Purpose

The 30-credit Master of Science in Computer Science is designed to equip students with the necessary theoretical fundamentals as well as knowledge about cutting-edge application tools to solve data science and software development problems. Students choose from two specialization tracks; Software Development or Data Science.

In the Software Development specialization, students are trained in the software development processes, including mobile and distributed data development, and software assurance. 

In the Data Science specialization, students are introduced to and trained in the mathematical methods of data science, computational data analysis, machine learning, and applications in big data. 

Program Objectives

For all students in the M.S. program in Computer Science:

1. Acquire a working knowledge of the theoretical foundations of Computer Science
2. Develop critical thinking, problem-solving and analytical skills

Additionally, for students in the Software Development specialization: 

   3. Develop software design and development skills

Additionally, for students in the Data Science specialization: 

Program Goals: 

  4. Develop and in-depth understanding of the key technologies in computer science

Student Learning Outcomes (SLO’s)

Students who graduate with a Master’s in Computer Science will be able to:

1. Communicate Computer Science concepts, designs, and solutions effectively and professionally
2. Develop efficient and effective algorithmic solutions to real-world problems
3. Analyze and evaluate the complexity and computability of solutions to real-world problems

Additionally, for students in the Software Development specialization: 

   4. Design, develop and test complex programs

Additionally, for students in the Data Science specialization:

   5. Mine, analyze, and visualize data

   6. Solve real-world problems by applying principles of computing intelligence and data analytics

Admission Requirements

1. One course in Discrete Structures
2. Two courses in Calculus
3. At least one course in Data Structures
4. Linear Algebra and Analysis of Algorithms are recommended but not required
5. Please refer to the general requirements for admission and matriculation in the Admissions   section of this catalog. 

Foundation Courses

• CISC 505 - Object-Oriented Programming and Analysis of Algorithms Credit(s): 3
• CISC 510 - Theoretical Concepts in Computer Science Credit(s): 3
• CISC 520 - Database Management Systems Credit(s): 3

Specialization I: Software Development

• CISC 515 - Software Design and Development Credit(s): 3
• CISC 545 - Distributed Application Development Credit(s): 3
• CISC 555 - Mobile Application Development Credit(s): 3
• CISC 575 - Software Assurance Credit(s): 3

Specialization II: Data Science

• CISC 530 - Mathematical Methods for Data Analysis Credit(s): 3
• CISC 540 - Computational Data Analysis Credit(s): 3
• CISC 550 - Machine Learning Credit(s): 3
• CISC 560 - Big Data Credit(s): 3

Electives

(Choose any two of the following)

• CISC 565 - Social Media and Large-Scale Data Analytics Credit(s): 3
• CISC 570 - Advanced Operating Systems Credit(s): 3
• Any other graduate CISC major course
• Any graduate IASP (Cybersecurity) course

Capstone

• CISC 600 - Capstone Course in Computer Science- Thesis (cc) Credit(s): 3

• Or

• CISC 601 - Capstone Course in Computer Science- Software Development Project (cc) Credit(s): 3 (Software Development Specialization Students)

• Or

• CISC 602 - Capstone Course in Computer Science- Data Science Project (cc) Credit(s): 3

• (Data Science Specialization Students)

Total Credits: 30