This course is part of the Data Science Foundations: Data Structures and Algorithms Specialization

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Sriram Sankaranarayanan

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Part of the Master of Science in Data Science degree
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Data Science Foundations: Data Structures and Algorithms SpecializationUniversity of Colorado Boulder

About this Course

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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 can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

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Earn a Certificate upon completion

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Includes hands on learning projects.

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Course 1 of 3 in the

Data Science Foundations: Data Structures and Algorithms Specialization

Intermediate Level

Calculus: derivatives and integrals. Probability theory: distributions, expectations, and moments. Some programming experience with Python.

Approx. 34 hours to complete

English

What you will 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

Skills you will gain

Analysis of Algorithms
Hashtables
Algorithm Design
Python Programming
Data Structure Design

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 3 in the

Data Science Foundations: Data Structures and Algorithms Specialization

Intermediate Level

Calculus: derivatives and integrals. Probability theory: distributions, expectations, and moments. Some programming experience with Python.

Approx. 34 hours to complete

English

Instructor

Instructor rating

4.8/5 (81 Ratings)

Sriram Sankaranarayanan

Professor

Department of Computer Science

27,126 Learners

3 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Content Rating94%(1,403 ratings)

Week1

Week 1

10 hours to complete

Basics of Algorithms Through Searching and Sorting

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.

10 hours to complete

7 videos (Total 202 min), 10 readings, 5 quizzes

7 videos

What is an Algorithm?28mAn Introduction Through the Insertion Sort Algorithm44mTime and Space Complexity30mAsymptotic Notation31mBinary Search22mMerge Sort Algorithm, Analysis and Proof of Correctness28mPitfalls and Logarithms15m

10 readings

Welcome and Where to Find Help 10mImportant Prerequisites10mLogistics: Textbook and Readings10mCLRS Chapter 110mOverview of Module 110mCLRS Chapter 210mCLRS Chapter 310mBinary Search Lecture Slides10mJupyter Notebook on Binary Search10mNotes on MergeSort10m

4 practice exercises

Insertion Sort and Running Times30mAsymptotic Notation and Complexity30mBinary Search 30mMergesort Algorithm30m

Week2

Week 2

9 hours to complete

Heaps and Hashtable Data Structures

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.

9 hours to complete

5 videos (Total 120 min), 6 readings, 6 quizzes

5 videos

A Simple Data Structure: The Dynamic Array20mHeap, Min/Max-Heaps and Properties of Heaps24mHeap Primitives: Bubble Up/Bubble Down29mPriority Queues, Heapify, and Heapsort28mHashtables - Introduction17m

6 readings

Overview of Module 210mCLRS Chapter 10, 10.1 + Jupyter Notebook50mCLRS Chapter 6.1 and 6.210mCLRS Chapter 6.310mCLRS Chapter 6.4 and 6.510mCLRS Chapter 11.1 and 11.2 10m

5 practice exercises

Basics of Data Structures30mBasics of Heap Data Structures30mBubble-Up/Bubble-Down, Insertion and Deletion Operations30mHeapify, Priority Queues and Heapsort30mHashtables30m

Week3

Week 3

9 hours to complete

Randomization: Quicksort, Quickselect, and Hashtables

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.

9 hours to complete

7 videos (Total 152 min), 6 readings, 6 quizzes

7 videos

Introduction to Randomization + Average Case Analysis + Recurrences23mPartition and Quicksort Algorithm13mDetailed Design of Partitioning Schemes25mAnalysis of Quicksort Algorithm28mQuickselect Algorithm and its Applications18mSelecting Hash Functions22mUniversal Hash Functions and Analysis20m

6 readings

Overview of Module 310mCLRS Chapter 7.110mCLRS Chapter 7.110mCLRS Chapter 7.2 - 7.410mCLRS Chapter 9.1, 9.210mCLRS Chapter 11.310m

5 practice exercises

Quicksort and Partition 30mPartition Schemes30mAnalysis of Quicksort30mQuickselect Algorithm30mUniversal Hash Functions30m

Week4

Week 4

6 hours to complete

Applications of Hashtables

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.

6 hours to complete

5 videos (Total 113 min), 6 readings, 2 quizzes

Reviews

4.7

68 reviews

5 stars
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3 stars
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TOP REVIEWS FROM ALGORITHMS FOR SEARCHING, SORTING, AND INDEXING

by MCMay 29, 2022

It is amazing from week 1 to week 3 . I really learned a a lot. But I am kind of lost when in week 4.

by MSJun 25, 2022

this course is very good .

you learn to make best slogithm to solve a problem.

by STAug 10, 2021

this is the best course on data structures that i have found! Pr. Sriram Sankaranarayanan thank you! if i was a multi-billionaire i would definitely donate a check to you for this wonderful course :)

by RMMay 13, 2022

Some programming assignments were easy. Apart from that this course is excellent

View all reviews

About the Data Science Foundations: Data Structures and Algorithms Specialization

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.

The courses in this specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Data Science Foundations: Data Structures and Algorithms

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Algorithms for Searching, Sorting, and Indexing

About this Course

What you will learn

Skills you will gain