CSC-2740 - Data Structures and Algorithms* (4)

Prerequisite: CSC-2592 ; MTH-2500
This course provides comprehensive introduction to analysis and design of computer algorithms. Students are trained to analyze and evaluate the asymptotic performance (worst, average and best case) of various algorithms. Students understand that the average-case running time of algorithms is probabilistic and are able to employ the linearity of expectations to analyze them. Students are able to explain correctness of algorithms using inductive proofs and loop invariants. The course also explores various divide-and-conquer algorithms and solutions to recurrences. Understand the greedy paradigm and explain the appropriate use of it in algorithm design. Graph algorithms and elementary data structures for implementing them are also explored. Upon successful completion of this course, students are able to synthesize efficient algorithms for various system designs. ITS-2740 is now CSC-2740.

List Course Outcomes (consistent for all sections)
CO1 - Synthesize efficient algorithms for various system designs.

CO2 - Discuss data abstraction and abstract data types in detail.

CO3 - Explain data abstraction as a problem-solving technique.

CO4 - Utilize basic abstract data types such as the stack, queue, binary tree, binary search tree, table, heap, and priority queues in applications.

CO5 - Analyze the efficiency of various searching and sorting algorithms utilizing order-of-magnitude and Big O notation.

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