Byte-Sized-Chunks: Graph Algorithms and Problems in Java from Udemy

What's inside

Learning objectives

Design and implement software using canonical graph algorithms - djikstra, prim, kruskal, bellman ford and topological sort

Understand the use-cases for the common graph algorithm

Design and implement software using canonical graph algorithms - djikstra, prim, kruskal, bellman ford and topological sort
Understand the use-cases for the common graph algorithm

Syllabus

Its A Connected World!

You, This Course, and Us!

The graph is a data structure that is used to model a very large number of real world problems. It's also an programming interview favorite. The study of graphs and algorithms associated with graphs forms an entire field of study called graph theory.

Edges in a graph can be directed or undirected. A graph with directed edges forms a Directed Graph and those with undirected edges forms an Undirected Graph. These edges can be likened to one-way and two-way streets.

Different relationships can be modeled using either Directed or Undirected graphs. When a graph has no cycles it's called an acyclic graph. A graph with no cycles is basically a tree.

There are a number of different ways in which graphs can be implemented. However they all follow they same basic graph interface. The graph interface allows building up a graph by adding edges and traversing a graph by giving access to all adjacent vertices of any vertex.

An adjacency matrix is one way in which a graph can be represented. The graph vertices are rows and columns of the matrix and the cell value shows the relationship between the vertices of a graph.

The adjacency list and the adjacency set are alternate ways to represent a graph. Here the connection between the vertices is represented using either a linked list or a set.

Compare the adjacency matrix, adjacency list and the adjacency set in terms of space and time complexity of common operations

Common traversal methods of trees apply to graphs as well. There is an additional wrinkle with graphs, dealing with cycles and with unconnected graphs. Otherwise the algorithms are exactly the same as those we use to traverse trees.

Graph Algorithms

Topological sort is an ordering of vertices in a graph where a vertex comes before every other vertex to which it has outgoing edges? A mouthful? This lecture will make things easy to follow. Topological sort is widely used in real world problems.

Here is the code in Java to implement topological sort.

Shortest Path Algorithms

Graphs with simple edges (directed or undirected) are unweighted graphs. The distance table is an important data structure used to find the shortest path between any two vertices on a graph. This is used in almost every shortest path algorithm.

Visualize the shortest path algorithm using the distance table, step by step.

Shortest path implementation in Java.

So far we only deal with unweighted graphs. Graphs whose edges have a weight associated are widely used to model real world problems (traffic, length of path etc).

A greedy algorithm is one which tries to find the local optimum by looking at what is the next best step at every iteration. It does not look at the overall picture. It's best used for optimization problems where the solution is very hard and we want an approximate answer.

Finding the shortest path in a weighted graph is a greedy algorithm.

Dijkstra's algorithm is a greedy algorithm to find the shortest path in a weighted graph.

The implementation of Dijkstra's algorithm in Java.

A weighted graph can have edge weights which are negative. Dealing with negative weights have some quirks which are dealt with using the Bellman Ford algorithm.

Visualize how the Bellman Ford works to find the shortest path in a graph with negative weighted edges.

If a graph has a negative cycle then it's impossible to find a shortest path as every round of the cycle makes the path shorter!

Implementation Of The Bellman Ford Algorithm

Spanning Tree Algorithms

A minimal spanning tree is a tree which covers all the vertices of of the graph and has the lowest cost. Prim's algorithm is very similar to Dijkstra's shortest path algorithm with a few differences.

The minimal spanning tree is used when we want to connect all vertices at the lowest cost, it's not the shortest path from source to destination.

Let's see how we implement Prim's algorithm in Java.

Kruskal's algorithm is another greedy algorithm to find a minimal spanning tree.

Implementation Of Kruskal's Algorithm

Graph Problems

Given a course list and pre-reqs for every course design a course schedule so pre-reqs are done before the courses.

