Este curso forma parte de Programa especializado: Data Science Foundations: Data Structures and Algorithms

4.7
estrellas
226 calificaciones

93 %

Sriram Sankaranarayanan

21.080 ya inscrito

Ofrecido Por

Parte del título de grado Master of Science in Data Science
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Programa especializado: Data Science Foundations: Data Structures and AlgorithmsUniversidad de Colorado en Boulder

Acerca de este Curso

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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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Curso 1 de 3 en

Programa especializado: Data Science Foundations: Data Structures and Algorithms

Nivel intermedio

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

Aprox. 34 horas para completar

Inglés (English)

Qué aprenderás

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

Habilidades que obtendrás

Analysis of Algorithms
Hashtables
Algorithm Design
Python Programming
Data Structure Design

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 1 de 3 en

Programa especializado: Data Science Foundations: Data Structures and Algorithms

Nivel intermedio

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

Aprox. 34 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,8/5 (81 calificaciones)

Sriram Sankaranarayanan

Professor

Department of Computer Science

26.928 alumnos

3 cursos

Ofrecido por

Universidad de Colorado en Boulder

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Este curso es parte de Master of Science in Data Science completamente en línea de Universidad de Colorado en Boulder. Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.

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Programa - Qué aprenderás en este curso

Calificación del contenido93%(1,361 calificaciones)

Semana1

Semana 1

10 horas para completar

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 horas para completar

7 videos (Total 202 minutos), 10 lecturas, 5 cuestionarios

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 lecturas

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 ejercicios de práctica

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

Semana2

Semana 2

9 horas para completar

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 horas para completar

5 videos (Total 120 minutos), 6 lecturas, 6 cuestionarios

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 lecturas

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 ejercicios de práctica

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

Semana3

Semana 3

9 horas para completar

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 horas para completar

7 videos (Total 152 minutos), 6 lecturas, 6 cuestionarios

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 lecturas

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

5 ejercicios de práctica

Quicksort and Partition 30mPartition Schemes30mAnalysis of Quicksort30mQuickselect Algorithm30mUniversal Hash Functions30m

Semana4

Semana 4

6 horas para completar

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 horas para completar

5 videos (Total 113 minutos), 6 lecturas, 2 cuestionarios

Reseñas

4.7

68 reseña

5 stars
82,53 %
4 stars
10,48 %
3 stars
2,62 %
2 stars
1,74 %
1 star
2,62 %

Principales reseñas sobre ALGORITHMS FOR SEARCHING, SORTING, AND INDEXING

por ST10 de ago. de 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 :)

por DN29 de ago. de 2022

Week 4 about Hashtable is a little difficult to understand! I need more exercise and programming for week 4.

por SL6 de feb. de 2023

The lector is actually professional and explains everything clearly

por MC29 de may. de 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.

Ver todas las reseñas

Acerca de Programa especializado: Data Science Foundations: Data Structures and Algorithms

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

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Algorithms for Searching, Sorting, and Indexing

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás