Advanced Data Structures in Java

Go to Course: https://www.coursera.org/learn/advanced-data-structures

Introduction

### Course Review: Advanced Data Structures in Java on Coursera **Overview** The "Advanced Data Structures in Java" course on Coursera is an exceptional opportunity for intermediate Java developers aiming to deepen their understanding of data structures, particularly graphs. This course tackles real-world problems like route optimization in Google Maps, efficient data packet routing by internet routers, and resource allocation strategies used by aid organizations. Through practical application and theoretical groundwork, learners are equipped with the skills necessary to represent and manipulate structured data effectively. **Syllabus Breakdown** 1. **Introduction to the Course** The course begins with an introduction to its unique structure and expectations. A pre-course quiz helps students assess their readiness. The highlight of this week is the initiation of a mapping application inspired by Google Maps. 2. **Introduction to Graphs** As the backbone of the course’s content, this week delves into graphs - the primary data structure for this course. Students will learn about the essential properties of graphs and get hands-on coding experience with them. 3. **Class Design and Simple Graph Search** This week emphasizes the importance of designing classes from scratch, which is crucial for advancing as a Java developer. The essentials of depth-first and breadth-first search algorithms are introduced, allowing students to structure their map search engine effectively. 4. **Finding Shortest Paths in Weighted Graphs** Building on previous lessons, this section incorporates weights into graphs, transforming them into weighted graphs. Students get to explore advanced algorithms for finding the shortest path, a key feature for any mapping application. 5. **Route Planning and NP-Hard Graph Problems** The spotlight shifts to more complex problems like the Travelling Salesperson Problem (TSP), which offers insights into NP-Hard challenges. Students learn to approach difficult problems using heuristics and approximation algorithms, a skill highly valuable in computer science. 6. **End of Course Project Extension** In the final week, learners are invited to personalize their mapping application, applying their knowledge creatively and receiving feedback through a peer review gallery. This offers an excellent opportunity for practical application and hands-on learning. **What I Loved About the Course** - **Practical Application:** The course effectively marries theory with practice. Creating a mapping application allows students to apply graph data structures in a meaningful way, solidifying their understanding. - **In-Depth Learning:** Each module builds on the previous week's concepts, allowing for a gradual ramp-up of complexity. The introduction of advanced concepts like NP-Hard problems enables deeper thinking and problem-solving skills. - **Community Engagement:** The optional peer review process encourages interaction among students, fostering a sense of community and camaraderie while providing constructive feedback. **Recommendation** I highly recommend the "Advanced Data Structures in Java" course for any intermediate Java programmers looking to enhance their skills in data structure manipulation, particularly graphs. The focus on practical applications, along with comprehensive theory, provides an enriching learning experience that prepares students for real-world programming challenges. Whether you aim to improve your software development skills or tackle complex algorithms, this course is an excellent investment in your professional development. With engaging content, supportive community features, and a meaningful project at its core, this course stands out as a must-take for aspiring data structure experts. Enroll today and take your Java programming skills to the next level!

Syllabus

Introduction to the Course

Welcome to the first week in the third course of our Intermediate Java Programming Specialization. Once again start with introductions, and in particular introduce the unique structure of this course. Also, if you're not sure if this course is right for you, we've got an optional pre-course quiz coming right up that can help you figure out if you're in the right place. If you decide to stay with us (and we really hope you will!) we've got a great backbone project for you: your very own mapping application, inspired by Google Maps! The core data structure throughout this course is graphs, which may very well be the most fundamental data structure in all of computer science. Ready to begin? So are we!

This week we'll start getting technical, introducing you to the central data structure in the course: Graphs. You'll learn the basics and then have a chance to dive in a little deeper into the code, getting ready to start building that Google Maps-like application.

This week you'll get the backbone of your map search engine up and running. In previous courses, including the previous courses in this specialization, you've probably been given most of the classes you needed to complete the assignments. But learning how to design classes from scratch is a key skill that you will need as you become a more sophisticated Java programmer. This week we'll give you the tools you need to create a robust and elegant class design for your map search engine. We'll introduce a similar problem and show you how it can be represented as a graph. Then we'll introduce two core search algorithms: depth-first search and breadth-first search. Finally, we'll turn our graph problem into a set of Java classes. Your task on the programming assignment this week will be to do the same thing, but in the context on the map search engine!

In the past two weeks, you've developed a strong understanding of how to design classes to represent a graph and how to use a graph to represent a map. In this week, you'll add a key feature of map data to our graph representation -- distances -- by adding weights to your edges to produce a "weighted graph". Although this might seem like a small change, the algorithms that work for unweighted graphs may prove ineffective for weighted graphs. To address this problem, you'll explore more advanced shortest path algorithms. First, you'll see how to find the shortest path on a weighted graph, then you'll see how to find it more quickly. In the project, you'll apply these ideas to create the core of any good mapping application: finding the shortest route from one location to another.

In this week, we'll go beyond the problem of finding a path between two points, and focus on problems requiring overall path planning. For example, if you wanted to go on errands and visit 6 different locations before returning home, what is the optimal route? This problem is actually a really well known problem in computer science known as the Travelling Salesperson Problem (TSP). Attempting to solve the problem will lead us to explore complexity theory, what it means to be NP-Hard, and how to solve "hard" problems using heuristics and approximation algorithms. We'll end the week by showing how reformulating a problem can have a huge impact: making something which was effectively unsolvable before, now solvable!

You made it to the last week of our course! We're glad you're still with us. As a reward, there's no new content to learn this week. Instead you'll get the opportunity to extend your project in a direction of your own choosing. We hope you've got some neat ideas for personalizing your map application, and we look forward to seeing them in the peer review gallery. Submitting to the peer review gallery is optional (though the extension is required), but we hope you'll choose to participate.