Undergraduate Program

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 2021

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 using Hadoop and Spark; libraries for machine learning; no-sql data stores; and cloud services. PREREQUISITES: CSC 161, CSC 171 or some equivalent programming experience strongly recommended. WAITLIST: If registration is full, submit your info to our Spring 2021 waitlist at: https://forms.gle/3gBYD73Gi51caoXo9 Location Online Room 13 (ASE) (MW 9:00AM - 10:15AM)
DSCC 202-1 Data Science at Scale Brendan Mort; Lloyd Palum 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 Online Room 21 (ASE) (MW 4:50PM - 6:05PM)
DSCC 210-1 Digital Imaging Gregory Heyworth TR 11:05AM - 12:20PM
Blank Description Location Rettner Hall Room 307 (TR 11:05AM - 12:20PM)
DSCC 240-1 Data Mining Thaddeus Pawlicki 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: Prerequisites will be strictly enforced: CSC171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC262; MATH165. Location Online Room 4 (ASE) (TR 4:50PM - 6:05PM)
DSCC 261-1 Database Systems Eustrat Zhupa MW 3:25PM - 4:40PM
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: CSC172; CSC173 and CSC 252 recommended Location Online Room 5 (ASE) (MW 3:25PM - 4:40PM)
DSCC 263-1 Data Management Systems Fatemeh Nargesian TR 2:00PM - 3:15PM
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) Location Online Room 23 (ASE) (TR 2:00PM - 3:15PM)
DSCC 265-1 Intermed Statistical Methods Yusuf Bilgic TR 4:50PM - 6:05PM
This course is a continuation of DSCC262, covering intermediate statistical methodology and related computational methods, with an emphasis on the R statistical computing environment. PREREQUSITE: DSC262/CSC262 (preferred). STT212 or STT 213 also permitted. Location Online Room 3 (ASE) (TR 4:50PM - 6:05PM)
DSCC 267-1 Database for Bus/Data Sci Pedro Fernandez MW 2:00PM - 3:15PM
Study of the design of databases and data structures for supporting business and data science. Topics include relational database structure and design, structured query language, database management systems, integration of backend database servers, data warehousing and mining, on-line analytical processing, and database application. Survey of new database technologies. WAITLIST: If registration is full, submit your info to our Spring 2021 waitlist at: https://forms.gle/3gBYD73Gi51caoXo9 Location (MW 2:00PM - 3:15PM)
DSCC 383W-1 Data Science Capstone + Practicum Ajay Anand; Lisa Altman; Pedro Fernandez MW 11:50AM - 1:05PM
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. WAITLIST: If registration is full, submit your info to our Spring 2021 waitlist at: https://forms.gle/3gBYD73Gi51caoXo9 Location Online Room 16 (ASE) (MW 11:50AM - 1:05PM)
DSCC 390A-2 Supervised Teaching DSCC 383W Lisa Altman –
Departmental Permission Required
DSCC 390A-3 Supervised Teaching DSCC 265 Lisa Altman –
Departmental Permission Required
DSCC 390A-4 Supervised Teaching DSCC 201 Lisa Altman –
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 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.

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 using Hadoop and Spark; libraries for machine learning; no-sql data stores; and cloud services. PREREQUISITES: CSC 161, CSC 171 or some equivalent programming experience strongly recommended. WAITLIST: If registration is full, submit your info to our Spring 2021 waitlist at: https://forms.gle/3gBYD73Gi51caoXo9
DSCC 383W-1 Data Science Capstone + Practicum Ajay Anand; Lisa Altman; Pedro Fernandez
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. WAITLIST: If registration is full, submit your info to our Spring 2021 waitlist at: https://forms.gle/3gBYD73Gi51caoXo9
DSCC 267-1 Database for Bus/Data Sci Pedro Fernandez
Study of the design of databases and data structures for supporting business and data science. Topics include relational database structure and design, structured query language, database management systems, integration of backend database servers, data warehousing and mining, on-line analytical processing, and database application. Survey of new database technologies. WAITLIST: If registration is full, submit your info to our Spring 2021 waitlist at: https://forms.gle/3gBYD73Gi51caoXo9
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: CSC172; CSC173 and CSC 252 recommended
DSCC 202-1 Data Science at Scale Brendan Mort; Lloyd Palum
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 210-1 Digital Imaging Gregory Heyworth
Blank Description
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)
DSCC 240-1 Data Mining Thaddeus Pawlicki
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: Prerequisites will be strictly enforced: CSC171, CSC 172 and MATH 161. Recommended: CSC 242 or CSC262; MATH165.
DSCC 265-1 Intermed Statistical Methods Yusuf Bilgic
This course is a continuation of DSCC262, covering intermediate statistical methodology and related computational methods, with an emphasis on the R statistical computing environment. PREREQUSITE: DSC262/CSC262 (preferred). STT212 or STT 213 also permitted.
Friday