Java: Linear Data Structures and Trees from Coursera

Code and run your first Java program about data structures and algorithms in minutes without installing anything!

This course is designed for learners familiar with Java basics and object-oriented programming. It provides a solid foundation of not just Java, but core data structures and algorithms topics that can be transferred to other languages. The modules in this course cover linear data structures like lists (singly linked, doubly linked, circular), stacks, queues, and introduces the first non-linear data structure trees.

To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You'll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.

What's inside

Syllabus

Tree ADT

Welcome to Week 3 of the Linear Data Structures and Trees course. This module introduces the first non-linear data structure, trees. Topics include the tree ADT, binary trees, binary search trees, tree traversal, and different searching algorithms for trees.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a solid foundation in core data structures and algorithms topics, which can be transferred to other languages, making it valuable for polyglot programmers

Uses runnable code examples with suggested edits to explore code examples further, which helps build a deeper understanding by doing

Assignments contain short explanations with images and runnable code examples, which allows for a hands-on, self-paced learning experience

Requires familiarity with Java basics and object-oriented programming, so learners should have some prior experience with Java

Covers self-balancing trees like AVL trees, Red-Black trees, and B-trees, which are essential for efficient data storage and retrieval

Reviews summary

Java data structures and trees with codio

According to learners, this course provides a solid foundation in core linear data structures like lists, stacks, and queues, and introduces trees. A major strength highlighted by students is the hands-on approach, utilizing the Codio platform for runnable code examples and interactive assignments. The text-based explanations are generally found to be clear, but some learners felt that more complex or advanced topics, particularly self-balancing trees, could benefit from greater depth or additional explanation. Success in the course seems to rely heavily on meeting the stated prerequisites in Java basics and object-oriented programming; students without this background may find the pace challenging.

Video-free, uses text and runnable code.

"I appreciated the text-based format and runnable examples; it allowed me to go at my own pace and focus on the code."

"As someone who prefers reading and coding over watching videos, this format was perfect for me."

"While the text is generally clear, sometimes a video explanation would have helped visualize the more complex data structure operations."

"The lack of video lectures might be a drawback for learners who prefer visual explanations of algorithms."

Provides a good base in core data structures.

"This course gave me a really solid foundation in linked lists, stacks, and queues. The explanations for these were very clear."

"I found the initial modules on linear data structures and basic trees to be well-explained and easy to follow."

"If you're new to data structures, the way it breaks down ADTs and basic implementations is very beneficial."

"The course covers the fundamental data structures needed to move on to more complex topics."

Excellent practical coding exercises.

"The hands-on coding exercises are the strongest part of this course; being able to run and modify code directly in the browser was fantastic."

"I really appreciated the integrated coding environment. It made practicing data structures much more engaging than just reading."

"The assignments helped solidify the concepts immediately. I learned by doing, which is exactly what I needed."

"Having runnable code examples alongside explanations was incredibly helpful for understanding how the concepts work in practice."

Later topics feel rushed or less detailed.

"While the linear structures were great, the section on self-balancing trees (AVL, Red-Black) felt very rushed and lacked depth."

"I struggled with the advanced tree topics in the final week. It felt like the complexity jumped significantly without enough explanation."

"Could use more detailed explanations and examples for B-trees and Red-Black trees."

"The last module was challenging because the text explanations weren't quite sufficient for the complexity of the algorithms presented."

Needs strong Java/OOP background.

"Definitely recommend having a solid grasp of Java and OOP before starting this. It assumes you know these well."

"As the course description says, familiarity with Java basics is crucial. If you don't have it, you will struggle."

"I found myself constantly reviewing Java concepts because the course jumps right into applying them to data structures."

"Came in with some Java experience, but wished I had reviewed OOP more thoroughly beforehand."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Java: Linear Data Structures and Trees with these activities:

Review Java Fundamentals

Show steps

Reinforce your understanding of Java fundamentals, including object-oriented programming concepts, before diving into data structures. This will make it easier to grasp the implementation details of lists, stacks, queues, and trees.

Show steps

Review basic syntax and data types.
Practice writing simple Java programs.
Brush up on object-oriented principles like inheritance and polymorphism.

Review 'Data Structures and Algorithms in Java'

Show steps

Reference a comprehensive textbook on data structures and algorithms in Java to deepen your understanding of the concepts covered in the course.

View Data Structures & Algorithms in Python... on Amazon

Show steps

Read the chapters related to lists, stacks, queues, and trees.
Study the code examples provided in the book.
Try implementing the data structures yourself based on the book's explanations.