Find the shortest path in a weighted graph where the number of edges also determine which path is shorter.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers graph algorithms like Dijkstra's, Bellman-Ford, Prim's, and Kruskal's, which are essential for solving optimization problems in software development

Explores adjacency matrices, lists, and sets, which are fundamental data structures for representing graphs and understanding their properties

Taught by an ex-Googler with experience at Google, Microsoft, and Flipkart, which suggests practical insights into real-world applications of graph algorithms

Uses Java for implementation, which is a widely used language in industry and academia, making the code examples directly applicable to many projects

Focuses on algorithms and data structures, which are best learned visually, and the course uses animation to help learners understand these concepts

Is a subset of a larger course, which may mean that learners will need to take the larger course to gain a more comprehensive understanding

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 Byte-Sized-Chunks: Graph Algorithms and Problems in Java with these activities:

Review Basic Data Structures

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Reinforce your understanding of fundamental data structures like linked lists, trees, and hash tables, as graph algorithms often build upon these concepts.

Browse courses on Graphs

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Review the definitions and properties of common data structures.
Implement basic operations (insertion, deletion, search) for each data structure.
Solve simple problems using these data structures.

Create a Graph Algorithm Cheat Sheet

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Consolidate your knowledge by creating a cheat sheet summarizing the key concepts and algorithms covered in the course.

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Review your notes, assignments, and quizzes from the course.
Summarize the key concepts and algorithms in a concise format.
Include pseudocode, time complexity analysis, and use cases for each algorithm.

Review 'Algorithms' by Robert Sedgewick and Kevin Wayne

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Deepen your understanding of graph algorithms by studying a comprehensive algorithms textbook.

View Introduction to Programming in Java: An... on Amazon

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Read the chapters related to graph algorithms.
Study the provided pseudocode and Java implementations.
Work through the exercises at the end of each chapter.

Four other activities

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Create a Blog Post on Dijkstra's Algorithm

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Deepen your understanding of Dijkstra's algorithm by explaining it in your own words in a blog post.

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Research Dijkstra's algorithm and its applications.
Write a clear and concise explanation of the algorithm.
Include examples and diagrams to illustrate the algorithm.
Publish the blog post on a platform like Medium or your own website.

Solve Graph Problems on LeetCode

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Solidify your understanding of graph algorithms by solving a variety of problems on LeetCode.

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Identify graph-related problems on LeetCode.
Implement solutions in Java using the algorithms learned in the course.
Analyze the time and space complexity of your solutions.

Implement a Graph Visualization Tool

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Reinforce your understanding of graph data structures and algorithms by building a tool that visualizes graphs and their traversals.

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Choose a suitable Java GUI library (e.g., Swing, JavaFX).
Implement graph data structures (adjacency matrix, adjacency list).
Implement graph traversal algorithms (BFS, DFS).
Visualize the graph and the traversal process.

Review 'Introduction to Algorithms' by Cormen et al.

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Expand your knowledge of graph algorithms with a rigorous treatment from a classic algorithms textbook.

View Introduction to Algorithms, fourth edition on Amazon

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Read the chapters on graph algorithms.
Study the pseudocode and proofs of correctness.
Attempt the exercises and problems at the end of each chapter.

Career center

Learners who complete Byte-Sized-Chunks: Graph Algorithms and Problems in Java will develop knowledge and skills that may be useful to these careers:

Algorithm Developer

An algorithm developer designs and implements algorithms to solve specific problems, often in areas like search, optimization, or machine learning. This course helps algorithm developers by deepening their understanding of graph algorithms and their applications. Algorithm developers need to be proficient in algorithms such as Dijkstra's, Bellman-Ford, Prim's, and Kruskal's, all of which are covered in this course. Skills in implementing topological sort are also directly applicable to dependency resolution and scheduling problems. With this knowledge, an algorithm developer can better design and optimize algorithms for real-world scenarios and expand their problem-solving toolkit. Someone looking to strengthen their expertise in algorithm design and implementation would find this course useful.

