Curriculum for Computer Science & Innovation Degree

There is a difference between what science is, its history and meaning, and what science does through experimental and applied practices. This interdisciplinary course invites students to examine how scientific knowledge is defined, constructed, and communicated. Students will critically analyze different examples of science in society to understand how science shapes and is shaped by culture, power, and people. By the end of the course, students will be equipped to evaluate the ways in which science reflects and transforms the world around us.

How does technological change impact people across the globe? What gets lost-or found-in translation? Through interdisciplinary analysis, this course examines technological change as a global phenomenon. We will examine how innovations from one perspective can be exploitative from another, investigating how technologies evolve and affect each other across different times and places. The course emphasizes ethical inquiry and considers how the economic, political, and cultural forces driving technological change affect people's lives differently depending on context, identity, and access. Students will leave with a deeper understanding of how science and technology shape and are shaped by the world.

This course equips students with interdisciplinary tools and methods to analyze and respond to complex, real-world problems. Drawing on diverse frameworks and theories, students will explore how their own identities and experiences shape their understanding of contemporary issues. Emphasis is placed on synthesizing knowledge across fields to develop innovative, contextually relevant solutions. Students will communicate their insights effectively through a range of expressive modes tailored to varied audiences and purposes.

Students learn the history and basics of computing, as well as the fundamentals of programming. General topics include: the history of computing, number systems and classical computing, Boolean logic, algorithm design and implementation, and modern computer organization. Programming topics include: memory and variables, mathematical operations, basic input and output, decision making, repetitions, functions, and arrays.

This course builds on the content from Introduction to Programming. Students study the Object Oriented Programming (OOP) Paradigm with topics such as objects, classes, encapsulation, abstraction, modularity, inheritance, and polymorphism. Students examine and use structures such as arrays, structs, classes, and linked lists to model complex information. Pointers and dynamic memory allocation are covered, as well as principles such as overloading and overriding. Students work to solve problems by selecting implementation options from competing alternatives.

Students compare and contrast a variety of data structures. Students compare algorithms for tasks such as searching and sorting, while articulating efficiency in terms of time complexity. Students implement data structures and algorithms to support solution designs.

This course takes a shaders-first dive into modern computer graphics programming. Students are introduced to vertex and fragment shaders, 2D and 3D coordinate spaces, drawing primitives, lighting and shading, data flow and manipulation, and modern GPU capabilities. Linear algebra and 3D math concepts will be refreshed and/or introduced for applicable topics.

Students will learn the concepts, techniques, and structures of discrete mathematics necessary for the software engineer, including logic, sets, functions, relations, Boolean algebra, and combinatorics. The course also introduces the concept of mathematical proof.

Students will gain a solid understanding of network concepts, models, protocols, and applications. Emphasis is placed on the design and implementation of socket-based network programs, comprising both client and server architectures, and including advanced concepts such as non-blocking sockets, multiplexing, threads, asynchronous programming, and multicasting. Practical skills are developed through hands-on exercises and assignments using selected programming languages.

This course will introduce students to database design, Structured Query Language (SQL), normalization, and relational database theory. Traditional relational databases will be contrasted with NoSQL paradigm databases including document-oriented, key-value store, and graph. Students will gain hands-on experience writing database applications.

Students study the foundations of computer language and computer architecture. We use Assembly programming as the mechanism for gaining a better understanding of computer architecture and how hardware and low-level programming can be harnessed in terms of software development. Historical and current computer architecture concepts will be covered and used as a basis to discuss future trends.

Students will be able to write effective and efficient code by learning to identify and apply the unique capabilities of different programming paradigms and emerging languages.

Students will gain hands-on experience in the design of object-oriented software by applying design principles and design patterns. Learning the most common object-oriented software design patterns and general reusable solutions to commonly occurring problems within a given context in software design will enable students to communicate objects and classes. Each pattern is associated with an underlying object-oriented design principle, solves a specific design problem, and applies to particular a context with particular consequences of applying it; different components of the pattern collaborate with each other. Knowing how to use common design patterns will help students engineer solutions more quickly and correctly.

Artificial Intelligence (AI) is a broad area of computer science with many varied sub-disciplines. This rotating topics course will allow students to explore subjects in AI based on faculty expertise and student interest.

This course will introduce students to practical machine learning and its applications. It will cover both supervised and unsupervised clustering algorithms and their applications in business problems like market segmentation. Students will also become familiar with artificial neural networks and their use in prediction modeling. Software packages and tools related to machine learning algorithms will also be covered.