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Robert Sedgewick
and
Kevin Wayne
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
Read more
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
Read more
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.
All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material.
This course does not offer a certificate upon completion.
Register for this course and see more details by visiting:
OpenCourser.com/course/lptfuo/algorithms
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What's inside
Syllabus
Introduction
Welcome to Algorithms, Part II.
Undirected Graphs
We define an undirected graph API and consider the adjacency-matrix and adjacency-lists representations. We introduce two classic algorithms for searching a graph—depth-first search and breadth-first search. We also consider the problem of computing connected components and conclude with related problems and applications.
Read more
Syllabus
Introduction
Welcome to Algorithms, Part II.
Undirected Graphs
We define an undirected graph API and consider the adjacency-matrix and adjacency-lists representations. We introduce two classic algorithms for searching a graph—depth-first search and breadth-first search. We also consider the problem of computing connected components and conclude with related problems and applications.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Taught by Robert Sedgewick and Kevin Wayne, who have written an acclaimed textbook on algorithms
Covers topics in graph theory and string processing, making it relevant for programmers in various fields, including computer science, data science, and software engineering
Develops problem-solving skills and deepens understanding of algorithms and data structures, which are fundamental concepts in computer science
Covers topics that are frequently used in industry, such as graph algorithms, string processing, and data compression
Uses Java implementation and examples throughout the course, making it accessible to learners with a background in Java programming
Requires students to have a basic understanding of data structures, algorithms, and Java programming
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Reviews summary
Rigorous algorithms study with java focus
According to learners, Algorithms, Part II offers a deep and rigorous dive into advanced algorithmic topics like graphs, strings, network flow, and intractability. Many find the course highly challenging yet rewarding, particularly appreciating the strong theoretical foundation provided by the lectures from Princeton University. The course emphasizes Java implementations, which some students find practical and helpful for understanding concepts, while others note it requires solid Java prerequisites. Students frequently mention the need for significant time commitment due to the difficult homework assignments. The lack of a certificate is a neutral observation noted by some.
No completion certificate offered.
"Note that this course does not offer a certificate upon completion."
"It's a pity there's no certificate, as it's a high-quality course."
"The lack of a certificate didn't bother me, I took it for the knowledge."
Strong prerequisite reliance.
"Taking Part I beforehand is absolutely crucial; this course assumes that knowledge."
"If you haven't taken Part I or have a weak background, this course will be extremely difficult."
"It follows logically from Part I, expanding on the foundational concepts."
"Make sure your understanding of Part I material is solid before attempting Part II."
Requires strong Java proficiency.
"Working through the Java code examples provided in the course was very beneficial for me."
"You definitely need a solid understanding of Java before starting this course."
"While I appreciated the practical coding, the Java requirement might be a barrier for some."
"Implementing the algorithms in Java helped solidify my learning."
Clear and well-structured explanations.
"The instructor does an amazing job explaining complex ideas clearly."
"Lectures are the highlight of the course; they are concise and easy to follow."
"I found the way the concepts were presented in the videos to be highly effective."
"The video content makes even the most difficult topics approachable."
Deep dive into advanced algorithms.
"The material is incredibly thorough, covering complex graph and string algorithms in detail."
"This course is a serious exploration of algorithms, requiring dedicated study and engagement."
"I found the depth of the topics covered to be far beyond typical introductory courses."
"It provides a very strong theoretical background for advanced computer science."
"The reduction lectures and intractability section were particularly insightful."
Homework requires significant effort.
"The programming assignments are very difficult and time-consuming, but ultimately helpful."
"Be prepared to spend many hours on the weekly problem sets; they are not trivial."
"Some assignments felt overwhelming at times, really pushing my understanding."
"Completing the homework is essential for truly grasping the concepts, despite the difficulty."
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 Algorithms, Part II with these
activities:
Create a comprehensive study guide
Show steps
Enhance your learning by organizing and summarizing course materials into a concise and accessible study guide.