See salaries and explore the career path for Algorithm Developer

Network Engineer

A network engineer designs, implements, and manages computer networks. This course is extremely useful for network engineers, as graph algorithms are fundamental to network routing, network optimization, and network security. Network engineers apply algorithms like Dijkstra's and Bellman-Ford to find the shortest paths between network nodes, ensuring efficient data transmission. Understanding spanning tree algorithms such as Prim's and Kruskal's is crucial for designing robust and fault-tolerant networks. This course helps network engineers understand the theoretical underpinnings of network protocols and to design and troubleshoot network issues more effectively. This course helps network engineers better understand the algorithms used in network design and management.

See salaries and explore the career path for Network Engineer

Software Engineer

A software engineer designs, develops, and tests software applications. This course may be useful for software engineers because it provides a strong foundation in graph algorithms, a crucial area in computer science. Software engineers use graph algorithms to solve various problems, such as network routing, social network analysis, and recommendation systems. This course covers essential algorithms like Dijkstra's, Bellman-Ford, Prim's, and Kruskal's, all of which can be applied in real-world software development scenarios. Moreover, grasping the concept of topological sort and its applications, as taught in this course, prepares a software engineer to tackle scheduling and dependency resolution challenges effectively. Anyone looking to refine their problem-solving skills and broaden their toolkit should consider this course.

See salaries and explore the career path for Software Engineer

Game Developer

A game developer designs and implements video games. This course helps game developers by providing them with the knowledge to implement pathfinding, artificial intelligence, and network multiplayer features. Game developers can use algorithms such as Dijkstra's and A* (an algorithm related to Dijkstra's) for character navigation and AI decision-making. Understanding graph structures also enables the creation of complex game worlds and interactions. This course helps game developers create more engaging and realistic gaming experiences. Anyone looking to enhance their game development skills would find this course useful.

See salaries and explore the career path for Game Developer

Operations Research Analyst

An operations research analyst uses mathematical and analytical techniques to solve complex problems and improve decision-making. Operations research analysts can use graph algorithms to optimize logistics, scheduling, and resource allocation. This course provides the technical foundation for operations research analysts to tackle complex optimization problems and enhance their decision-making capabilities. This course can help operations research analysts model complex problems in a structured way.

See salaries and explore the career path for Operations Research Analyst

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. This course may be useful to machine learning engineers as graph algorithms are relevant to machine learning tasks like recommendation systems, social network analysis, and feature engineering. Understanding and implementing algorithms like Dijkstra's, Bellman-Ford, Prim's, and Kruskal's can enhance a machine learning engineer's ability to solve complex problems related to graph-structured data. Machine learning engineers can use the knowledge gained from this course to build more efficient and effective machine learning models, as well as to better understand the underlying relationships in their data. The practical focus on various graph algorithms helps machine learning engineers translate theory into working code.

See salaries and explore the career path for Machine Learning Engineer

Supply Chain Analyst

A supply chain analyst optimizes the flow of goods, information, and finances across a supply chain. As a supply chain analyst, applying graph algorithms can optimize logistics, manage inventory, and improve efficiency. You can use algorithms like Dijkstra's and Bellman-Ford to find the most cost-effective routes for transporting goods, while spanning tree algorithms help optimize warehouse layouts and distribution networks. This course helps supply chain analysts to model and optimize complex supply chain networks to improve overall performance. This course provides the skills to analyze complex supply chains and make data-driven decisions.

See salaries and explore the career path for Supply Chain Analyst

Data Scientist

A data scientist analyzes large datasets to extract meaningful insights and develop data-driven solutions. For a data scientist, this course may be useful to learn about graph algorithms, which are increasingly important for tasks like social network analysis, fraud detection, and recommendation engines. Understanding algorithms such as Dijkstra's, Prim's, and Kruskal's enables data scientists to efficiently analyze complex relationships within data. The course's focus on practical implementations of these algorithms, along with concepts like topological sort, allows a data scientist to build sophisticated models and algorithms. A data scientist who wants to enhance their skills in network analysis and graph-based modeling may consider this course.

