Fall Term Schedule
Only courses with a DSC course number are listed on this page. See MS program for all of the required and elective courses for the degree.
Fall 2023
Number | Title | Instructor | Time |
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DSCC 401-1
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.
|
DSCC 420-1
Gonzalo Mateos Buckstein
MW 4:50PM - 6:05PM
|
The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes. Prerequisites: ECE 242 or equivalent
|
DSCC 440-2
Jiebo Luo
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.
|
DSCC 449-1
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.
|
DSCC 461-1
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.
|
DSCC 462-2
Anson Kahng
TR 4:50PM - 6:05PM
|
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 (or equivalent discrete math and calculus coursework)
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DSCC 475-1
Ajay Anand
TR 11:05AM - 12:20PM
|
Description: 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 equivalent)
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DSCC 483-1
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. FOR DSC GRADUATING SENIORS and MS CANDIDATES. GRADUATING STUDENTS 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 494-01
Cantay Caliskan
|
Internship must have approval of ASE graduate practical research internship. |
DSCC 494-02
Ajay Anand
|
Blank Description |
DSCC 495-9
Cantay Caliskan
|
The course will focus on investigation of current literature on machine learning and artificial intelligence applications for emotion recognition and exploration/collection of image and audio data for the empirical analysis emotions both as a cause and as an outcome. The course is designed to eventually help all parties involved to produce a paper in the area of emotion recognition. The research will help expand the literature in the areas of computational social science (at large), and political communication. Course evaluation will be based on: (1) The compilation of datasets that will be used for empirical analysis (i) (2) the compilation of literature review related to emotion recognition for audio and video data (ii), (3) evaluation of code and algorithms for emotion recognition (iii), (4) attendance to weekly meetings and the submission of progress reports (iv). |
DSCC 895-1
|
Blank Description |
DSCC 897-1
Ajay Anand
|
Please see advisor before enrolling. |
DSCC 899-1
Ajay Anand
|
see advisor before enrolling |
Fall 2023
Number | Title | Instructor | Time |
---|---|
Monday and Wednesday | |
DSCC 401-1
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 483-1
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. FOR DSC GRADUATING SENIORS and MS CANDIDATES. GRADUATING STUDENTS this semester have priority for eligibility/instructor permission. PERMISSION REQUEST: To seek instructor permission/eligibility, follow directions on UR Student. |
|
DSCC 461-1
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. |
|
DSCC 420-1
Gonzalo Mateos Buckstein
|
|
The goal of this course is to learn how to model, analyze and simulate stochastic systems, found at the core of a number of disciplines in engineering, for example communication systems, stock options pricing and machine learning. This course is divided into five thematic blocks: Introduction, Probability review, Markov chains, Continuous-time Markov chains, and Gaussian, Markov and stationary random processes. Prerequisites: ECE 242 or equivalent |
|
Tuesday and Thursday | |
DSCC 449-1
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 475-1
Ajay Anand
|
|
Description: 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 equivalent) |
|
DSCC 440-2
Jiebo Luo
|
|
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. |
|
DSCC 462-2
Anson Kahng
|
|
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 (or equivalent discrete math and calculus coursework) |