Course Search Results

  • 4.00 Credits

    This course introduces the fundamentals of the hardware environment that serves as the basis for the functional components of a digital computer system. Digital logic, machine-level representation of data, instruction sets and addressing modes, I/O devices and their interconnections, processor organization, and memory architectures are among the essential topics of the course. Students further examine assembly-level machine organizations to create assembly language programs, implement I-O operations and interrupts, and describe how the instructions of a high-level language maps to assembly/machine instructions. Prerequisite:    MATH 163 (Prerequisite or Corequisite) and CSCI (Grade of C or Better) Corequisite:    MATH 163 (Prerequisite or Corequisite)
  • 3.00 Credits

    This course prepares students to enter the mobile computing field. Students begin to prepare for these careers in a variety of entry-level positions such as mobile app developer, software developer, programmer, and mobile game developer. The course builds on a solid foundation of programming skills and design skills and introduces the specific skills needed for developing Android mobile/wireless applications. Students gain an understanding of mobile/wireless technologies and how these technologies are utilized and integrated to meet specific business needs. Current technologies and architectures that provide the network and communications infrastrucure for mobile enabled systems are also covered. Students will learn to design mobile user interfaces and apply standards to create intuitive, usable and efficient applications. Prerequisite:    CSCI 111 with a grade of C or higher
  • 4.00 Credits

    Statistics for Computing and Data Science is a study of fundamental probability and statistical methods as they apply to the fields of computer science, data science, and precursory knowledge for further study in statistical computing. Major topics include descriptive and inferential statistics, basic probability theory, discrete and continuous distributions, and an introduction to estimation and regression. Students use a statistical programming language to apply course concepts, conduct experiments, and perform simulations. Prerequisite:    MATH 161 and (CSCI 111 or CSCI 118) (Grade of C or Better for Each Course)
  • 4.00 Credits

    In this introduction to the mathematical foundations of machine learning, statistical models and algorithms for supervised and unsupervised learning will be implemented to perform classification, clustering, and rule learning. This course uses the Python and R programming languages. Prerequisite:    CSCI 118 (Grade of C or Better) or CSCI 218 (Grade of C or Better)
  • 3.00 Credits

    This is the introductory course for the Corporate Social Responsibility Proficiency Certificate. This course provides an overview of a business's obligation, known as the Triple Bottom Line to create fair stakeholder relationships and to use environmentally sustainable practices while achieving financial success. Students will apply critical and systems thinking to evaluating corporate social responsibility policies.
  • 3.00 Credits

    This course provides a comprehensive overview of how and why businesses establish good relationships with all of their stakeholders in order to maintain financial success over time. Stakeholders include employees, owners, suppliers, customers, and the communities where businesses are located. Prerequisite:    CSR 110 (Prequisite or Corequisite) Corequisite:    CSR 110 (Prequisite or Corequisite)
  • 3.00 Credits

    Students will examine a wide range of tools and methods that businesses can use in achieving environmental sustainability goals related to energy use, material mangement, water, and food systems. Students will also review financial considerations in a business's decision-making process regarding these sustainable technology options. Prerequisite:    CSR 110 (Prequisite or Corequisite) Corequisite:    CSR 110 (Prequisite or Corequisite)