See salaries and explore the career path for Data Scientist

Data Analyst

A data analyst examines datasets to identify trends, patterns, and insights that help organizations make better decisions. Data analysts often work with graph-structured data, especially when analyzing social networks, supply chains, or customer relationships. This course helps data analysts understand and apply graph algorithms to extract valuable insights from complex datasets. Data analysts can use algorithms such as Dijkstra's, Prim's, and Kruskal's to identify key influencers, optimize routes, and uncover hidden relationships. This course provides the technical foundation for data analysts to perform advanced network analysis and provide more impactful recommendations. Any data analyst wishing to expand their analytical skills would benefit from this course.

See salaries and explore the career path for Data Analyst

Research Scientist

A research scientist conducts research to advance knowledge in a specific field, often requiring advanced degrees (such as a master's degree or a doctorate). This course may be useful for research scientists working on graph theory, network science, or related areas. Research scientists can use the algorithms and techniques taught in this course as a foundation for developing novel algorithms and models. Research scientists can apply these algorithms to solve complex problems in various fields, such as social science, biology, and engineering. This course provides the theoretical and practical knowledge needed to conduct cutting-edge research in graph-related fields. Someone wishing to expand their expertise in graph theory would find this course helpful.

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Database Administrator

A database administrator manages and maintains databases, ensuring data integrity, security, and accessibility. This course may be useful for database administrators because understanding graph representations and algorithms helps optimize database queries and data relationships. Database administrators can use knowledge of graph algorithms to design more efficient database schemas, particularly when dealing with complex relationships between entities. This course can help database administrators improve database performance and scalability, especially in applications that rely on graph databases or network analysis. The practical skills learned in this course can be applied directly to database design and optimization.

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Security Analyst

A security analyst protects computer systems and networks from cyber threats. This course may be useful for security analysts by providing them with the knowledge to analyze network traffic, identify malicious activity, and understand the structure of cyber networks. Security analysts can use graph algorithms to detect anomalies, trace attack paths, and visualize network vulnerabilities. This course helps security analysts improve their ability to detect and respond to cyber threats. Someone wishing to expand their cybersecurity knowledge would find this course helpful.

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Web Developer

A web developer designs, codes, and maintains websites and web applications. This course may be useful for web developers building applications that involve social networks, recommendation systems, or complex data relationships. Web developers can utilize graph algorithms to implement features such as friend suggestions, personalized content recommendations, and network visualizations. Understanding concepts like topological sort helps manage dependencies and workflows in web applications. This course enhances a web developer's ability to create dynamic and interactive web experiences. Anyone wishing to broaden their web development toolkit would benefit from this course.

See salaries and explore the career path for Web Developer

Bioinformatician

A bioinformatician analyzes biological data using computational tools and techniques, often requiring an advanced degree. This course may be useful for bioinformaticians because graph algorithms are used in analyzing biological networks, such as protein-protein interaction networks and gene regulatory networks. Bioinformaticians can use graph algorithms to identify key proteins, predict gene functions, and understand complex biological processes. This course provides the technical foundation for bioinformaticians to tackle complex biological problems and can enhance their analytical skills in understanding complex biological systems.

See salaries and explore the career path for Bioinformatician

Geospatial Analyst

A geospatial analyst analyzes geographic data to create maps, perform spatial analysis, and solve location-based problems. This course may be useful for geospatial analysts because graph algorithms are used in routing, network analysis, and resource allocation. Geospatial analysts can use algorithms such as Dijkstra's and Prim's to optimize transportation networks, plan infrastructure projects, and analyze spatial relationships. This course provides the technical foundation for geospatial analysts to tackle complex spatial problems efficiently. Any geospatial analyst wishing to refine their analytical skills would find this course helpful.

See salaries and explore the career path for Geospatial Analyst

Financial Analyst

A financial analyst analyzes financial data, provides investment recommendations, and manages financial risks. This course may be useful for financial analysts, as graph algorithms are used in fraud detection, risk management, and network analysis. A financial analyst can use graph algorithms to identify fraudulent transactions, analyze financial networks, and assess systemic risks. This course provides the technical skills for financial analysts to perform advanced financial analysis and manage complex financial systems, as well as enhance their skills in identifying fraud and systemic risks through network analysis.

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Byte-Sized-Chunks

Graph Algorithms and Problems in Java

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