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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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.

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Earn a Certificate upon completion

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Course 2 of 3 in the

Data Science Foundations: Data Structures and Algorithms Specialization

Advanced Level

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

Approx. 34 hours to complete

English

Subtitles: English

What you will learn

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

Skills you will gain

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

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.

Course 2 of 3 in the

Data Science Foundations: Data Structures and Algorithms Specialization

Advanced Level

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

Approx. 34 hours to complete

English

Subtitles: English

Instructor

Instructor rating

4.94/5 (17 Ratings)

Sriram Sankaranarayanan
Top Instructor

Professor

Department of Computer Science

14,599 Learners

3 Courses

Offered by

University of Colorado 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.

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

Week

Week 1

10 hours to complete

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 hours to complete

5 videos (Total 147 min), 9 readings, 6 quizzes

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 readings

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 practice exercises

Basics of Binary Search Trees30m

Binary Search Tree: Insert and Delete30m

Red-Black Tree Basics30m

Tree Rotations30m

Skip Lists30m

Week

Week 2

8 hours to complete

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 hours to complete

7 videos (Total 125 min), 6 readings, 5 quizzes

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 readings

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 practice exercises

Graph Representations30m

Combined Quiz on Graph Traversals30m

Topological Sort Graphs30m

Strongly Connected Components30m

Week

Week 3

8 hours to complete

Union-Find Data Structures and Spanning Tree Algorithms

Union Find Data-structure with rank compression.

8 hours to complete

5 videos (Total 127 min), 5 readings, 5 quizzes

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 readings

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 practice exercises

Amortized Analysis30m

Minimum Spanning Tree30m

Kruskal's Algorithm30m

Disjoint Set Forest30m

Week

Week 4

8 hours to complete

Shortest Path Algorithms

In this module, you will learn about:

8 hours to complete

6 videos (Total 155 min), 6 readings, 4 quizzes

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 readings

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 practice exercises

Shortest Path Problems Properties30m

Shortest Path - Bellman Ford Algorithm30m

Dijkstra's Algorithm30m

Reviews

4.8

7 reviews

5 stars
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TOP REVIEWS FROM TREES AND GRAPHS: BASICS

by BCJul 2, 2021

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

by APMar 29, 2022

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

by SKAug 28, 2021

Great lecturer and course materials. Assignments were fun also

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:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
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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Trees and Graphs: Basics