Discrete Structure

The IT2017 task group recommends that a robust information technology program should have at least discrete structures (mathematics) and a variety of other mathematical experiences to prepare a competent IT professional for the mid-2020s. Institutions offering programs in information technology must ensure that students entering the program have the necessary mathematical prerequisites to engage in university-level mathematics courses.

Prerequisites vary by region; however, they should include pre-calculus, usually taught in secondary schools or in preparatory programs.

As with the IT domains, we partition the mathematics curriculum of an IT degree program into essential and supplemental domains. Table 6.3 depicts a single essential domain with its accompanying sub-domains.

Table 6.3: Related IT essential mathematics

IT Essential Mathematics and Levels of Student Engagement

The supplemental domains of the mathematics curriculum of an IT degree program consist of selected subjects from college-level mathematics appropriate for the IT discipline. These include but not limited to the following.

Programs should seek to include as much appropriate mathematics as possible to reflect the goals and the needs of their constituents, so their graduates can achieve success in the workplace or in graduate studies.

 The IT2017 task group recommends that IT students must achieve the IT essential mathematics domain competencies in addition to the supplemental mathematics. The IT2017 task group also recommends that the mathematics should be at least 10% of a baccalaureate IT degree program to prepare a competent and competitive IT graduate.

Another reference : https://ccecc.acm.org/files/publications/CSTransfer2017.pdf  pp.17

Furthermore, computer science programs must provide students with a level of “mathematical maturity.” Lower division, undergraduate computer science programs need enough

mathematical maturity to have the basis on which to build CS-specific mathematics. The concepts established in a course on Discrete Structures are foundational material for computer science, and for that reason such coursework must be completed early in the program of study.

The transfer guidelines include an entire Knowledge Area on Discrete Structures estimated at 40 professor/student contact hours to cover the 34 recommended learning outcomes. The typical pre-requisite for a discrete structures course is pre-calculus; and a typical discrete

structures course includes requisite concepts in set theory, induction, recursion, logic, graph theory, and combinatorics, and uses the notion of formal mathematical proof as a unifying theme. These concepts are critical to the study of algorithms and data structures. The recommended Discrete Structures course can be taught very successfully by computer science faculty with appropriate qualifications; in this manner, the content can be presented from the computing perspective, with examples and assessment activities tailored to that perspective as

well. Concepts in discrete structures also serve as underpinnings for advanced computer science topics. For example, an ability to create and understand a formal proof is essential in

formal specification, in verification, and in cryptography; professionals use graph theory concepts in networks, operating systems, and compilers and set theory concepts in software engineering and in databases. The theoretical concepts of calculus not only help provide mathematical maturity, but also are required for studying the efficiency of algorithms, typically measured by Big-O notation. An introductory calculus course that includes the foundational concepts of limits, functions, and upper and lower bounds necessary for understanding asymptotic analysis will serve a computer science major well. Mathematics faculty typically teach this course, intended for engineering,science or mathematics majors. For computer science majors, the ability to think abstractly and

to generate software solutions of mathematical models for real-world scenarios is enhanced through the study of calculus. Because introductory calculus is a well-established, time-honored course, the transfer guidelines do not include learning outcomes for the application of calculus to computer science.

Other types of mathematics courses may also be appropriate for undergraduate computer science majors. With the preponderance of algorithms for big data analytics, data visualization, and machine learning, lower division, undergraduate courses in linear algebra as well as probability and statistics will serve a computer science major well for emerging and unforeseen careers. This curricular guidance does not include student learning outcomes for these mathematics courses

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