Graduate Program

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.

Sortable | Group by Weekday

Fall 2020

Number	Title	Instructor	Time
DSCC 401-1 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 Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7 Location Online Room 1 (ASE) (MW 9:00AM - 10:15AM)
DSCC 420-1 Intro to Random Processes 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 Location Wegmans Room 1400 (MW 4:50PM - 6:05PM)
DSCC 440-1 Data Mining 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. Location Hoyt Hall Room 104 (TR 3:25PM - 4:40PM)
DSCC 461-1 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. Location Hutchison Hall Room 141 (MW 12:30PM - 1:45PM)
DSCC 462-1 Computational Introduction to Statistics Joseph Ciminelli 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 WAITLIST: If registration is full, submit your info to our Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7 Location (TR 2:00PM - 3:15PM)
DSCC 462-2 Computational Introduction to Statistics Joseph Ciminelli 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 WAITLIST: If registration is full, submit your info to our Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7 Location Online Room 3 (ASE) (TR 2:00PM - 3:15PM)
DSCC 475-1 Time Series Analysis & Forecasting in Data Science 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) WAITLIST: If registration is full, submit your info to our Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7 Location Online Room 4 (ASE) (TR 11:05AM - 12:20PM)
DSCC 481-1 Artificial Intelligence and Deep Learning in Healthcare Jack Teitel MW 3:25PM - 4:40PM
This course, taught by an in-industry data scientist, will focus on how to take machine learning and apply it to healthcare. The first half of the course will cover significant medical content, such as what medical data looks like, where it comes from, and how to handle it. In addition, we will cover the basics of machine learning algorithms such as SVMs, Decision Forests, and Neural Networks, and how to specifically apply these algorithms to medical data. In the second half we will go into deep learning, specifically in the case of using CNNs to process a variety of medical images for tasks such as classification, regression, and segmentation. Throughout the course we will have several guest lectures and project walkthroughs, designed to give specific examples of how to utilize the techniques taught in this course in a real-life setting. Having prior machine learning experience will be helpful, but is not a requirement. This course will be open to undergraduate students only with instructor permission. PREREQUISITES: DSC262 or equivalent statistics course; CSC 161 or CSC 171 or equivalent introductory programming. PERMISSION REQUEST: To seek instructor permission, submit your info to our Fall 2020 list at: https://forms.gle/yc8u4rs5raBoeW2V7 Location Dewey Room 2110D (MW 3:25PM - 4:40PM)
DSCC 483-1 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: DSCC 240/440 (Data Mining) AND an introductory statistics course such as DSCC/CSC/STT 262, STT 212, STT213 or equivalent; DSCC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. PERMISSION REQUEST: To seek instructor permission, submit your info to our Fall 2020 list at: https://forms.gle/yc8u4rs5raBoeW2V7 Location Goergen Hall Room 108 (MW 11:50AM - 1:05PM)
DSCC 491-1 Master's Reading Course Ajay Anand –
To register for Independent Study, contact program advisor before registering.
DSCC 494-2 Internship – –
No description
DSCC 494-3 Internship – –
No description
DSCC 494-8 Internship Zhiyao Duan –
Blank Description
DSCC 495-1 Master's Research Ajay Anand –
Notify advisor before enrolling
DSCC 530-1 Data-Enabled Research Ehsan Hoque F 11:00AM - 12:30PM T 5:00PM - 7:00PM
This course provides an introduction to experimental and analytical methods in cognitive science and artificial intelligence. This class will comprise of a monthly seminar following by an intensive discussion on the assigned reading topics. The course is open to graduate students in any discipline with application. The course is recommended for those who intend to pursue research at the intersection of cognitive science and computer science, but prior experience in those fields is not required. It is required for students supported by the BCS/CS NRT graduate training grant. Location (F 11:00AM - 12:30PM) (T 5:00PM - 7:00PM)
DSCC 895-1 Cont of Master's Enrollment – –
Blank Description
DSCC 897-1 Masters Dissertation Ajay Anand –
Please see advisor before enrolling.
DSCC 899-1 Master's Dissertation Ajay Anand –
see advisor before enrolling
DSCC 985-1 Leave of Absence – –
No description

Number	Title	Instructor	Time
Monday and Wednesday
DSCC 401-1 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 Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7
DSCC 483-1 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: DSCC 240/440 (Data Mining) AND an introductory statistics course such as DSCC/CSC/STT 262, STT 212, STT213 or equivalent; DSCC 261/461 (Database Systems) strongly recommended prior but may be taken concurrently. PERMISSION REQUEST: To seek instructor permission, submit your info to our Fall 2020 list at: https://forms.gle/yc8u4rs5raBoeW2V7
DSCC 461-1 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.
DSCC 481-1 Artificial Intelligence and Deep Learning in Healthcare Jack Teitel
This course, taught by an in-industry data scientist, will focus on how to take machine learning and apply it to healthcare. The first half of the course will cover significant medical content, such as what medical data looks like, where it comes from, and how to handle it. In addition, we will cover the basics of machine learning algorithms such as SVMs, Decision Forests, and Neural Networks, and how to specifically apply these algorithms to medical data. In the second half we will go into deep learning, specifically in the case of using CNNs to process a variety of medical images for tasks such as classification, regression, and segmentation. Throughout the course we will have several guest lectures and project walkthroughs, designed to give specific examples of how to utilize the techniques taught in this course in a real-life setting. Having prior machine learning experience will be helpful, but is not a requirement. This course will be open to undergraduate students only with instructor permission. PREREQUISITES: DSC262 or equivalent statistics course; CSC 161 or CSC 171 or equivalent introductory programming. PERMISSION REQUEST: To seek instructor permission, submit your info to our Fall 2020 list at: https://forms.gle/yc8u4rs5raBoeW2V7
DSCC 420-1 Intro to Random Processes 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 475-1 Time Series Analysis & Forecasting in Data Science 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) WAITLIST: If registration is full, submit your info to our Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7
DSCC 462-1 Computational Introduction to Statistics Joseph Ciminelli
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 WAITLIST: If registration is full, submit your info to our Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7
DSCC 462-2 Computational Introduction to Statistics Joseph Ciminelli
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 WAITLIST: If registration is full, submit your info to our Fall 2020 waitlist at: https://forms.gle/yc8u4rs5raBoeW2V7
DSCC 440-1 Data Mining 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.
Friday
DSCC 530-1 Data-Enabled Research Ehsan Hoque
This course provides an introduction to experimental and analytical methods in cognitive science and artificial intelligence. This class will comprise of a monthly seminar following by an intensive discussion on the assigned reading topics. The course is open to graduate students in any discipline with application. The course is recommended for those who intend to pursue research at the intersection of cognitive science and computer science, but prior experience in those fields is not required. It is required for students supported by the BCS/CS NRT graduate training grant.