This course covers basics of algorithm design and analysis, as well as algorithms for sorting arrays, data structures such as priority queues, hash functions, and applications such as Bloom filters.
Algorithms for Searching, Sorting, and Indexing
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Algorithms for Searching, Sorting, and Indexing
This course is part of Foundations of Data Structures and Algorithms Specialization
Instructor: Sriram Sankaranarayanan
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4.7
(385 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 35 hours
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What you'll learn
Explain fundamental concepts for algorithmic searching and sorting
Describe heap data structures and analyze heap components, such as arrays and priority queues
Design basic algorithms to implement sorting, selection, and hash functions in heap data structures
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Assessments
15 quizzes
Taught in English
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This course is part of the Foundations of Data Structures and Algorithms Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this module the student will learn the very basics of algorithms through three examples: insertion sort (sort an array in ascending/descending order); binary search: search whether an element is present in a sorted array and if yes, find its index; and merge sort (a faster method for sorting an array). Through these algorithms the student will be introduced to the analysis of algorithms -- i.e, proving that the algorithm is correct for the task it has been designed for and establishing a bound on how the time taken to execute the algorithm grows as a function of input. The student is also exposed to the notion of a faster algorithm and asymptotic complexity through the O, big-Omega and big-Theta notations.
What's included
7 videos12 readings4 quizzes1 programming assignment1 discussion prompt
In this module, the student will learn about the basics of data structures that organize data to make certain types of operations faster. The module starts with a broad introduction to data structures and talks about some simple data structures such as first-in first out queues and last-in first out stack. Next, we introduce the heap data structure and the basic properties of heaps. This is followed by algorithms for insertion, deletion and finding the minimum element of a heap along with their time complexities. Finally, we will study the priority queue data structure and showcase some applications.
What's included
5 videos6 readings5 quizzes1 programming assignment
We will go through the quicksort and quickselect algorithms for sorting and selecting the kth smallest element in an array efficiently. This will also be an introduction to the role of randomization in algorithm design. Next, we will study hashtables: a highly useful data structure that allows for efficient search and retrieval from large amounts of data. We will learn about the basic principles of hash-table and operations on hashtables.
What's included
7 videos6 readings5 quizzes1 programming assignment
In this module, we will learn randomized pivot selection for quicksort and quickselect. We will learn how to analyze the complexity of the randomized quicksort/quickselect algorithms. We will learn open address hashing: a technique that simplifies hashtable design. Next we will study the design of hash functions and their analysis. Finally, we present and analyze Bloom filters that are used in various applications such as querying streaming data and counting.
What's included
5 videos6 readings1 quiz1 programming assignment
Instructor
Instructor ratings
4.7 (136 ratings)Offered by
Recommended if you're interested in Algorithms
University of Colorado Boulder
Stanford University
University of Colorado Boulder
Stanford University
Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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