|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
This course explores time series prediction using Python to build classical, machine learning, and deep learning models. Through coding projects, students develop cutting-edge skills in analyzing complex data, programming predictive models, and applying techniques to real-world problems. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): DATA 5600 or ECN 4330 or STAT 5100 Prerequisite Recommendation(s): Prior experience with Python programming is strongly recommended Dual-listed as: DATA 5630 Repeatable for credit: No Grade Mode: Standard
-
3.00 Credits
Computational finance is an interdisciplinary pillar of modern FinTech, at the intersection of data science, computer science, and economics. In this course students apply tools from computational science and statistics to identify and exploit arbitrage opportunities for entrepreneurial financial innovation. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite Recommendation(s): Students should have advanced Python programming experience, along with prior coursework in probability, statistics, and introductory finance Dual-listed as: DATA 5690
-
3.00 Credits
Prediction is a core activity of modern FinTech. This course uses probability, computational statistics, machine learning, and financial econometrics to develop and implement predictive strategies for trading and risk management by identifying and exploiting arbitrage opportunities. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): DATA 5690 or DATA 6690 Dual listed as: DATA 5695
-
2.00 - 3.00 Credits
Provides a conceptual and practical overview of analytical tools, techniques, and practices used to support date-driven decision making in an organization.
-
1.00 Credits
Laboratory for DATA 6860 allowing students to complete assigned class projects (required only for DATA students who enroll in DATA 6860)
-
3.00 Credits
This capstone course for the Master of Data Analytics program requires students to work on an applied real-world project, supervised by MDATA faculty. Prerequisite/Restriction: Admission to the Master of Data Analytics program
-
2.00 Credits
This course introduces the theories, practices, and application of visualizations and the relevant ethical considerations of their use. This knowledge will be the foundation for future visualization efforts and increase technical capacity in the use of visualization.
-
4.00 Credits
This studio course is focused on the use of Unreal Engine to create visualizations. Developed to support the video game industry, Unreal Engine is increasingly being used in design and visualization to create realistic representations of proposed architecture and landscapes.
-
4.00 Credits
First course in automotive or diesel technology. Students will gain needed skills in shop safety and other basic skills that will prepare students for specific automotive or diesel courses. The following topics will be covered: using manual and information systems, precision measurement, tires and wheels, bearings, headlamp adjustment, oils and fluids, cleaning methods, gaskets and sealants, cooling systems and belts and hoses. Course can be articulated with high school automotive courses. Cross-listed as: AUTO 1000
-
4.00 Credits
Designed to instruct the student on correct diesel engine overhaul procedures from disassembly to assembly. Identification, operation, inspection, repair, maintenance and failure analysis of each diesel engine component will be discussed. Attention is also given to parts cleaning methods as well as fasteners and measuring tools. Prerequisite/Restriction: MATH 0990 or a higher level MATH course.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Cookies Policy |
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2025 AcademyOne, Inc.
|
|
|