CEDAR Curriculum & Course Requirements
Our courses support students learning from real-world, data-driven projects related to sustainable engineering embedded within the context of circular economy. Our goal is to educate students not simply to be advanced problem solvers, but to be innovators who holistically identify approaches leveraging data-driven techniques to create sustainable systems and environments.
Students will take introductory and advanced experiential coursework (total of 12 credit hours, intended to be accommodated within the student’s existing credit requirements). Students can identify their own plan of study to take courses related to their area of application/interest to fulfil CEDAR requirements. Additional courses can be found in the Graduate Student Catalog. Students will work with their advisor to identify a plan of study that fits within their program/area of interest.
To fulfill the requirements for a Certificate in Data Science, take the following:
Course 1: DS501 (or DS595) is required for all students.
Course 2 and Course 3: Two additional courses must be from the Data Science core courses listed in the graduate catalog, and
Course 4 and Course 5 (if needed to fulfill 12 credits): The remaining credits can be earned from any Data Science elective course(s) listed in the graduate catalog or otherwise approved by the program.
For students lacking a background in either the computational side or the statistical side, they should consider taking one of the following ramp up courses as the elective course, which will help them building the needed background.
DS 517. Mathematical Foundations for Data Science
CS 5007. Intro to Applications of CS with Data Structures and Algorithms (Programming for non-CS
NEW COURSE (Spring 2022)
DS595: Machine Learning for Engineering and Science Applications
Instructor: Dr. Randy Paffenroth
Mondays/Thursdays from 1:00PM – 2:20PM. No Pre-reqs required.
Details: Looking to blend your affinity for Data Science and Machine Learning to problems in engineering and sciences? DS595 is just the course you have been waiting for. Topics covered in this course will include predictive modeling, feature engineering, and model assessment, with a particular focus on working successfully even with small data sets. In this course, you will learn about and apply algorithms with wide applicability in engineering and physical sciences including classic techniques such as multiple linear regression, more advanced methods such as random forests, and state-of-the-art techniques such as deep neural networks.
Other Courses include (but are not limited to):
Data Science Graduate Courses
DS 502. STATISTICAL METHODS FOR DATA SCIENCE
DS 503. BIG DATA MANAGEMENT
DS 504. BIG DATA ANALYTICS
DS 517. MATHEMATICAL FOUNDATIONS FOR DATA SCIENCE
DS 541. DEEP LEARNING
DS 577. MACHINE LEARNING IN CYBERSECURITY
DS 595. MACHINE LEARNING FOR ENGINEERING AND SCIENCE APPLICATIONS
DS 597. DIRECTED RESEARCH
DS 598. GRADUATE QUALIFYING PROJECT
Data Science Component
MA 511. Applied Statistics for Engineers and Scientists
CHE 515. Research Analysis and Design
BCB 501. Bioinformatics
BCB 502. Biovisualization
BCB 503. Biological and biomedical database mining
BCB 504. Statistical methods in genetics and Bioinformatics
BB 553. Experimental design and statistics in the life sciences.
CS 534. Artificial Intelligence
CS 539. Machine learning
CS 541. Deep learning
CS 573. Data visualization
CS 582 Biovisualization
CS 583. Biological and biomedical database mining
CS 585. Big data management
CS 586. Big data analytics
MIS 502. Data management for analytics
MKT 568. Data mining business applications.
AE 5103. Computational Fluid Dynamics
CH/CHE 554. Molecular Modeling
MA 508. Mathematical Modeling
CE 524. Finite element method and applications.
CE 5303. Applied finite element methods in engineering.
CS 566. Graphical models for reasoning under uncertainty.
CS 568. Artificial intelligence for adaptive educational technology
FP 520. Fire Modeling
ME 5103. Computational fluid dynamics
SD 551. Modeling and experimental analysis of complex problems
SD 553. Model analysis and evaluation techniques
SYS 521. Model based systems engineering
SYS 540. Introduction to systems thinking.
Strong Mathematical Component
MA 501. Engineering Mathematics
MA 510. Numerical Methods.
MA 514. Numerical Linear Algebra
Any course from Math department
CHE 504: Mathematical Analysis in Chemical Engineering
CS 522. Numerical methods
ME 5001. Applied Numerical Methods in Engineering
ME 5108. Introduction to computational fluid dynamics
MPE 550. Computational methods in physics