Coursera

Ordered Data Structures (Coursera)

Offered by University of Illinois at Urbana-Champaign,

In this course, you will learn new data structures for efficiently storing and retrieving data that is structured in an ordered sequence. Such data includes an alphabetical list of names, a family tree, a calendar of events or an inventory organized by part numbers. The specific data structures covered by this course include arrays, linked lists, queues, stacks, trees, binary trees, AVL trees, B-trees and heaps. This course also shows, through algorithm complexity analysis, how these structures enable the fastest algorithms to search and sort data.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

Course 2 of 3 in the Accelerated Computer Science Fundamentals Specialization.

Course Syllabus

Week 1: Orientation; Linear Structures
Week 2: Introduction to Tree Structures
Week 3: Advanced Tree Structures
Week 4: Heap Structures

Go to Class

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Coursera

University of Colorado System

Data Structures and Design Patterns for Game Developers (Coursera)

CS: Software Engineering CS: Programming

This course is the fourth course in the specialization about learning how to develop video games using the C# programming language and the Unity game engine on Windows or Mac. Why use C# and Unity instead of some other language and game engine? Well, C# is a really good language for learning how to program and then programming professionally. Also, the Unity game engine is very popular with indie game developers; Unity games were downloaded 16,000,000,000 times in 2016! Finally, C# is one of the programming languages you can use in the Unity environment.

Nov 9th 2026

4 Weeks

Programming C# Data Structures

Coursera

University of Colorado Boulder

Trees and Graphs: Basics (Coursera)

Computer Science

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.

Nov 9th 2026

4 Weeks

Data Structures Graphs Graph Algorithms

Coursera

University of Pennsylvania

Inheritance and Data Structures in Java (Coursera)

CS: Software Engineering Computer Science

This course provides a comprehensive look at Java inheritance, including access modifiers and overriding methods. Students are introduced to abstract classes, and will learn how to read and write to files, use regular expressions for parsing text, and how to leverage complex data structures like collections and maps. Additionally, this course offers strategies for catching errors and debugging code, including an overview of Eclipse’s debugging tool.

Nov 9th 2026

4 Weeks

Java Debugging Data Structures

Coursera

LearnQuest

Python Scripting: Dates, Classes and Collections (Coursera)

Computer Science

This course is the second course in a series that aims to prepare you for a role working as a programmer. In this course, you will be introduced to the four main concepts in programming: Advanced String Operations and Dates, Modeling Classes, Development of Classes and Collections. Labs will allow the students to apply the material in the lectures in simple computer programs designed to re-enforce the material in the lesson.

Nov 9th 2026

4 Weeks

Programming Python Data Structures

Coursera

École Polytechnique Fédérale de Lausanne

Parallel programming (Coursera)

CS: Software Engineering CS: Programming

Nov 9th 2026

4 Weeks

Algorithms Data Structures Parallel Computing

Coursera

University of Colorado Boulder

Fundamental Tools of Data Wrangling (Coursera)

Statistics & Data Analysis Data Science

Data wrangling is a crucial step in the data analysis process, as it involves the transformation and preparation of raw data into a suitable format for analysis. The "Fundamental Tools for Data Wrangling" course is designed to provide participants with essential skills and knowledge to effectively manipulate, clean, and analyze data. Participants will be introduced to the fundamental tools commonly used in data wrangling, including Python, data structures, NumPy, and pandas. Through hands-on exercises and practical examples, participants will gain the necessary proficiency to work with various data formats and effectively prepare data for analysis.

Nov 9th 2026

5-12 Weeks

Python Data Structures IPython

Coursera

University of Colorado Boulder

Dynamic Programming, Greedy Algorithms (Coursera)

Computer Science

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.

Nov 9th 2026

4 Weeks

Algorithms Data Structures Dynamic Programming

Coursera

École Polytechnique Fédérale de Lausanne

Parallel programming (Scala 2 version) (Coursera)

CS: Software Engineering

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library.

Nov 2nd 2026

4 Weeks

Programming Algorithms Data Structures

Coursera

University of Colorado Boulder

Advanced Data Structures, RSA and Quantum Algorithms (Coursera)

Computer Science

Introduces number-theory based cryptography, basics of quantum algorithms and advanced data-structures.

Nov 9th 2026

4 Weeks

Cryptography Data Structures Quantum Computing

Coursera

Stanford University

Graph Search, Shortest Paths, and Data Structures (Coursera)

CS: Theory

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Nov 9th 2026

4 Weeks

Algorithms Data Structures Computer Science

Coursera

University of Colorado Boulder

Algorithms for Searching, Sorting, and Indexing (Coursera)

Computer Science

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.

Nov 9th 2026

4 Weeks

Algorithms Data Structures QuickSort

Coursera

University of California, San Diego

Data Structures and Performance (Coursera)

CS: Theory Data Science

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? This is an intermediate Java course. We recommend this course to learners who have previous experience in software development or a background in computer science, and in particular, we recommend that you have taken the first course in this specialization (which also requires some previous experience with Java).

Nov 2nd 2026

5-12 Weeks

Programming Java Algorithms