Implement Basic List Operations

Show steps

Practice implementing basic operations for singly linked lists, such as insertion, deletion, and searching. This will solidify your understanding of list ADTs and prepare you for more complex data structures.

Show steps

Create a singly linked list class in Java.
Implement methods for adding nodes to the beginning and end of the list.
Implement methods for deleting nodes from the list.
Write test cases to verify the correctness of your implementation.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Cheat Sheet for Tree Traversal

Show steps

Create a concise cheat sheet summarizing the different tree traversal algorithms (inorder, preorder, postorder, level-order). This will help you quickly recall the steps involved in each algorithm.

Show steps

Research the different tree traversal algorithms.
Summarize the steps for each algorithm in a clear and concise manner.
Include code examples in Java for each algorithm.
Organize the cheat sheet for easy reference.

Build a Simple Tree Visualizer

Show steps

Start a project to visualize binary search trees. This will help you understand tree traversal algorithms and the structure of self-balancing trees.

Show steps

Choose a Java GUI library (e.g., Swing or JavaFX).
Implement a binary search tree data structure.
Create a visual representation of the tree nodes and edges.
Implement tree traversal algorithms (e.g., inorder, preorder, postorder) to update the visualization.

Review 'Introduction to Algorithms'

Show steps

Consult a classic algorithms textbook to gain a deeper understanding of the theoretical foundations of data structures and algorithms.

View Introduction to Algorithms, fourth edition on Amazon

Show steps

Read the sections on trees and tree algorithms.
Focus on the theoretical explanations and proofs.
Compare the algorithms presented in the book with the Java implementations covered in the course.

Contribute to a Java Data Structures Library

Show steps

Contribute to an open-source Java data structures library by implementing a missing feature or fixing a bug. This will provide valuable hands-on experience and expose you to real-world software development practices.

Show steps

Find an open-source Java data structures library on GitHub or GitLab.
Identify a missing feature or a bug to fix.
Implement the feature or fix the bug.
Submit a pull request with your changes.

Career center

Learners who complete Java: Linear Data Structures and Trees will develop knowledge and skills that may be useful to these careers:

Algorithm Engineer

Algorithm engineers design, develop, and implement algorithms, often for specialized applications. This role requires a strong grasp of core data structures and algorithms, something this course helps provide. The course's focus on linear data structures like lists, stacks, queues, and non-linear structures like trees fits well with the expertise needed for an algorithm engineer. The fact that you’ll work with runnable code examples and complete practical coding exercises in this course can be invaluable in developing the skills necessary for implementing and optimizing algorithms.

See salaries and explore the career path for Algorithm Engineer

Software Developer

A software developer designs, develops, and maintains software applications. This role requires a strong understanding of data structures and algorithms, which a course like this can help build. You'll be expected to use data structures and algorithms on a daily basis. A deep comprehension of abstract data types such as lists, stacks, queues, and trees is essential for a software developer, and this course provides hands-on experience with these concepts in Java. The course's focus on runnable code examples and practical coding exercises will help you feel more prepared to start developing software.

See salaries and explore the career path for Software Developer

Game Developer

Game developers design and develop video games, and often use a variety of data structures and algorithms to create game logic, manage game objects, and optimize game performance. This course may be helpful for a game developer. This course covers linear data structures such as lists, stacks, queues, and trees, which can be used in game development for managing game states, object hierarchies, and other game-related data. The practical exercises in this course can help a game developer better understand these data structures and gain a hands-on understanding by exploring runnable code examples.

See salaries and explore the career path for Game Developer

Mobile Application Developer

Mobile application developers create applications for mobile devices, such as smartphones and tablets. They need to understand how to efficiently process and store data. This course may be helpful for a mobile application developer. The course explores data structures like lists, stacks, queues, and trees, which are often used in mobile apps for storing and retrieving data. The hands-on approach of the course, with runnable code examples and practical coding exercises, may be helpful for mobile app developers as they can help apply their skills directly when building applications. This course is taught using Java, which can be useful for developers working on Android applications.

See salaries and explore the career path for Mobile Application Developer

Data Engineer

Data engineers build and maintain the infrastructure to store and process data, and must have understanding of data storage and processing techniques. This course may be useful for a data engineer. The course explores data structures like lists, stacks, queues, and trees, which are core concepts in data management. The course's hands-on approach via practical exercises and runnable code examples will help those wish to understand how these structures work. Data engineers working on building and maintaining these systems can benefit from a strong background in common data structures.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

Machine learning engineers build, train, and deploy machine learning models. This requires an understanding of data structures and algorithms. A machine learning engineer may be interested in this course. This course covers linear data structures such as lists, stacks and queues, and non-linear data structures such as trees. These data structures are often used behind the scenes in machine learning to store and process data. Those who seek to build and deploy machine learning models may find this course useful.

