CS 1112

Introduction to Computing: An Engineering and Science Perspective

Course Description

Course information provided by the Courses of Study 2024-2025.

Programming and problem solving using Python. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays, strings, recursion, object-oriented programming, algorithms, and data handling and visualization. Assignments are designed to build an appreciation for complexity, dimension, randomness, simulation, and the role of approximation in engineering and science. Weekly discussion section provides guided practice on the computer, with staff present to help. NO programming experience is necessary; some knowledge of Calculus is required.

When Offered Fall, Spring.

Prerequisites/Corequisites Prerequisite: MATH 1110, MATH 1910, or equivalent.
Forbidden Overlaps Forbidden Overlap: due to an overlap in content, students will not receive credit for both CS 1110 and CS 1112. Students may not enroll in CS 1110 if they have taken or are also enrolled in CS 2110/ENGRD 2110, CS 2112, or have taken or are currently enrolled in a course offered or cross-listed with a CS number 3000 or above. (Students looking to learn Python rather than learn how to program should take CS 1133 instead.) Students who have affiliated with the computer-science major may not enroll.

Distribution Category (SMR-AS)

Comments Co-enrolling in MATH 1110 or MATH 1910 is acceptable assuming that student is comfortable with the concepts of series summation and derivatives. No prior programming experience required.

• Be fluent in the use of procedural statements-assignments, conditional statements, loops, function calls-and arrays. Be able to design, code, and test small Python programs that meet requirements expressed in English. This includes a basic understanding of top-down design.
• Understand the concepts of object-oriented programming as used in Python: classes, subclasses, inheritance, and overriding.
• Have knowledge of basic sorting and searching algorithms.
• Have a working familiarity with libraries such as NumPy and Matplotlib for scientific computation and data visualization.

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