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

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63 ratings

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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Shareable Certificate

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. 33 hours to complete

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. 33 hours to complete

English

Instructor

Instructor rating

4.8/5 (26 Ratings)

Sriram Sankaranarayanan

Professor

Department of Computer Science

27,289 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

Week1

Week 1

10 hours to complete

Binary Search Trees and Algorithms on Trees

10 hours to complete

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

5 videos

Binary Search Trees -- Introduction and Properties22mBinary Search Trees -- Insertion and Deletion31mRed-Black Trees Basics33mRed-Black Trees -- Rotations/Algorithms for Insertion (and Deletion)29mSkip Lists30m

9 readings

Welcome and Where to Find Help 10mImportant Prerequisites10mLogistics: Textbook and Readings10mOverview of Module 110mReading CLRS Chapter 1210mCLRS Chapter 12.1-12.310mCLRS Chapter 13 - 13.110mCLRS Chapter 13.2 - 13.310mSkip 10m

5 practice exercises

Basics of Binary Search Trees30mBinary Search Tree: Insert and Delete30mRed-Black Tree Basics30mTree Rotations30mSkip Lists30m

Week2

Week 2

8 hours to complete

Basics of Graphs and Graphs Traversals

8 hours to complete

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

7 videos

Graphs and Their Representations14mGraph Traversals and Breadth First Traversal17mDepth First Search33mTopological Sorting and Applications11mStrongly Connected Components - Definitions15mStrongly Connected Components - Properties16mStrongly Connected Components - Algorithm16m

6 readings

Overview of Module 210mCLRS Chapter 22 (Section 22.1)10mCLRS Chapter 22 (Section 22.2)10mCLRS Chapter 22 (Section 22.3)10mCLRS Chapter 22 (Section 22.410mCLRS Chapter 22 (Section 22.5)10m

4 practice exercises

Graph Representations30mCombined Quiz on Graph Traversals30mTopological Sort Graphs30mStrongly Connected Components30m

Week3

Week 3

8 hours to complete

Union-Find Data Structures and Spanning Tree Algorithms

8 hours to complete

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

5 videos

Amortized Analysis of Data Structures27mAmortized Analysis: Potential Functions26mSpanning Trees and Minimal Spanning Trees with Applications26mKruskal’s Algorithm for Finding Minimal Spanning Trees8mUnion-Find Data Structures and Rank Compression38m

5 readings

Overview of Module 310mCLRS Chapter 1710mCLRS Chapter 23 (Section 23.1)10mCLRS Chapter 23 (Section 23.2)10mCLRS Chapter 2110m

4 practice exercises

Amortized Analysis30mMinimum Spanning Tree30mKruskal's Algorithm30mDisjoint Set Forest30m

Week4

Week 4

8 hours to complete

Shortest Path Algorithms

8 hours to complete

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

6 videos

Shortest Path Problems and Their Properties29mBellman-Ford Algorithm for Single Source Shortest Paths45mDijkstra’s Algorithm for Single Source Shortest Paths with Nonnegative Edge Weights20mProof of Dijkstra's Algorithm12mShortest Path on DAGs11mAll Pairs Shortest Path Problems and Floyd-Warshall’s Algorithm34m

6 readings

Overview of Module 410mCLRS Chapter 24 (up to section 24.1)10mCLRS Chapter 24 (Section 24.1)10mCLRS Chapter 24 (Section 24.3 and 24.5)10mCLRS Chapter 24 (Section 24.2)10mCLRS Chapter 25 (Sections 25.1 and 25.2)10m

3 practice exercises

Shortest Path Problems Properties30mShortest Path - Bellman Ford Algorithm30mDijkstra's Algorithm30m

Reviews

4.7

17 reviews

5 stars
81.53%
4 stars
13.84%
3 stars
3.07%
1 star
1.53%

TOP REVIEWS FROM TREES AND GRAPHS: BASICS

by MHJan 1, 2023

Excellent content. Lectures along with textbook provide student with working knowledge of trees and graphs.

by DNSep 28, 2022

This course is easy to understand and implement. It needs more programming exercises further!

by MCDec 25, 2022

Excellent explanation of complex topics. The coding exercise are also well thought out and the tips along the way help to reinforce key concepts.

by BCJul 2, 2021

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

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.

More questions? Visit the Learner Help Center.

Trees and Graphs: Basics

About this Course

What you will learn

Skills you will gain