Beyond courses in the three core disciplines of computer science, business, and statistics—relevant graduate courses in other potential areas of concentration, such as finance, manufacturing, healthcare, national security, engineering, fraud detection, science, smart grid management, sustainability and the like, may be added as electives. Please contact the Program Director for guidance as to what courses are available to graduate students.
Data Science Course Descriptions
DS 501. Introduction to Data Science.
This course provides an overview of Data Science, covering a broad selection of key challenges in and methodologies for working with big data. Topics to be covered include data collection, integration, management, modeling, analysis, visualization, prediction and informed decision making, as well as data security and data privacy. This introductory course is integrative across the core disciplines of Data Science, including databases, data warehousing, statistics, data mining, data visualization, high performance computing, cloud computing, and business intelligence. Professional skills, such as communication, presentation, and storytelling with data, will be fostered. Students will acquire a working knowledge of data science through hands-on projects and case studies in a variety of business, engineering, social sciences, or life sciences domains. Issues of ethics, leadership, and teamwork are highlighted. Prerequisites: None beyond meeting the Data Science admission criteria.
DS 502 / MA 543. Statistical Methods for Data Science.
This course surveys the statistical methods most useful in data science applications. Topics covered include predictive modeling methods, including multiple linear regression, and time series; data dimension reduction; discrimination and classification methods, clustering methods; and committee methods. Students will implement these methods using statistical software. Prerequisites: Statistics at the level of MA 2611 and MA2612 and linear algebra at the level of MA 2071.
DS 503 / CS 585 Big Data Management.
Emerging applications in science and engineering disciplines generate and collect data at unprecedented speed, scale, and complexity that need to be managed and analyzed efficiently. This course introduces the emerging techniques and infrastructures developed for big data management including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. Query processing, optimization, access methods, storage layouts, and energy management techniques developed on these infrastructures will be covered. Students are expected to engage in hands-on projects using one or more of these technologies. Prerequisites: A beginning course in databases at the level of CS4432 or equivalent knowledge, and programming experience.
DS 504 / CS 586. Big Data Analytics.
Innovation and discoveries are no longer hindered by the ability to collect data, but the ability to summarize, analyze, and discover knowledge from the collected data in a scalable fashion. This course covers computational techniques and algorithms for analyzing and mining patterns in large-scale datasets. Techniques studied address data analysis issues related to data volume (scalable and distributed analysis), data velocity (high-speed data streams), data variety (complex, heterogeneous, or unstructured data), and data veracity (data uncertainty). Techniques include mining and machine learning techniques for complex data types, and scale-up and scale-out strategies that leverage big data infrastructures. Real-world applications using these techniques, for instance social media analysis and scientific data mining, are selectively discussed. Students are expected to engage in hands-on projects using one or more of these technologies. Prerequisites: A beginning course in databases and a beginning course in data mining, or equivalent knowledge, and programming experience.
DS 595 Special Topics in Data Science.
An offering of this course will cover a topic of current interest in detail. This serves as a flexible vehicle to provide a one-time offering of topics of current interest as well as to offer new topics before they are made into a permanent course. Prerequisites: will vary with topic.
DS 596 Independent Study.
This course will allow a student to study a chosen topic in Data Science under the guidance of a faculty member affiliated with the Data Science program. The student must produce a written report.
DS 597 Directed Research.
A directed research study, conducted under the guidance of a faculty member affiliated with the Data Science Program, investigates challenges and techniques central to data science, and aims to innovate novel approaches and techniques towards solving these challenges. The student must produce a written report.
DS 598 Graduate Qualifying Project.
This 3-credit graduate qualifying project, typically done in teams, is to be carried out in cooperation with a sponsor or industrial partner. It must be overseen by a faculty member affiliated with the Data Science Program. This offering integrates theory and practice of Data Science, and should include the utilization of tools and techniques acquired in the Data Science Program. In addition to a written report, this project must be presented in a formal presentation to faculty of the Data Science program and sponsors. Professional development skills, such as communication, teamwork, leadership, and collaboration, along with storytelling, will be practiced. Prerequisite: DS 501, completion of at least 24 credits of the DS degree, or consent of the instructor.
DS 599 Master’s Thesis in Data Science.
A thesis in Data Science consists of a research and development project worth (a minimum of) 9 graduate credit hours advised by a faculty member affiliated with the Data Science Program. A thesis proposal must be approved by the DS Program Review Board and the student’s advisor, before the student can register for more than three thesis credits. The student must satisfactorily complete a written thesis document, and present the results to the DS faculty in a public presentation.
