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Vincent Gripon,
Patrick Meyer,
Nicolas Farrugia,
Carlos Eduardo Rosar Kos Lassance,
and
Ghouti Boukli Hacene
Algorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.
Learning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won't just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you'll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.
Read more
Algorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.
Learning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won't just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you'll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.
Read more
Algorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.
Learning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won't just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you'll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.
To be specific, you’ll have to work out a route faster than your opponent through a maze while picking up objects.
Each week, you will learn new material to improve your artificial intelligence in order to beat your opponent. This structure means that as a learner, you’ll confront each abstract notion with a real-world problem.
We’ll go over data-structures, basic and advanced algorithms for graph theory, complexity/accuracy trade-offs, and even combinatorial game theory.
This course has received financial support from the Patrick and Lina Drahi Foundation.
Register for this course and see more details by visiting:
OpenCourser.com/course/c0rcpc/advanced
What's inside
Learning objectives
- Ways to express a computational problem (such as pathfinding) using graph theory
- How to choose the appropriate algorithm to solve the given computational problem
- How to code the algorithmic solution in python
- Methods for evaluating the proposed solution in terms of its complexity (amount of resources, scalability) or performance (accuracy, latency)
- Ways to express a computational problem (such as pathfinding) using graph theory
- How to choose the appropriate algorithm to solve the given computational problem
- How to code the algorithmic solution in python
- Methods for evaluating the proposed solution in terms of its complexity (amount of resources, scalability) or performance (accuracy, latency)
Syllabus
Week 1:Fundamentals of Graph Theory, Problem Solving, Good Programming PracticesWeek 2: Graph Traversal, Routing, Queuing StructuresWeek 3:Shortest Paths, Min-Heaps, Algorithmic ComplexityWeek 4:NP-Completeness, Traveling Salesman Problem, BacktrackingWeek 5:Heuristics, Greedy Approaches, Accuracy/Complexity tradeoffWeek 6:Combinatorial Game Theory, Winning Strategies
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data structures, graph theory algorithms, and combinatorial game theory, which are essential knowledge for developers, data scientists, and analysts
Taught by Vincent Gripon, Patrick Meyer, Nicolas Farrugia, Carlos Eduardo Rosar Kos Lassance, and Ghouti Boukli Hacene, who are known for their work in algorithms, data structures, and graph theory
Explores graph theory and algorithms in a practical setting by challenging learners to develop a faster route through a maze
Covers advanced concepts like NP-Completeness, Traveling Salesman Problem, and combinatorial game theory, which are valuable for computer science students and professionals
Requires learners to have a basic understanding of programming and data structures, which may pose a barrier for beginners
Emphasizes problem-solving and algorithmic thinking, which are transferable skills that can benefit learners in various fields
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Reviews summary
Course is highly recommended
Learners who reviewed this course were highly satisfied with the learning experience they received, highly recommending it to other learners. They especially praised the practical, hands-on approach of the course and the quality of the assignments.
This course is very well made.
"Your review helps other learners like you discover great courses."
"Only review the course if you have taken or started taking this course."
The course takes a very practical approach.
The assignments in this course are very engaging.
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 Advanced Algorithmics and Graph Theory with Python with these
activities:
Read 'Introduction to Algorithms' by Cormen, Leiserson, Rivest, and Stein
Show steps
Supplement your understanding of algorithms and data structures through a comprehensive review of this classic textbook.
View
Introduction to Algorithms, fourth edition
on Amazon
Show steps
-
Purchase or borrow the book
-
Read and understand the chapters relevant to the course
-
Take notes and highlight important concepts
-
Complete the exercises at the end of each chapter
Practice dijkstra's algorithm on leetcode
Show steps
Refresh your understanding of the Dijkstra's algorithm and its applications in finding the shortest path in a graph.
Browse courses on
Dijkstra's Algorithm
Show steps
-
Visit leetcode.com and search for 'Dijkstra's Algorithm'
-
Choose a problem and read the description carefully
-
Implement the algorithm in your preferred programming language
-
Submit your solution and review the results
Organize a study group with classmates
Show steps
Foster collaboration and reinforce your understanding by engaging in regular study sessions with peers.
Show steps
-
Identify a group of classmates with similar interests
-
Schedule regular study sessions
-
Discuss course materials, solve problems together
-
Provide feedback and support to each other
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow the 'Graph Algorithms' tutorial on HackerRank
Show steps
Reinforce your knowledge of graph algorithms by working through interactive tutorials and solving practice problems.
