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

4.8
estrellas
38 calificaciones

Sriram Sankaranarayanan

4843 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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Basic algorithms on tree data structures, binary search trees, self-balancing trees, graph data structures and basic traversal algorithms on graphs. This course also covers advanced topics such as kd-trees for spatial data and algorithms for spatial data.
Trees and Graphs: Basics 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.

Fechas límite flexibles

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Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 3 en

Programa especializado: Data Science Foundations: Data Structures and Algorithms

Nivel avanzado

Completion of the previous course. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.

Aprox. 34 horas para completar

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

Define basic tree data structures and identify algorithmic functions associated with them
Execute traversals and create graphs within a binary search tree structure
Describe strongly connected components in graphs

Habilidades que obtendrás

Analysis of Algorithms
Algorithm Design
Python Programming
Data Structure Design
Graphs Algorithms

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 2 de 3 en

Programa especializado: Data Science Foundations: Data Structures and Algorithms

Nivel avanzado

Completion of the previous course. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.

Aprox. 34 horas para completar

Inglés (English)

Subtítulos: Inglés (English)

Instructor

Calificación del instructor

4,94/5 (18 calificaciones)

Sriram Sankaranarayanan

Professor

Department of Computer Science

17.977 alumnos

3 cursos

ofrecido por

Universidad de Colorado en Boulder

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

Comienza a trabajar para obtener tu maestría

Este curso es parte del 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

Semana

Semana 1

10 horas para completar

Binary Search Trees and Algorithms on Trees

In this module, you will learn about binary search trees and basic algorithms on binary search trees. We will also become familiar with the problem of balancing in binary search trees and study some solutions for balanced binary search trees such as Red-Black Trees.

10 horas para completar

5 videos (Total 147 minutos), 9 lecturas, 6 cuestionarios

5 videos

Binary Search Trees -- Introduction and Properties22m

Binary Search Trees -- Insertion and Deletion31m

Red-Black Trees Basics33m

Red-Black Trees -- Rotations/Algorithms for Insertion (and Deletion)29m

Skip Lists30m

9 lecturas

Welcome and Where to Find Help10m

Important Prerequisites10m

Logistics: Textbook and Readings10m

Overview of Module 110m

Reading CLRS Chapter 1210m

CLRS Chapter 12.1-12.310m

CLRS Chapter 13 - 13.110m

CLRS Chapter 13.2 - 13.310m

Skip 10m

5 ejercicios de práctica

Basics of Binary Search Trees30m

Binary Search Tree: Insert and Delete30m

Red-Black Tree Basics30m

Tree Rotations30m

Skip Lists30m

Semana

Semana 2

8 horas para completar

Basics of Graphs and Graphs Traversals

In this module, you will learn about graphs and various basic algorithms on graphs such as depth first/breadth first traversals, finding strongly connected components, and topological sorting.

8 horas para completar

7 videos (Total 125 minutos), 6 lecturas, 5 cuestionarios

7 videos

Graphs and Their Representations14m

Graph Traversals and Breadth First Traversal17m

Depth First Search33m

Topological Sorting and Applications11m

Strongly Connected Components - Definitions15m

Strongly Connected Components - Properties16m

Strongly Connected Components - Algorithm16m

6 lecturas

Overview of Module 210m

CLRS Chapter 22 (Section 22.1)10m

CLRS Chapter 22 (Section 22.2)10m

CLRS Chapter 22 (Section 22.3)10m

CLRS Chapter 22 (Section 22.410m

CLRS Chapter 22 (Section 22.5)10m

4 ejercicios de práctica

Graph Representations30m

Combined Quiz on Graph Traversals30m

Topological Sort Graphs30m

Strongly Connected Components30m

Semana

Semana 3

8 horas para completar

Union-Find Data Structures and Spanning Tree Algorithms

Union Find Data-structure with rank compression.

8 horas para completar

5 videos (Total 127 minutos), 5 lecturas, 5 cuestionarios

5 videos

Amortized Analysis of Data Structures27m

Amortized Analysis: Potential Functions26m

Spanning Trees and Minimal Spanning Trees with Applications26m

Kruskal’s Algorithm for Finding Minimal Spanning Trees8m

Union-Find Data Structures and Rank Compression38m

5 lecturas

Overview of Module 310m

CLRS Chapter 1710m

CLRS Chapter 23 (Section 23.1)10m

CLRS Chapter 23 (Section 23.2)10m

CLRS Chapter 2110m

4 ejercicios de práctica

Amortized Analysis30m

Minimum Spanning Tree30m

Kruskal's Algorithm30m

Disjoint Set Forest30m

Semana

Semana 4

8 horas para completar

Shortest Path Algorithms

In this module, you will learn about:

8 horas para completar

6 videos (Total 155 minutos), 6 lecturas, 4 cuestionarios

6 videos

Shortest Path Problems and Their Properties29m

Bellman-Ford Algorithm for Single Source Shortest Paths45m

Dijkstra’s Algorithm for Single Source Shortest Paths with Nonnegative Edge Weights20m

Proof of Dijkstra's Algorithm12m

Shortest Path on DAGs11m

All Pairs Shortest Path Problems and Floyd-Warshall’s Algorithm34m

6 lecturas

Overview of Module 410m

CLRS Chapter 24 (up to section 24.1)10m

CLRS Chapter 24 (Section 24.1)10m

CLRS Chapter 24 (Section 24.3 and 24.5)10m

CLRS Chapter 24 (Section 24.2)10m

CLRS Chapter 25 (Sections 25.1 and 25.2)10m

3 ejercicios de práctica

Shortest Path Problems Properties30m

Shortest Path - Bellman Ford Algorithm30m

Dijkstra's Algorithm30m

Reseñas

4.8

10 reseña

5 stars
80,95 %
4 stars
16,66 %
3 stars
2,38 %

Principales reseñas sobre TREES AND GRAPHS: BASICS

por AP29 de mar. de 2022

I have grade 100% My mail id- freespace644@gmail.com

por BC2 de jul. de 2021

very solid course - would love more programming assignments and tougher final

por SK28 de ago. de 2021

Great lecturer and course materials. Assignments were fun also

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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Trees and Graphs: Basics