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.
Fall 2024
| Number | Title | Instructor | Time |
|---|
|
DSCC 000-1
–
F 11:00AM - 3:30PM
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Reserved for weekly data science (GIDS) colloquiums
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DSCC 201-01
Brendan Mort
MW 9:00AM - 10:15AM
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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.
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DSCC 240-1
Cantay Caliskan
TR 3:25PM - 4:40PM
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|
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.
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DSCC 242-1
Ted Pawlicki
TR 3:25PM - 4:40PM
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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.
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DSCC 261-1
Eustrat Zhupa
MW 12:30PM - 1:45PM
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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.
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DSCC 265-01
Yukun Ma
TR 2:00PM - 3:15PM
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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. DSCC 240 strongly recommended.
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DSCC 275-1
Ajay Anand
TR 11:05AM - 12:20PM
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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)
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DSCC 383W-01
Ajay Anand; Cantay Caliskan
MW 10:25AM - 11:40AM
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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.
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DSCC 390A-01
Joseph Ciminelli
7:00PM - 7:00PM
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Departmental Permission Required
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DSCC 390A-02
Brendan Mort
7:00PM - 7:00PM
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Departmental Permission Required
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DSCC 390A-03
Ajay Anand
7:00PM - 7:00PM
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Departmental Permission Required
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DSCC 391-1
–
7:00PM - 7:00PM
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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.
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DSCC 395-1
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7:00PM - 7:00PM
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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.
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Fall 2024
| Number | Title | Instructor | Time |
|---|---|
| Monday and Wednesday | |
|
DSCC 201-01
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
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. |
|
|
DSCC 261-1
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 275-1
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 265-01
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. PRE-REQUISITES: DSCC/CSC/STAT 262 or STAT 212 or STAT 213 or equivalent introductory statistics background. DSCC 240 strongly recommended. |
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|
DSCC 240-1
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. |
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|
DSCC 242-1
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. Prerequisites: CSC 172 and MTH 150; CSC 173 STRONGLY Recommended. Audits not allowed. Open to CSC and DSCC majors only during registration week. |
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| Friday | |
|
DSCC 000-1
–
|
|
|
Reserved for weekly data science (GIDS) colloquiums |
|