Algorithms and data structures are the essential frameworks for solving almost any computer engineering problem.
Algorithms and data structures are the essential frameworks for solving almost any computer engineering problem. You need a fundamental grasp of these tools to understand how computers and programming languages work, their relative strengths and weaknesses and what makes a specific solution the best choice.
In this course, you’ll examine, create, compare and test the major types of algorithms and data structures. You’ll learn how to conduct a Big-O analysis to assess the complexity and runtime of your code, and how to understand the tradeoffs of using different algorithms and data structures. You’ll learn to identify different problem-solving approaches and how to forge solutions for optimal performance and efficiency.
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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.
Models, algorithms, recurrences, summations, growth rates. Probabilistic tools, upper and lower bounds; worst-case and average-case analysis, amortized analysis, dynamization. Comparison-based algorithms: search, selection, sorting, hashing. Information extraction algorithms (graphs, databases).
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.
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.
Exploration of the design and implementation of data structures and algorithms fundamental to computer systems and applications and to software engineering.
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