Computing Science and Mathematics
Bachelor of Science (BSc)
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About Computing Science and Mathematics
What is Computing Science and Mathematics?
This program combines two areas of study for ultimate impact. Computing science includes big data, programming languages, computer construction, computational theory, software design and implementation, patterns and processes, operating systems, and human interaction with technology. Mathematics provides the critical foundation for computing science and is the principal language for scientists.
Why Choose This Program?
If you enjoy solving problems, building puzzles, making technology useful and applying knowledge to diverse fields, this program is for you.
Computing science and mathematics prepares you for the integral and rapidly growing fields in programming, artificial intelligence, mathematics and computational intelligence. The hands-on skills you will learn in this program are enhanced by a thorough exposure to the underlying principles, theories, and ethics that carry on in spite of changes within the tech realm.
Major Map
View what studying in this program could look like each year, from courses to experiential learning to career development.
Program Information
Degree
Bachelor of Science
Major
Computing Science and Mathematics
Streams
- Computing Science
- Computational and Applied Mathematics
Minors (Optional)
- Management
- Music
Students in this program are eligible for 150+ Augustana awards (totalling over $495,000)
Low 70s program admission-average range
Develop mobile apps and have the opportunity to create a real-world application for a client
Program Objectives
In this program, you will:
- Embrace computing and mathematical thinking to decompose problems, recognize patterns and design abstract frameworks, analyze and design algorithms, and model real-world challenges as computational problems.
- Create robust and efficient software that follows standard design principles to meet specified needs.
- Contextualize working knowledge and theory of computing science and mathematics, applying these capabilities within society at large while recognizing the ethical and professional practices.
Learning Outcomes
You will leave this program with the ability to:
- Provide professional leadership in interdisciplinary settings requiring complex computational and mathematical reasoning.
- Adapt as the technologies change, having a strong foundation in algorithmic thinking, data analysis, and problem-solving.
- Communicate clearly and effectively about technologies through verbal and written materials for a variety of audiences, using appropriate sources and technologies.
Careers
An undergraduate degree in computing science and mathematics is great for entry into the workforce or graduate and professional programs. Potential career options include:
Course Highlights
Introduction to modern web architectures and technologies. Web platforms and standards. Client-side/server-side programming and web languages (e.g. HTML, JavaScript, PHP, CSS, Node.js). Introduction to internet security. Design and implementation of a simple web application.
Software engineering paradigms, requirements specification, iterative software development, object-oriented design patterns, visual modelling with UML, software architecture; testing, verification and maintenance; software development environments and software engineering tools; societal implications such as the cost of failure and professional responsibilities.
Introduction to cryptography in theory and practice, including its applications and mathematical foundations. Topics include classical cryptosystems, private-key cryptosystems (including DES and AES), hashing and public-key cryptosystems (including RSA), digital signatures, selected topics in cryptography.
Fundamental concepts of discrete and continuous dynamical systems, both linear and nonlinear; nonlinear differential equations; deterministic, nondeterministic, and chaotic dynamics; strange attractors and fractals. Applications in ecology, biology and physics.
Survey of concepts and applications of artificial intelligence, including knowledge representation, state-space search, heuristic search, expert systems and shells, natural language processing, propositional logic, learning and cognitive models, vision; implementation using an AI language (LISP or PROLOG).
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