Browse courses on
Study Guide
Show steps
-
Gather all relevant course materials
-
Extract key concepts, algorithms, and examples
-
Organize and structure the information in a logical flow
Organize weekly study groups
Show steps
Foster collaboration and enhance your understanding by discussing course concepts with peers.
Show steps
-
Connect with classmates and form a study group
-
Schedule regular meetings
-
Review course material, discuss problems, and share insights
Read textbook Algorithms, Fourth Edition
Show steps
Strengthen your foundation in algorithms and data structures, deepening your understanding of concepts covered in the course.
View
Algorithms
on Amazon
Show steps
-
Read textbook chapters 1-7
-
Complete corresponding practice exercises
-
Attend office hours or discussion forums to clarify concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on advanced graph algorithms
Show steps
Expand your knowledge by exploring specialized graph algorithms beyond the scope of the course.
Browse courses on
Graph Algorithms
Show steps
-
Identify relevant graph algorithm tutorials
-
Follow the tutorials and implement the algorithms
-
Experiment with different graph inputs and analyze outputs
Solve coding challenges on LeetCode
Show steps
Improve your problem-solving skills and reinforce your understanding of algorithms and data structures through practical application.
Browse courses on
Data Structures
Show steps
-
Choose problems from LeetCode related to course topics
-
Implement solutions in Java
-
Review solutions and optimize code
Write blog posts summarizing course concepts
Show steps
Enhance your understanding by explaining concepts in your own words, solidifying your grasp of key ideas and improving your communication skills.
Browse courses on
Algorithms
Show steps
-
Identify a specific concept or algorithm
-
Research and gather information
-
Write a clear and concise blog post
-
Share your blog post and engage with readers
Build a data visualization tool
Show steps
Demonstrate your skills by creating a tool that visually represents relationships and patterns in data, enhancing your understanding of data structures.
Browse courses on
Data Visualization
Show steps
-
Define the project scope and goals
-
Research and choose appropriate data visualization techniques
-
Design and implement the tool using Java
-
Test and refine the tool
Contribute to open-source projects related to algorithms
Show steps
Gain practical experience and contribute to the community by participating in open-source projects that utilize algorithms.
Browse courses on
Open Source
Show steps
-
Identify open-source projects related to algorithms
-
Review project documentation and contribute to discussions
-
Implement or improve features related to algorithms
Create a comprehensive study guide
Show steps
Enhance your learning by organizing and summarizing course materials into a concise and accessible study guide.
Browse courses on
Study Guide
Show steps
Show steps
Show steps
- Gather all relevant course materials
- Extract key concepts, algorithms, and examples
- Organize and structure the information in a logical flow
Organize weekly study groups
Show steps
Foster collaboration and enhance your understanding by discussing course concepts with peers.
Show steps
Show steps
Show steps
- Connect with classmates and form a study group
- Schedule regular meetings
- Review course material, discuss problems, and share insights
Read textbook Algorithms, Fourth Edition
Show steps
Strengthen your foundation in algorithms and data structures, deepening your understanding of concepts covered in the course.
View
Algorithms
on Amazon
Show steps
Show steps
Show steps
- Read textbook chapters 1-7
- Complete corresponding practice exercises
- Attend office hours or discussion forums to clarify concepts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on advanced graph algorithms
Show steps
Expand your knowledge by exploring specialized graph algorithms beyond the scope of the course.
Browse courses on
Graph Algorithms
Show steps
Show steps
Show steps
- Identify relevant graph algorithm tutorials
- Follow the tutorials and implement the algorithms
- Experiment with different graph inputs and analyze outputs
Solve coding challenges on LeetCode
Show steps
Improve your problem-solving skills and reinforce your understanding of algorithms and data structures through practical application.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Choose problems from LeetCode related to course topics
- Implement solutions in Java
- Review solutions and optimize code
Write blog posts summarizing course concepts
Show steps
Enhance your understanding by explaining concepts in your own words, solidifying your grasp of key ideas and improving your communication skills.