Browse courses on
Graph Algorithms
Show steps
-
Create an account on HackerRank
-
Enroll in the 'Graph Algorithms' tutorial
-
Complete the tutorials and practice problems
-
Review the solutions and discuss with the community
Build a graph database using Neo4j
Show steps
Apply the concepts of graph theory by building a graph database using Neo4j to enhance your understanding of data relationships.
Show steps
-
Install Neo4j on your local machine
-
Create a new graph database
-
Define your data model and create nodes and relationships
-
Write queries to retrieve and manipulate data
-
Deploy your graph database to the cloud
Solve coding challenges on Codeforces
Show steps
Enhance your problem-solving and coding skills by attempting coding challenges related to algorithms and data structures.
Browse courses on
Coding Challenges
Show steps
-
Create an account on Codeforces
-
Select a contest or practice problem
-
Read the problem statement carefully
-
Develop an algorithm and code your solution
-
Submit your solution and analyze the results
Attend a workshop on graph algorithms
Show steps
Deepen your understanding of graph algorithms by attending a dedicated workshop led by experts in the field.
Browse courses on
Graph Algorithms
Show steps
-
Identify a relevant workshop
-
Register for the workshop
-
Attend the workshop and actively participate
-
Take notes and ask questions
Read 'Introduction to Algorithms' by Cormen, Leiserson, Rivest, and Stein
Show steps
Supplement your understanding of algorithms and data structures through a comprehensive review of this classic textbook.
View
Introduction to Algorithms, fourth edition
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book
- Read and understand the chapters relevant to the course
- Take notes and highlight important concepts
- Complete the exercises at the end of each chapter
Practice dijkstra's algorithm on leetcode
Show steps
Refresh your understanding of the Dijkstra's algorithm and its applications in finding the shortest path in a graph.
Browse courses on
Dijkstra's Algorithm
Show steps
Show steps
Show steps
- Visit leetcode.com and search for 'Dijkstra's Algorithm'
- Choose a problem and read the description carefully
- Implement the algorithm in your preferred programming language
- Submit your solution and review the results
Organize a study group with classmates
Show steps
Foster collaboration and reinforce your understanding by engaging in regular study sessions with peers.
Show steps
Show steps
Show steps
- Identify a group of classmates with similar interests
- Schedule regular study sessions
- Discuss course materials, solve problems together
- Provide feedback and support to each other
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow the 'Graph Algorithms' tutorial on HackerRank
Show steps
Reinforce your knowledge of graph algorithms by working through interactive tutorials and solving practice problems.
Browse courses on
Graph Algorithms
Show steps
Show steps
Show steps
- Create an account on HackerRank
- Enroll in the 'Graph Algorithms' tutorial
- Complete the tutorials and practice problems
- Review the solutions and discuss with the community
Build a graph database using Neo4j
Show steps
Apply the concepts of graph theory by building a graph database using Neo4j to enhance your understanding of data relationships.
Show steps
Show steps
Show steps
- Install Neo4j on your local machine
- Create a new graph database
- Define your data model and create nodes and relationships
- Write queries to retrieve and manipulate data
- Deploy your graph database to the cloud
Solve coding challenges on Codeforces
Show steps
Enhance your problem-solving and coding skills by attempting coding challenges related to algorithms and data structures.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Create an account on Codeforces
- Select a contest or practice problem
- Read the problem statement carefully
- Develop an algorithm and code your solution
- Submit your solution and analyze the results
Attend a workshop on graph algorithms
Show steps
Deepen your understanding of graph algorithms by attending a dedicated workshop led by experts in the field.
Browse courses on
Graph Algorithms
Show steps
Show steps
Show steps
- Identify a relevant workshop
- Register for the workshop
- Attend the workshop and actively participate
- Take notes and ask questions
Career center
Learners who complete Advanced Algorithmics and Graph Theory with Python will develop knowledge and skills
that may be useful to these careers:
Algorithm Engineer
Algorithm Engineer
Algorithm Engineers specialize in designing and analyzing algorithms to solve complex computational problems. The concepts you learn in this course, such as graph traversal and shortest paths, are fundamental to algorithm engineering. By developing your algorithm design and analysis skills, you will gain a competitive edge in this field.