See salaries and explore the career path for Machine Learning Engineer

Robotics Engineer

Robotics engineers design, build, and test robots. Robotics engineers need to understand how to efficiently process and store data, often in real-time. This course may be useful for a robotics engineer. The course covers data structures like lists, stacks, queues, and trees, which can be used in robot control systems to manage robot states, sensor data, and other information. The hands-on approach of this course, with runnable code examples and practical exercises, may assist robotics engineers in writing software for robots.

See salaries and explore the career path for Robotics Engineer

Data Scientist

Data scientists analyze large datasets to extract meaningful insights and often need to build efficient algorithms. This course may be useful for those who wish to be data scientists. The course covers linear data structures like lists, stacks, and queues, as well as non-linear structures such as trees, which can be useful for storing and accessing data. The hands-on nature of the course, with runnable code examples and practical assignments, provides a good foundation for understanding how these data structures work in practice. A data scientist may utilize these concepts when implementing machine learning algorithms or other data analysis techniques.

See salaries and explore the career path for Data Scientist

Embedded Systems Engineer

Embedded systems engineers design, develop, and test software and hardware for embedded systems, and should understand how to efficiently manage memory and data in resource-constrained environments. This course may be useful for an embedded systems engineer. The course explores abstract data types, such as lists, stacks, queues, and trees, which are important for software used in embedded systems. The practical, hands-on approach in this course may be useful to those creating embedded systems.

See salaries and explore the career path for Embedded Systems Engineer

Web Developer

Web developers create and maintain websites, and often work with data. A web developer may be interested in data structures. This course may be useful for those who wish to improve their background in data structures and algorithms. The course focuses on linear data structures like lists, stacks, queues, and non-linear structures like trees. These types of data structures can be used to manage information on the web. The course's emphasis on practical exercises may help a web developer understand how these structures are implemented in code.

See salaries and explore the career path for Web Developer

Cybersecurity Analyst

Cybersecurity analysts monitor and protect computer systems and networks from cyber threats, and often perform analysis on large amounts of data. A cybersecurity analyst may benefit from an understanding of data structures. This course may be useful for a cybersecurity analyst. This course covers data structures like lists, stacks, queues, and trees, which can be used to analyze and process security logs, and detect patterns that could indicate malicious activity. Its practical, hands-on approach can help with a cybersecurity analyst's understanding of how data is stored and manipulated.

See salaries and explore the career path for Cybersecurity Analyst

Computer Science Professor

A computer science professor teaches courses on computer science topics and conducts research in the field. A computer science professor should have a mastery of data structures and algorithms. This course may be helpful for those who wish to teach these concepts. This course covers many of the fundamental data structures that a professor needs to know. While it is practical in nature, this material is core to undergraduate computer science, and a professor will benefit from a deeper understanding of these concepts. A computer science professor usually holds a doctorate, and this course material would typically be covered during their studies.

See salaries and explore the career path for Computer Science Professor

Quality Assurance Engineer

Quality assurance engineers test software to ensure it meets requirements and is free of bugs. They may use specialized data structures and algorithms to automate testing. This course may be useful for a quality assurance engineer who wishes to understand how to test software that uses data structures. The course covers linear data structures such as lists, stacks, queues, and non-linear structures like trees. Although a quality assurance engineer might not need to implement these data structures, understanding them can be useful when testing them. This course's practical approach, with runnable code examples and hands-on exercises, may help with testing efforts.

See salaries and explore the career path for Quality Assurance Engineer

Database Administrator

Database administrators manage and maintain databases to ensure efficient and secure data storage and retrieval. Their work often involves optimizing database performance and implementing efficient data storage solutions. This course may be useful to a database administrator. While the course focuses on data structures such as lists, stacks, queues and trees, which are the building blocks of many databases, it does not directly teach database administration skills. However, understanding how data is organized can be beneficial for comprehending how databases function.

See salaries and explore the career path for Database Administrator

Financial Analyst

Financial analysts work with financial data to make recommendations, and may need to build applications to analyze data. A financial analyst may leverage a course in data structures and algorithms. This course may be useful to a financial analyst who wants to learn more about data storage and manipulation. The course teaches linear data structures such as lists, stacks, queues, and non-linear structures like trees. Although not specific to financial analysis, the concepts in the course could be useful for building custom analysis tools.

See salaries and explore the career path for Financial Analyst

Java

Linear Data Structures and Trees

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Java data structures and trees with codio

Activities

Career center

Reading list

Share

Similar courses