Masters in Computer Science

The computer science field has many positions that are poised for significant job growth and offer high earning potential. In fact, Business Insider’s ranking of jobs found that 13 of the 15 hottest tech jobs paying over $100,000 are in computer science.1

Masters in Computer Science: What Will I Learn?

With a masters in Computer Science you will learn advanced computing, while honing your problem-solving, analytical and creative abilities. This degree will allow you to gain the knowledge and skills needed to shape tomorrow’s technology advancements.

Computer science programs focus on topic areas that allow students to gain knowledge and experience in developing enterprise-scale applications, database systems, security solutions and intelligent automated systems. Many programs also offer specializations. This allows students to target their coursework to a certain focus area within the field.

Most masters in Computer Science programs culminate in a capstone thesis or project. This allows a student to pull upon the knowledge that they gained throughout the duration of the project. In a capstone, students are often able to partner with an external organization to combine theory and practice. It can be very helpful in demonstrating to employers their computer science abilities.

Click here to learn more about an online master’s in Computer Science and widen your career opportunities in this growing field.

1Bort, J. (2015, May 8). The 15 hottest tech jobs that pay over $100,000. Business Insider. Retrieved from

Featured Degree


When you earn your online MS in Computer Science from Lewis University, you receive a practical education that is customizable, market-relevant and immediately applicable to your career. In this fully online computer science degree, you will learn to:

  • Apply, design and develop principles for constructing software systems to meet desired needs
  • Design, implement and evaluate a network-based distributed system, process, component or program
  • Employ theoretical frameworks for analyzing computational problems