A. Computer Science Core
CS101 Computer Science Fundamentals (3 cr.)
This course provides a foundation in computing and algorithmic principles. Students are introduced to the basic conceptual building blocks of computer hardware and software systems. The tools and principles of algorithmic problem solving and systems design are explored. In the second half of the semester, students gain experience with simple programming challenges.
CS110 Computer Science I: Introduction to Programming (3 cr.)
This course introduces students to the practice of software development. Students learn the fundamentals of programming, algorithm development, and basic design principles.
CS210 Computer Science II: Advanced Programming and Data Structures (3 cr.)
This course is a continuation of CS110. CS210 expands on the programming techniques covered in CS110, adding discussion of recursion and data structures such as lists, stacks, queues, balanced trees, graphs and heaps. Specific algorithms that use these structures efficiently and general algorithm techniques and their analysis are also covered.
CS220 Discrete Mathematics (3 cr.)
This course provides the theoretical foundation of modern computer hardware and software. It provides that foundation in the form of mathematical tools and concepts geared toward computer science applications. Topics covered include: logic and set theory; functions and relations; simple algorithm analysis; and an introduction to graph theory.
CS255 Database Design (3 cr.)
A study of fundamental database management systems. Course topics include: data modeling, database design and structured query language (SQL), transaction management, data integrity and security. Object-relational mapping techniques and technologies will also be covered.
CS300 Networking (3 cr.)
This course examines computer networks and data communication. Topics include: telecommunications history; transmission media; transmission characteristics; error detection and correction; local and wide area networking applications; standard network models; industry standards; protocols; network management; wireless and mobile networks; network security.
CS307 Introduction to Cybersecurity (3 cr.)
This course provides an overview of modern security concepts. Topics covered will include security terminology, risk management, security policy and strategy, security awareness, cryptography, operating system security, network security, physical security and digital forensics. The course will contain a lab component where students will investigate current hardware and software tools for vulnerability analysis and penetration testing.
B. All of the following:
BU243 Business Computer Applications (3 cr.)
This course provides in-depth coverage of Microsoft Excel and Access in the context of business applications. Excel topics include formulas and functions, charting, large datasets, pivot tables and what-if analysis. Access topics include relational database concepts, database design, basic query construction, and report generation. This course combines on-line and hands-on learning.
BU351 Information Systems for Business Intelligence (3 cr.)
This course focuses on the fundamentals of information systems and their foundational technologies as they can be used for business analysis and intelligence. Areas studied will include hardware, operating systems, database systems, knowledge management, decision support systems, and networked computing concepts. Data oriented techniques for business intelligence and decision making are introduced. Structured Query Language (SQL) is studied.
BU352 Data Analysis and Business Modeling (3 cr.)
This course is designed to introduce the concept of data science in business. The application of data science helps businesses make informed decisions by using sound judgment and data. This is a skill development class that explores how statistics, optimization models, and key performance indicators are used in business. Students in this course will leave with a specialized skillset used in a variety of roles within an organization. Storytelling skills are taught and a final project is required.
BU354 Data Mining for Decision Making (3 cr.)
This course provides both the theoretical and practical knowledge of data mining topics. Particular topics include cluster analytics, text-mining regression, and random forest models. Neural networks and artificial intelligence are explored. Students will have the opportunity to work with a number of exercises to practice and understand the uses of data mining in business organizations. Students will complete a machine learning project as part of the course requirements. Students will present their work at the Celebration of Scholarship.
BU420 Business Analytics (3 cr.)
This course will examine methods that have been studied in previous Business Intelligence major courses and those from the business core proven to be of value in recognizing patterns and making predictions from an applications perspective. Course learning will involve utilizing a variety of software to aid in the review of analytical cases to improve understanding of enterprise level analytics. Students will build a data warehouse, using data profiling and quality skills, and lifecycle models introduced in the course.
BU469 Business Capstone Project (3 cr.)
The topics and projects for this course vary according to the needs and interests of business majors.
C. Six Credits of Computer Science electives or Internship