This course presents some fundamental data structures, including lists, stacks, queues, hashes, and binary trees. Students will master a number of algorithms including Mergesort and Quicksort.
DurationThis course presents some fundamental data structures, including lists, stacks, queues, hashes, and binary trees. Students will master a number of algorithms including Mergesort and Quicksort.
We will study techniques for analyzing the running time and space requirements of these algorithms, and of other algorithms based on the data structures. The goals for the course are for the students to analyze running times of algorithms, and to design algorithms using appropriate data structures to solve non-trivial computing problems.
Located where the Great Plains meet the Rocky Mountains, and now with the James C. Kennedy Mountain Campus adjacent to the Roosevelt National Forest, we embody the spirit of exploration and discovery that defines our region.
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In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments.
This course is designed to give experienced web developers an advanced understanding of algorithmic thinking, the varieties of common data structures and their applications in databases and modern blockchain applications.
This course covers data structures, recursion, analysis, sorting and searching (sequential and binary), tree and tree algorithms, graphs and graph algorithms, as related to organizational problem solving across industries.
This course covers and relates fundamental components of programs. Students use various data structures to solve computational problems, and implement data structures using a high-level programming language.
Survey of advanced algorithms and data structures such as heaps and heapsort, quicksort, red-black trees, B-trees, hash tables, graph algorithms, divide and conquer algorithms, dynamic programming, and greedy algorithms. Methods for proving correctness and asymptotic analysis.
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