Browse courses on
Algorithms
Show steps
Show steps
Show steps
- Identify a specific concept or algorithm
- Research and gather information
- Write a clear and concise blog post
- Share your blog post and engage with readers
Build a data visualization tool
Show steps
Demonstrate your skills by creating a tool that visually represents relationships and patterns in data, enhancing your understanding of data structures.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Define the project scope and goals
- Research and choose appropriate data visualization techniques
- Design and implement the tool using Java
- Test and refine the tool
Contribute to open-source projects related to algorithms
Show steps
Gain practical experience and contribute to the community by participating in open-source projects that utilize algorithms.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source projects related to algorithms
- Review project documentation and contribute to discussions
- Implement or improve features related to algorithms
Career center
Learners who complete Algorithms, Part II will develop knowledge and skills
that may be useful to these careers:
Algorithm Engineer
Algorithm Engineer
Algorithm Engineers design and develop algorithms to solve complex problems in various domains such as computer science, finance, and healthcare. Familiarity with efficient algorithms and data structures is essential for Algorithm Engineers, as it enables them to develop high-performance algorithms for real-world applications. This course provides a strong theoretical foundation in algorithm design and analysis, as well as practical experience in implementing efficient algorithms in Java.
Computational Biology Researcher
Computational Biology Researcher
Computational Biology Researchers create and improve algorithms and software tools to analyze biological data. Usually, this data has a large volume, and to manage such data, familiarity with efficient algorithms such as minimum spanning trees, graph traversal algorithms, and dynamic programming algorithms taught in the course is crucial. Also, the course's introduction to concepts such as data structures, graph theory, and string processing can help researchers gain a deep understanding of how to develop and analyze efficient algorithms for biological data analysis.
Data Scientist
Data Scientist
Data Scientists collect, manage, and analyze large datasets to extract meaningful insights and help organizations make data-driven decisions. Knowledge of efficient algorithms and data structures covered in this course is highly valuable for Data Scientists. The course also emphasizes performance analysis of Java implementations, a skill that is in high demand for data science roles.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Familiarity with efficient algorithms and data structures is a core requirement for Software Engineers, as it enables them to develop efficient and scalable software solutions. The course's coverage of graph algorithms, string processing algorithms, and optimization algorithms provides a strong foundation for Software Engineers to tackle complex software development challenges.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. Familiarity with efficient algorithms and data structures is crucial for Machine Learning Engineers, as it enables them to develop efficient and scalable machine learning models. This course provides a strong foundation in algorithm design, implementation, and performance analysis, which can enhance a Machine Learning Engineer's ability to build and deploy high-performance machine learning solutions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Familiarity with efficient algorithms and data structures is crucial for Quantitative Analysts, as it enables them to develop and analyze complex financial models efficiently. The course's coverage of graph algorithms, optimization algorithms, and string processing algorithms provides a solid foundation for Quantitative Analysts to succeed in their roles.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific disciplines, including computer science, biology, and physics. Familiarity with efficient algorithms and data structures is beneficial for Research Scientists, as it enables them to develop and analyze complex algorithms for scientific research. This course provides a strong foundation in algorithm design, implementation, and performance analysis, which can enhance a Research Scientist's ability to conduct groundbreaking research.
Front-End Developer
Front-End Developer
Front-End Developers are responsible for the user interface and user experience of websites and applications. Familiarity with efficient algorithms and data structures is crucial for optimizing the performance and responsiveness of web applications. This course, with its focus on Java implementations, can help Front-End Developers build a solid foundation in algorithm design and implementation, leading to more efficient and user-friendly web applications.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain database systems to store and manage data. Familiarity with efficient algorithms and data structures is beneficial for Database Administrators, as it enables them to optimize database performance and ensure data integrity. This course provides a solid foundation in algorithms, data structures, and performance analysis, which can enhance a Database Administrator's ability to manage complex database systems.
