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 2018

Number	Title	Instructor	Time
DSC 401 (DSC 401) TOOLS FOR DATA SCIENCE MORT B 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. Fall offering for data science majors and graduate students only; spring offering will be open to non-majors. BUILDING: GAVET \| ROOM: 206 PREREQUISITES: Restricted to DSC majors and graduate students.
DSC 420 (ECE 440) INTRODUCTION TO RANDOM PROCESSES MATEOS BUCKST 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. BUILDING: GAVET \| ROOM: 206 PREREQUISITES: ECE242 or equivalent
DSC 440 (CSC 240) DATA MINING PAWLICKI T TR 12:30PM - 1:45PM
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. BUILDING: GRGEN \| ROOM: 101 PREREQUISITES: Prerequisites will be strictly enforced: CSC171, CSC 172 and MTH 161. Recommended: CSC 242 or CSC262; MTH165.
DSC 461 (CSC 261) DATABASE SYSTEMS ZHUPA E 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. BUILDING: HARK \| ROOM: 115 PREREQUISITES: CSC172; CSC173 and CSC 252 recommended
DSC 462 (DSC 462) COMPUTATIONAL INTRODUCTION TO STATISTICS ALMUDEVAR A TR 2:00PM - 3:15PM
This course will cover foundational concepts in probability and statistical inference, with an emphasis on topics of interest to computer scientists. Following an introduction to elementary probability theory, topics will include applications of combinatorics; Markov chains; principles of statistical classification (Bayes' rule, sensitivity and specificity, ROC curves) and random number generation. The theory of statistical estimation and hypothesis testing will be introduced, and applied to one and two sample inference for population means, proportions, variances and correlations. Nonparametric procedures will be discussed. Topics also include statistical modeling (ANOVA, simple and multiple regression), and computational methods. Students will be introduced to the R statistical computing environment. BUILDING: HYLAN \| ROOM: 202 PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171
DSC 483 (DSC 383W) DATA SCIENCE CAPSTONE ANAND A 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. BUILDING: MOREY \| ROOM: 501 PREREQUISITES: DSC 240/440; DSC 261/461 recommended prior or concurrently
DSC 491 MASTER'S READING COURSE – –
No description BUILDING: \| ROOM:
DSC 494 INTERNSHIP – –
No description BUILDING: \| ROOM:
DSC 530 (DSC 530) METHODS IN DATA-ENABLED RESEARCH INTO HUMAN BEHAVIOR AND ITS COGNITIVE AND NEURAL MECHANISMS JACOBS R MW 10:25AM - 11:40AM
This course provides a hand-on introduction to experimental and analytical methods in cognitive science and artificial intelligence. Each year, it offers three modules from a rotating list, including topics such as brain imaging, computational linguistics, and computer vision. The course is open to graduate students in any discipline. The course is recommended for who intend to pursue research in the 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. The modules are on Bayesian neuroscience, computational models in cognitive science, and natural language dialog systems. BUILDING: MEL \| ROOM: 269 PREREQUISITES: none
DSC 895 CONT OF MASTER'S ENROLLMENT – –
No description BUILDING: \| ROOM:
DSC 897 MASTERS DISSERTATION – –
No description BUILDING: \| ROOM:
DSC 985 LEAVE OF ABSENCE – –
No description BUILDING: \| ROOM:

Number	Title	Instructor	Time
Monday and Wednesday
DSC 401 (DSC 401) TOOLS FOR DATA SCIENCE MORT B 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. Fall offering for data science majors and graduate students only; spring offering will be open to non-majors. BUILDING: GAVET \| ROOM: 206 PREREQUISITES: Restricted to DSC majors and graduate students.
DSC 483 (DSC 383W) DATA SCIENCE CAPSTONE ANAND A 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. BUILDING: MOREY \| ROOM: 501 PREREQUISITES: DSC 240/440; DSC 261/461 recommended prior or concurrently
DSC 530 (DSC 530) METHODS IN DATA-ENABLED RESEARCH INTO HUMAN BEHAVIOR AND ITS COGNITIVE AND NEURAL MECHANISMS JACOBS R MW 10:25AM - 11:40AM
This course provides a hand-on introduction to experimental and analytical methods in cognitive science and artificial intelligence. Each year, it offers three modules from a rotating list, including topics such as brain imaging, computational linguistics, and computer vision. The course is open to graduate students in any discipline. The course is recommended for who intend to pursue research in the 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. The modules are on Bayesian neuroscience, computational models in cognitive science, and natural language dialog systems. BUILDING: MEL \| ROOM: 269 PREREQUISITES: none
DSC 461 (CSC 261) DATABASE SYSTEMS ZHUPA E 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. BUILDING: HARK \| ROOM: 115 PREREQUISITES: CSC172; CSC173 and CSC 252 recommended
DSC 420 (ECE 440) INTRODUCTION TO RANDOM PROCESSES MATEOS BUCKST 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. BUILDING: GAVET \| ROOM: 206 PREREQUISITES: ECE242 or equivalent
Tuesday and Thursday
DSC 440 (CSC 240) DATA MINING PAWLICKI T TR 12:30PM - 1:45PM
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. BUILDING: GRGEN \| ROOM: 101 PREREQUISITES: Prerequisites will be strictly enforced: CSC171, CSC 172 and MTH 161. Recommended: CSC 242 or CSC262; MTH165.
DSC 462 (DSC 462) COMPUTATIONAL INTRODUCTION TO STATISTICS ALMUDEVAR A TR 2:00PM - 3:15PM
This course will cover foundational concepts in probability and statistical inference, with an emphasis on topics of interest to computer scientists. Following an introduction to elementary probability theory, topics will include applications of combinatorics; Markov chains; principles of statistical classification (Bayes' rule, sensitivity and specificity, ROC curves) and random number generation. The theory of statistical estimation and hypothesis testing will be introduced, and applied to one and two sample inference for population means, proportions, variances and correlations. Nonparametric procedures will be discussed. Topics also include statistical modeling (ANOVA, simple and multiple regression), and computational methods. Students will be introduced to the R statistical computing environment. BUILDING: HYLAN \| ROOM: 202 PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171
TBA
DSC 491 MASTER'S READING COURSE – –
No description BUILDING: \| ROOM:
DSC 494 INTERNSHIP – –
No description BUILDING: \| ROOM:
DSC 895 CONT OF MASTER'S ENROLLMENT – –
No description BUILDING: \| ROOM:
DSC 897 MASTERS DISSERTATION – –
No description BUILDING: \| ROOM:
DSC 985 LEAVE OF ABSENCE – –
No description BUILDING: \| ROOM: