The MSCSE program is designed for students who intend to become professional engineers in the high-technology industry, as well as for those who desire a modern, general education based on the problems and the promises of a technological society. CSTU offers a friendly atmosphere and a variety of academic programs that have made CSTU engineering graduates highly valued in high-tech firms in the Silicon Valley.

Program Length: 24 months

Semester Credit Hours (Credits): 30

Program Objectives:

• To provide each student the education by tailoring each student's study plan based on the student's background and interests.
• To provide in-depth professional training in the latest technologies.
• To provide relevant laboratory experience throughout each program as an integral part of the education.
• To nurture a learning environment which leads to professional values recognizing high quality and integrity in a true engineer.
• To provide graduate students an opportunity to pursue advanced training and professional development to practice their profession with increased competence.

A minimum of 30 semester credits of graduate study are required for the Master of Science in Computer Systems and Engineering program (MSCSE). They include a few required core courses, a number of elective courses based on the student's selection of technical pursuit, and a required capstone course. The computer systems engineering coursework will develop technical skills beneficial to the student for career planning. The student also has the opportunity to take elective courses outside of computer systems engineering to broaden the student's skillset. Upon clearing background preparation work, the student starts to take courses to meet the degree requirements. The student must begin his/her graduate study with the subjects listed in the Core Course section.

Graduates may gain entry-level employment or higher-level positions that may include such position titles as software engineer, data analyst, data engineer, machine learning engineer and artificial intelligence engineer. The following is a list of job classifications the MSCSE program prepares the graduates for using the United States Department of Labor's Standard:

• 15-1132.00 - Software Developers, Applications
• 15-1152.00 - Computer Network Support Specialists
• 41-9031.00 - Sales Engineers
• 15-1131.00 - Computer Programmers
• 15-1199.01 - Software Quality Assurance Engineers and Testers
• 15-1134.00 - Web Developers
• 15-1111.00 - Computer and Information Research Scientists
• 15-1199.07 - Data Warehousing Specialists

I. Core Requirements (21 credits)

• CSE552 Full Stack Development with AI Assistance
• CSE590 Special Topics
• CSE591 Seminars
• CSE600 Python for AI
• CSE602 Practical User Experience with AI
• CSE604 Machine Learning Fundamental
• CSE606 AI Application with GAN
• CSE608 AI Application with Reinforcement Learning
• CSE610 Cloud Computing and Security
• CSE612 Computer Vision and multimodal (includes VLA models)
• CSE616 Business Intelligence & Analytics and Data-Driven Strategy
• CSE618 Advanced Data Structure and Algorithms in Python
• CSE620 Deep Learning with PyTorch
• CSE622 NoSQL and Big Data Analytics
• CSE624 Network Security
• CSE626 Business Data Analytics & Visualization (Power BI / Tableau)
• CSE628 Machine Learning for NLP
• CSE629 AI Strategy and Solution Engineering for Business
• CSE630 SQL and Database Systems
• CSE631 Network & Communication Fundamentals
• CSE632 Introduction to Cloud Computing
• CSE633 Systems & Infrastructure (&Cloud) Security
• CSE635 Secure Software Engineering
• CSE636 DevOps with AI Assistance
• CSE637 AI Security
• CSE638 Deep Learning with TensorFlow
• CSE641 Robot Operating Systems (ROS/ROS2)
• CSE642 Generative artificial intelligence
• CSE643 Mechatronics and Embedded Systems
• CSE644 Cloud Management
• CSE645 SLAM & Autonomous Navigation and Path Planning
• CSE646 A Practical Course on AI for Industrial Application
• CSE648 ChatGPT application
• CSE650 Digital Integrated Circuit Design using FPGA
• CSE651 Software Development with Agentic AI
• CSE652 Prompt Engineering
• CSE654 AI and Gen AI Application
• CSE656 Network, Security and AI
• CSE658 Advanced Application of Large Language Models
• CSE662 Digital Marketing with AI
• CSE664 GPT Project
• CSE668 Project Management with AI
• CSE674 Data Analytics using Alteryx
• CSE676 Digital Marketing and Social Media Strategy with AI
• CSE678 LLM, LangChain and Applications

II. Electives (6 credits) The student may take any graduate-level courses to meet the requirements of elective courses, including those outside of computer engineering. When applicable, the student may take Curricular Practicum courses and engage in practical training to work on company projects that are directly related to the student's course of study. No more than 6 credits of practicum coursework may be counted towards graduation. Part-time CPT is 1 credit and full-time CPT is 2 credits. Part-time CPT together with concurrent 9 course credits and full-time CPT together with 6 concurrent course credits are considered full-time study. Each 1 credit of a practicum course requires at least 45 hours of practical experience related to the student's program curriculum.

• CSE598 Computer Systems Engineering Internship

III. Capstone Course CSE599 (3 credits) (A required subject) Students are required to start the capstone project as early as possible and under the guidance of the school adviser, integrate the knowledge and skills learned from the courses taken during the program.

• CSE599 Computer Systems Engineering Capstone

IV. Graduation RequirementsThe Master of Science in Computer Systems and Engineering (MSCSE) degree program requires a minimum of 30 credits of graduate-level courses. The MSCSE degree program requires coursework in the following categories:

• Core Requirements,
• Electives, and
• A Capstone Course.

The following are required for graduation:

• A graduate student admitted with undergraduate deficiencies must clear the deficiencies in the early terms. The student may clear a subject by either taking the course and earning a passing grade or passing a proficiency exam on the subject,
• Maintain a grade of C- or better for all courses taken to clear deficiencies or towards the degree requirements,
• Maintain an overall G.P.A. of 3.0 or better,
• Maintain good standing with the university – with a clear financial, library, and other school records,
• The student is approved to graduate after filing a petition for graduation.

Students received a master's degree upon successful completion of the program.