MASTER OF SCIENCE IN COMPUTER SCIENCE

" Moving humanity forward, one line of code at a time." - Donna Dulo

The Master of Science in Computer Science at Sofia University provides students with a broad background in software development and other core disciplines of computer science. It also gives them the opportunity to further their knowledge in foundational and applied topics. Learn technological skills with humanistic values. Classes available onsite and online.

Located in Palo Alto, the heart of the Silicon Valley just minutes away from top companies such as Google, Apple, HP and Facebook, Sofia University provides students with an environment rich in opportunities.

The program features:

  • Onsite and hybrid/online formats
  • Combine technological development with transpersonal values and skills for the practicing professional
  • Promote creativity, cultural sensitivity and mindfulness
  • Bring essential skills and values to the next generation of well-balanced leaders in the workplace
  • Small classes and close interaction with faculty
  • Faculty that are experts in their field, and bring the cutting edge technology and real-world computing experience to the classroom

Concentrations Offered:

  • Artificial Intelligence and Machine Learning
  • Data Science
  • Robotics Computing
  • Unmanned Aircraft (Drone) Computing
  • Quantum Computing
  • Virtual/Augmented Reality and Gaming Computing
  • Cyber Security and Information Assurance
  • Human Computer Interaction

The Sofia University Difference - Student Testimonials

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Program Design (Starting Summer quarter 2017)
The MSCS Program is designed to provide a greater variety of classes within a shorter term. The quarter system allows you to finish each class in only 10 weeks compared to a semester system which takes 16 weeks. Here’s the breakdown!

  • Complete the program in 1.5 years (18 months)
  • Total 12 subjects (each subject is 4 quarter credits which equal to 3 trimester credits)

Master of Science in Computer Science Curriculum

The Master of Science in Computer Science at Sofia University provides students with a broad background in software development and other core disciplines of computer science. It also gives them the opportunity to further their knowledge in foundational and applied topics. Located in Palo Alto, the heart of the Silicon Valley just minutes away from top companies such as Google, Apple, HP, and Facebook provides the students with an environment rich in opportunities.

Download the MSCS Catalog.MSCS Catalog 2018-2019

Program Requirements

  • 12 subjects – Computer Science courses: 5 core, 7 electives
    • 3 – Interdisciplinary courses (These are elective classes in Business, Psychology, Liberal Arts or any other subject outside of Computer Science; These are to provide a broader background upon graduation and the type of soft skills employers are seeking.)
    • 1 − Internship/Practicum

    Note: The internship requirement can be substituted with elective credit with Program Chair approval. Internship units taken beyond 3 units will NOT count towards degree requirements.

Course Transfer Information

Do you have courses you want to transfer in? Nine (9) units can be transferred in from other Master's programs (based on approval).

Tuition: Estimated total cost of tuition: $21,600.00

Contact: admissions@sofia.edu or call 650-493-4430

 

About the Concentrations

Artificial Intelligence & Machine Learning

Artificial intelligence (AI) is the intelligence exhibited by machines or software. Artificial intelligence covers the many aspects of how human intelligence is encoded in computer programs and mechanisms such as robots. This course introduces the foundation of simulating or creating intelligence from a computational point of view. It allows the students to gain generic problem-solving skills that have applicability to a wide range of real-world complex problems. It covers the techniques of reduction, reasoning, problem-solving, search, knowledge representation, and machine learning. It also covers computational complexity and issues arising at the junction between biological and artificial intelligence.

Machine learning is a complex yet a fast-moving field with many real-world commercial applications. The goal of machine learning is to build computer models that can produce useful information whether they are predictions, associations, or classifications. The ultimate goal for many machine-learning researchers is to build computing systems that can automatically adapt and learn from their experience. This course will study the theory and practical algorithms, basic concepts and paradigms, key techniques, challenges, and tricks of machine learning. It also covers examples of how machine learning is used/applied today in the real-world and exposes students to the construction and use of machine learning algorithms. This course discusses recent applications of machine learning, such as robotic control, speech recognition, face recognition, data mining, autonomous navigation, bioinformatics, and text and web data processing. It also fuses machine learning with other areas of artificial intelligence and robotics.