Consultant
Consultant
Consultants provide expert advice and solutions to businesses and organizations. Familiarity with efficient algorithms and data structures is valuable for Consultants, as it enables them to analyze complex business problems and develop data-driven solutions. This course provides a strong foundation in algorithm design, implementation, and performance analysis, which can enhance a Consultant's ability to deliver effective solutions to clients.
Systems Administrator
Systems Administrator
Systems Administrators manage and maintain computer systems and networks. Familiarity with efficient algorithms and data structures is beneficial for Systems Administrators, as it enables them to optimize system performance and troubleshoot complex issues. This course provides a foundation in algorithms, data structures, and performance analysis, which can enhance a Systems Administrator's ability to manage and maintain robust and efficient systems.
Business Analyst
Business Analyst
Business Analysts identify and analyze business needs and develop solutions to improve organizational performance. Familiarity with efficient algorithms and data structures can be beneficial for Business Analysts, as it enables them to understand and analyze complex business processes and develop data-driven recommendations. The course provides a foundation in algorithms, data structures, and performance analysis, which can enhance a Business Analyst's ability to contribute to data-driven decision-making within organizations.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation, such as user manuals and technical reports. Familiarity with efficient algorithms and data structures can benefit Technical Writers, as it enables them to understand and explain complex technical concepts in a clear and concise manner. This course provides a foundation in algorithms, data structures, and performance analysis, which can enhance a Technical Writer's ability to communicate technical information effectively.
Product Manager
Product Manager
Product Managers define and manage the development and launch of products. Familiarity with efficient algorithms and data structures can be beneficial for Product Managers, as it enables them to understand the technical aspects of product development and make informed decisions about product design. This course provides a foundation in algorithms, data structures, and performance analysis, which can enhance a Product Manager's ability to lead successful product development initiatives.
User Experience Designer
User Experience Designer
User Experience Designers create and evaluate user interfaces for websites and applications. Familiarity with efficient algorithms and data structures can be beneficial for User Experience Designers, as it enables them to understand the performance implications of different design choices. This course provides a foundation in algorithms, data structures, and performance analysis, which can enhance a User Experience Designer's ability to create user-friendly and efficient interfaces.
For more career information including salaries, visit:
OpenCourser.com/course/lptfuo/algorithms
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Algorithms, Part II.
Save
This is the textbook for the Algorithms, Fourth Edition course. It provides a comprehensive overview of the essential algorithms and data structures that all programmers should know. It valuable resource for both beginners and experienced programmers alike.
This classic textbook on algorithms that is used at many universities around the world. It provides a comprehensive overview of the essential algorithms and data structures that all programmers should know. It valuable resource for both beginners and experienced programmers alike.
Provides a practical guide to designing and implementing efficient algorithms. It valuable resource for both beginners and experienced programmers alike.
Provides a comprehensive overview of the essential algorithms and data structures that all programmers should know. It valuable resource for both beginners and experienced programmers alike.
Provides a comprehensive overview of the essential algorithms and data structures that all programmers should know. It valuable resource for both beginners and experienced programmers alike.
Provides a comprehensive overview of the essential algorithms and data structures that are used in competitive programming. It valuable resource for both beginners and experienced programmers alike.
Provides a comprehensive collection of programming challenges that are used in competitive programming. It valuable resource for both beginners and experienced programmers alike.
Provides a comprehensive overview of the essential algorithms and data structures that are used in combinatorial optimization. It valuable resource for both beginners and experienced programmers alike.
Provides a comprehensive overview of the essential algorithms and data structures that are used in linear programming and network flows. It valuable resource for both beginners and experienced programmers alike.
Save
Provides a comprehensive overview of the essential algorithms and data structures that are used in integer programming. It valuable resource for both beginners and experienced programmers alike.
Save
Provides a comprehensive overview of the essential algorithms and data structures that are used in graph theory. It valuable resource for both beginners and experienced programmers alike.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lptfuo/algorithms
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Effort
6 weeks of study, 6–10 hours per week.
Level
Intermediate
Via
Coursera
Institution
Princeton University
Instructors
Robert Sedgewick
Kevin Wayne
Language
English
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