCC majors only during registration week.
Tuesday and Thursday
DSCC 229-1 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 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 (DSC 262/STT212/STT213 or equivalent), Linear Algebra and Differential equations (MTH 165 or equivalent), and applied Python programming (CSC161 or DSCC201 or equivalent)
DSCC 262-1 Computational Introduction to Statistics Bruce Blaine
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
DSCC 240-1 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. Prerequisites will be strictly enforced: CSC171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC262; MATH165. Only open to CSC and DSCC majors during registration week.
DSCC 242-1 Introduction to Artificial Intelligence George Ferguson
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. Audits not allowed. Open to CSC and DSCC majors only during registration week.