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Course Criteria
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4.00 Credits
This course continues using abstract data types and the concepts presented in CSci 2001 and introduces stacks, queues, linked lists, and trees. This course also covers advanced programming topics of recursion, sorting methods, and complexity measures. This is an object-oriented programming course.
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4.00 Credits
The course covers mathematical topics essential for work in computer science. Topics include: number bases, mathematical induction, sets, relations, functions, congruence, recursion, combinations and permutations, probability, graphs, trees, logic, Boolean algebra, and proof techniques. Computing related problems and examples are integrated throughout the course. Prerequisites: MATH 1150 College Algebra (Minimum grade: 1.67 GPA Equivalent) Or A score of 79 or higher on the College Level Math (0167) placement test Or An ACT math score of 26 or higher. Recommended: Any CSCI course numbered 1030 or above (Minimum grade: 1.67 GPA Equivalent)
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1.00 Credits
The course will introduce the Python Programming language in terms familiar to students experienced with writing simple, yet complete, programs in other languages. Additionally, the course will focus on utilities and features considered strengths in Python. This includes interfaces to specialized libraries and databases. Prerequisites: CSCI 1120 or CSCI 1130 or CSCI 1150 or CSCI 2001 or CSCI 2400
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4.00 Credits
As an introduction to computer organization and structure, this course includes beginning machine and assembly language programming. Topics to be covered include logic gates and Boolean algebra, basic elements of computing devices, basic components of a computer, data representation and number systems, micro operations, microprogramming, and input-output programming.
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4.00 Credits
This course covers relational databases from conceptual design to implementation. The course will include logical and physical design, normalization, as well as the definition of tables and keys. The use of Structured Query Language (SQL) for data retrieval and manipulation will be emphasized.
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3.00 Credits
The main objective of this course is to provide practical training and real work experience for the students. Often, it will include productive work contribution, and prospective employee evaluation for the employer. It can lead to increased college-industry interaction for the department and the college. Completion of this class will better prepare the student for multiple activities in a workplace. It should reflect positively on the students resume (employers view internship experiences positively.) Internship is an excellent opportunity for a student to affirm career interests. These opportunities can also provide the credentials needed for full-time positions. Internships and co-ops provide opportunities to network with professionals; strengthen confidence, maturity, and professionalism; establish professional references. Prerequisite: Enrollment in the computer science program, completion or concurrent enrollment in CSci 2002, a "B" average in all CSci courses
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4.00 Credits
ASP.NET is a technology for creating web-based programs and services. This course introduces ASP.NET on the foundation of the prerequisite courses that taught the fundamentals of .NET framework, C# programming language, SQL Server database, and the primary development environment Microsoft Visual Studio. The main goal of this course is to teach the basics of creating and deploying Web applications utilizing ASP.NET technology. Besides using the C# programming language, the students will learn the commonly used ASP.NET controls included in Microsoft Visual Studio. The course includes the techniques of reading the data from a SQL Server database into a Web application and displaying it on a web page, as well as modifying and amending the database content. Prerequisites: CSci 1150 and CSci 1040
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4.00 Credits
This course introduces students to the rapidly growing field of Data Science. Students will learn the concepts and tools used to analyze data sets and make informed business and/or research decisions. Students will use various software, including databases, to gather, organize and visualize data for analysis.
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4.00 Credits
Provide students further exposure to the growing field of Data Science. Building upon the topics in Data Science I, students will learn about machine learning techniques, ways to deal with networked systems and extremely large data sets, and methods for improving the performance of computerized statistical models.
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2.00 Credits
This course provides a flexible in-depth review of interdisciplinary applications in Data Science. The curriculum has students independently explore and apply methods of Data Science in a real-world context related to their career interests.
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