General Program Requirements
30 credits required for degree completion
Minimum GPA of 3.0 is required for graduation
Education Objectives
This is an applied program that help students find their role in the tech workplace. Our curriculum allows students to learn and integrate many programming languages used in data science along with cloud platforms to be full stack data scientist or software engineers. From statistical and data visualization programming languages such as R, Python, and Apache Spark to cloud platforms such as AWS. Students will gains hands on experience, and work with faculty across fields in their capstone projects.
Degree Requirements scroll down for a default schedule
Required Courses |
18 credits |
|
Six courses (3 cr. each)
|
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Supervised Project or Thesis Course |
3-6 credits |
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3 credits
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6 credits
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Electives Courses |
6-9 credits |
| Current electives (3 cr. each) | |
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CSc I0220- Secure Cloud Computing
CSc I0230- Web Security
CSc I0420- Secure Operating Systems
CSc I0600- Fundamental Algorithms
CSc I0802- Web/ Geographical Information Systems
CSc I1000- Database Systems I
CSc I1100- Database Systems II
CSc I1301- Privacy for Data Scientist
CSc I1500- Artificial Intelligence
CSc I1600- Neural Language Processing
CSc I1900- Pattern Recognition and Machine Learning
CSc I1910- Deep Neural Networks
CSc I4490- Adversarial AI
CSc I4900- Computer Security
CSc I6710- Computer Vision
DSE G2200- Machine Learning for Finance and Trading
EAS B9035- Introduction to GIS
ECO B2000- Statistics and Introduction to Econometrics
ECO B9950- Advance Financial Analysis and Decision Making
EE I2200- Image Processing
EE I5500- Introduction to Mobile Robotics
EE I5600- Advanced Mobile Robotics
EE I6400- Computer-Aided VLSI Circuits Design
BIO B9700- Special Topics Course
BME I5000- Medical Imaging and Image Processing
BME I5100- Biomedical Signal Processing
BME I4200- Organ Transportation and Pharmacokinetics
CHE I5500- Interfacial Phenomena
CHE I5700- Advance Materials Engineering
CHE I8900- Nanotechnology
CE H6600- Engineering Hydrology
ENGR I6730- Data Reduction in the Physical Sciences
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* Courses in other areas or transfer/e-permit courses are reviewed by the faculty and department by request.
Default Schedule:
First semester, first year:
DSE I1020 Introduction to Data Science
DSE I1030 Applied Statistics
DSE I2700 Visual Analytics
Second semester, first year:
DSE I2100 Applied Machine Learning and Data Mining
DSE I2400 Data Engineering: Infrastructure and Applications
DSE I2450 Big Data and Scalable Computation
Third semester (Fall, second year): (Thesis Option)
Elective 1
DSE I9800 Master's Thesis
Fourth Semester (Spring, second year): (Thesis Option)
Elective 2
OR
Third Semester (Fall, second year): (Project Option)
Elective 1
Elective 2
DSE I9800 Master's Project
Fourth Semester (Spring, second year): (Project Option)
Elective 3
Last Updated: 02/24/2025 13:37