Data Science

The basic techniques of data science, algorithms for data mining, and basic statistical modeling are core competencies that will be studied in depth. Data science leverages all available and relevant data to effectively provide a predictive model that can be applied to real-world business, engineering, and scientific problems. A major goal of data science is to make it easier for others to find and coalesce data with greater ease. Data science technologies affect how data scientists access data and conduct research across various domains, including the biological sciences, medical informatics, social sciences, and the humanities.

Robotics Computing

The integration of software and hardware systems will be emphasized through proper computational paradigms such as algorithms, automata, search structures, and data manipulation in real-time reactive systems. Coverage of electronics and electronic interfaces will provide a solid foundation on which to base artificial intelligence structures. The use of sensors and motors, as controlled by software will be covered in addition to the use of embedded and mechanical software-driven systems. A special emphasis shall be placed on robot autonomy and learning through the precise use of computer algorithms and data structures. Robot sensing, analyzing, vision, and locomotion through computational structures will also be covered.

Unmanned Aircraft (Drone) Computing

This course introduces aeronautical foundations of unmanned aircraft structure and design. It focuses on the primary airframes of unmanned systems: fixed wing, rotorcraft, tiltrotor, and lighter than air along with various hybrid technologies. The course also introduces avionics, propulsion, and payload systems and their interactions and control through computer busses and architecture. A central focus of the course is the interaction of computer structures with the aircraft to promote safety while managing the foundational stability and control properties of the aircraft: lift, thrust, drag, and weight. A survey of aeronautical principles is presented along with aerodynamics and aviation science. Technologies such as launch and recovery systems, GPS, communications, ground stations, data-link technologies, and wireless technologies are also presented. The course concludes with the development of a comprehensive proposal applying unmanned aircraft technology to solve a challenging technological problem in a selected industry. It is vital for computer scientists to understand aerodynamics and aircraft structures in order to safely and reliably program unmanned aircraft of all sizes to function in the national airspace. This course will help computer scientists understand how a drone works so that they can safely develop programs, algorithms, and security for them.

Quantum Computing

This course is the first of a two-part series introducing the cutting-edge technology of quantum computing. The course, in both parts, gently covers the physics and mathematics required for the study of the complex computational aspects of quantum computing. Quantum mechanics is discussed in detail, particularly superposition and entanglement, and how quantum mechanics serves as a foundation for quantum computation using qubits. A quantum theoretical version of the Turing machine is explored as well as the actual quantum computers that are being built by leading members of the computing industry. Basic and advanced quantum structures are covered including quantum gates, and various quantum computer models are presented including Topological, Adiabatic, and One-way quantum computers. Additionally, various realizations of physical implications of quantum computers are discussed.

Virtual/Augmented Reality and Game Computing

This course will provide a gentle introduction to the critical physics and mathematics necessary for game programming. Physics studied include Newtonian mechanics, kinematics, projectile physics, the physics of solids, aerodynamics, hydrodynamics, and the physics of explosions and lasers. Mathematics studies include analytic geometry, vectors, matrices, probability, Monte Carlo simulation, spherical geometry, trigonometry, and basic algebra and calculus. The course will also cover light physics and graphics dynamics, gravity, gravitational forces, and energy. Basic game scenarios will be developed and calculated to plan for optimal game reality.

Cyber Security and Information Assurance

This course covers vital information assurance and computer security principles as applied to computer systems and organizational information systems. Information assurance principles such as availability, integrity, and confidentiality are applied strategically to ensure the integrity of data and information. The complex concepts of data privacy, data security, and the relationship of security to organizational computer systems are integral to this course. Many facets of computer security such as integrated circuit security, physical security, personnel security, systems security, and operations security are discussed and related directly to information assurance principles. The concepts of risks, threats, and vulnerabilities as applied to computational systems are covered as well as the mitigation them through various forms of software and computer technologies in a defense in depth structure. The course also includes a survey of various laws and government initiatives to implement information assurance in the organization in a lawful manner. The course concludes with the development of a NIST compliant comprehensive information assurance plan for the complete organization: PCs, networks, databases, and supporting communications infrastructure.

Sofia University is proud to lead the industry with this groundbreaking forward-looking concentration. The program covers the critical areas of unmanned systems computer science and technology that are central to safe and reliable unmanned aircraft systems operations. It covers unmanned aircraft computer and software technologies, vital algorithms that support unmanned aircraft operations and system autonomy, as well as critical software architectures that support anti-collision and auto-land technologies that are central to safe unmanned aircraft operations.

Click to download the Drone powerpoint to learn more about the concentration.