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Computer Science

Bachelor of Science Degree

The Bachelor of Science in Computer Science degree will help you develop a strong foundation in computing principles including algorithms, data structures, programming languages, cybersecurity, and databases. These skills can be applied to develop solutions to a wide variety of computational challenges. Courses will focus on computer theory, computing problems and solutions, and the design of computer systems from a scientific perspective.

This online degree program also provides an opportunity to study computer science more broadly by choosing elective courses that will deepen your computing skills in targeted areas such as programming, data analysis and visualization, artificial intelligence, cloud computing, and DevOps skills - all areas of expertise that are in high demand.

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Courses in the major include:

This course provides an introduction to problem solving and computer programming using the language Python. Students will analyze problems, design and implement solutions and assess the results. Topics include fundamental programming constructs such as variables, expressions, functions, control structures and lists. Emphasis is placed on numerical and data analysis for informed decision making. Prerequisite: None
The major focus of this course will be the relational, dimensional and NoSQL models. Topics include relational and dimensional modeling, business intelligence, NoSQL databases and their application, SQL, application development using databases and emerging trends. Students will prepare a small application using a commercial database management system.
This course provides an introduction to computer systems and provides the foundations to Computer Science. Topics include operating systems, parallel and distributed systems, communications networks, and computer architecture. Emphasis is placed on concepts and relationships between subdisciplines of computer systems.
This course explores the concepts underlying modern programming languages, including syntax, functions, expressions, types, polymorphism, assignment, procedures, pointers, encapsulation, classes, and inheritance. Programming paradigms, such as sequential, concurrent, object-oriented, functional, and logic programming.
This course introduces algorithms by looking at the real-world problems that motivate them. Students will use a range of design and analysis techniques for problems that arise in computing applications. The algorithm design process is emphasized as well as the role of algorithms in the broader field of computer science. The course incorporates ethics and privacy.
This course is a hands-on introduction to the design of abstract data types. Topics will include how to select and implement data structures for various problems or accomplish tasks. Fundamental data types used in computing such as lists, stacks, queues, priority queues, sets, maps, and binary trees are explored. Python language will be used for coding data structures.
Topics include software planning, specifications, coding, testing and maintenance. Coverage of modern software engineering techniques that ensure development of well-designed, reliable, flexible, modular, and verified software and software systems. Topics include software product development, cloud-based software, microservices architecture, code management and review, agile development, and DevOps.
This course introduces fundamental technologies used in information systems. Students will develop a foundation in cybersecurity by learning the fundamental concepts behind cybersecurity. In addition, students will learn cybersecurity principles used in the design of systems to protect information and assets against persistent and constantly evolving threats.
This course provides the theoretical basis and problem-solving experience needed to apply the techniques of descriptive and inferential statistics, to analyze quantitative data, and to improve decision making over a wide range of areas. Topics covered include descriptive statistics, linear regression, data gathering methodologies and probability, as well as confidence intervals and hypothesis testing for one and two samples. Use of technology in solving and interpreting statistical problems is emphasized. Prerequisite: MA 101 or placement via ALEKS Placement Assessment

Beyond the Core courses, choose three elective courses below (9 credits total). Other electives may be available upon request.

  • CS 440 - Artificial Intelligence
  • CS 490 - Computer Science Project
  • CS 310 - Programming with C++
  • CIS 313 - Cryptography
  • CIS 312 - Securing Access Control
  • DSC 320 - Math for Data Science
  • DSC 300 - Data Science Analysis and Methodologies
  • DSC 310 - Data Analysis and Visualization
  • DSC 350 - Data Wrangling for Data Science

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