List of Program Elective Courses:
Relevant Business Graduate Courses (A maximum of 16 graduate credits of School of Business coursework may count toward the M.S. in Data Science):
- ACC 503. Financial Intelligence for Strategic Decision-Making
- BUS 500. Business Law, Ethics and Social Responsibility
- FIN 500. Financial Information and Management
- FIN 501. Economics for Managers
- MIS 500. Innovating with Information Systems
- MIS 571. Database Applications Development
- MIS 573. Systems Design and Development
- MIS 576. Project Management
- MIS 581. Information Technology Policy and Strategy
- MIS 583. User Experience Applications
- MIS 584. Business Intelligence
- MKT 568. Data Mining Business Applications
- OBC 500. Group and Interpersonal Dynamics in Complex Organizations
- OBC 501. Interpersonal and Leadership Skills
- OIE 500. Analyzing and Designing Operations to Create Value
- OIE 541. Operations Risk Management
- OIE 544. Supply Chain Analysis and Design
- OIE 552. Modeling and Optimizing Processes
- OIE 598. Optimization Methods for Business Analytics
Relevant Computer Science Graduate Courses:
- CS 5084. Introduction to Algorithms: Design and Analysis
- CS 504. Analysis of Computations and Systems
- CS 509. Design of Software Systems
- CS 534. Artificial Intelligence
- CS 539. Machine Learning
- CS 542. Database Management Systems
- CS 561. Advanced Topics in Database Systems
- CS 548. Knowledge Discovery and Data Mining
- CS 584. Algorithms: Design and Analysis
- CS 585/DS 503. Big Data Management
- CS 586/DS 504. Big Data Analytics
- CS 525. Data Visualization (Special Topics in Computer Science)
- CS 525. Mobile Computing (Special Topics in Computer Science)
- CS 525. Topics in Computer Science (with prior approval of the Program Review Committee to determine that topic is relevant to Data Science)
- CS 536. Programming Language Design
- CS 545. Digital Image Processing
- CS 546. Human-Computer Interaction
- CS 549. Computer Vision
Note: Students cannot receive credit for both CS 5084 and CS 584.
Relevant Mathematical Sciences Graduate Courses:
- MA 543/DS 502. Statistical Methods for Data Science
- MA 542. Regression Analysis
- MA 554. Applied Multivariate Analysis
- MA 552. Distribution-Free and Robust Statistical Methods
- MA 550. Time Series Analysis MA 529. Stochastic Processes
- MA 511. Applied Statistics for Engineers and Scientists
- MA 540. Probability and Mathematical Statistics I
- MA 541. Probability and Mathematical Statistics II
- MA 546. Design and Analysis of Experiments
- MA 547. Design and Analysis of Observational and Sampling Studies
- MA 549. Analysis of Lifetime Data
- MA 556. Applied Bayesian Statistics
Relevant Learning Sciences and Technology Program Graduate Courses:
- CS 566. Graphical Models For Reasoning Under Uncertainty
- CS 565. User Modeling
- CS 567. Empirical Methods For Human-Centered Computing
- PSY 505. Advanced Methods and Analysis for the Learning and Social Sciences
Relevant Bioinformatics and Computational Biology Program Courses:
- BCB 501. Bioinformatics
- BCB 502/CS 582. Biovisualization
- BCB 503/CS 583. Biological and Biomedical Database Mining
- BCB 504/MA 584. Statistical Methods in Genetics and Bioinformatics
Relevant Biomedical Engineering Courses:
- BME 595. Special Topics: Machine Learning for Biomedical Informatics
Relevant Electrical and Computer Engineering Department Courses:
- ECE 502. Analysis of Probabilistic Signals And Systems
- ECE 503. Digital Signal Processing
- ECE 504. Analysis Of Deterministic Signals And Systems
- ECE 578/ CS 578. Cryptography and Data Security
- ECE 630. Advanced Topics in Signal Processing
- ECE 673. Advanced Cryptography
- ECE 5311. Information Theory And Coding
Other potential areas of concentration, such as finance, manufacturing, healthcare, national security, engineering, fraud detection, science, smart grid management, sustainability and the like, may be added as electives. Please contact the Program Director for guidance as to what courses are available to graduate students.