Research Scientist
Research Scientist
Research Scientists in computer science conduct research in various areas, including algorithms and graph theory. This course will help you develop the theoretical and practical skills necessary for conducting research in these areas. The emphasis on problem-solving and critical thinking will prepare you for the challenges of scientific research.
Data Scientist
Data Scientist
Data Scientists use data to extract insights and solve business problems. This course will help you build a strong foundation in graph theory and algorithms, which are essential for developing data analysis and machine learning models. The emphasis on heuristics and greedy approaches will help you design efficient algorithms for large-scale data processing.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve real-world problems. This course will provide you with a solid foundation in graph theory and algorithms, which are essential for developing efficient and scalable machine learning algorithms. The hands-on experience you gain will help you apply graph-based techniques to solve complex problems in machine learning.
Software Engineer
Software Engineer
As a Software Engineer, you will design, develop, and maintain software systems. This course will provide you with a solid foundation in graph theory and algorithms, which are essential for developing efficient and scalable software solutions. The emphasis on problem-solving and good programming practices will help you write high-quality, maintainable code.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical models and algorithms to solve complex decision-making problems in various industries. This course will provide you with a solid foundation in graph theory and algorithms, which are essential for developing efficient and effective operations research models. The emphasis on heuristics and greedy approaches will help you design algorithms that are scalable and provide practical solutions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The course's coverage of graph theory and optimization techniques will provide you with the tools and techniques to develop robust financial models. The emphasis on complexity and performance evaluation will help you ensure your models are efficient and reliable.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop algorithms and systems to understand and generate human language. This course will provide you with a strong foundation in graph theory and algorithms, which are essential for developing efficient and accurate natural language processing algorithms. The emphasis on complexity and performance evaluation will help you design algorithms that are scalable and meet real-time constraints.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop algorithms and systems to analyze and interpret visual data. This course will provide you with a strong foundation in graph theory and algorithms, which are essential for developing efficient and accurate computer vision algorithms. The emphasis on complexity and performance evaluation will help you design algorithms that are scalable and meet real-time constraints.
Financial Analyst
Financial Analyst
Financial Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course will provide you with a solid foundation in graph theory and algorithms, which are essential for developing robust financial models. The emphasis on complexity and performance evaluation will help you ensure your models are efficient and reliable.
Data Analyst
Data Analyst
A Data Analyst collects, processes, and interprets data to extract meaningful insights and patterns. In this role, you can leverage your knowledge of graph theory and algorithms to develop efficient data processing and analysis methods. The course's focus on complexity and performance evaluation will equip you with the skills to optimize data analysis algorithms for scalability and accuracy.
Game Developer
Game Developer
Game Developers design and develop video games. The concepts you learn in this course, such as pathfinding and combinatorial game theory, are essential for creating engaging and challenging games. By developing your problem-solving and algorithmic skills, you will gain a valuable edge in this competitive field.
Business Analyst
Business Analyst
Business Analysts use data and analysis to help organizations make informed decisions. This course will provide you with a solid foundation in graph theory and algorithms, which are essential for developing efficient and effective data analysis methods. The emphasis on heuristics and greedy approaches will help you design algorithms that are scalable and provide practical insights.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course may be useful for developing your problem-solving and decision-making skills, which are essential for successful project management. The emphasis on complexity and performance evaluation will help you assess and manage project risks and constraints effectively.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation, such as user manuals and training materials. This course may be useful for developing your communication and problem-solving skills, which are essential for effective technical writing. The emphasis on good programming practices will help you write clear and concise technical documentation.
For more career information including salaries, visit:
OpenCourser.com/course/c0rcpc/advanced
Reading list
We've selected nine 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
Advanced Algorithmics and Graph Theory with Python.
A classic work on combinatorial game theory, providing detailed analysis of a wide range of games. Serves as a valuable resource for both mathematicians and game enthusiasts.
A comprehensive handbook covering a wide range of graph theory topics, from basic concepts to advanced research areas. Serves as an invaluable reference for researchers and practitioners.
Classic textbook on algorithms, providing a comprehensive overview of the field and its applications. Serves as a valuable reference for students and professionals alike.
Python-oriented treatment of algorithms and data structures, providing practical examples and hands-on exercises. Useful for students and professionals seeking to apply algorithmic concepts in Python.
Provides an overview of complex networks, covering their structure, dynamics, and applications in various fields. Suitable for graduate students and researchers seeking an introduction to this rapidly growing area.
A concise and accessible introduction to graph theory, suitable for undergraduate students with limited mathematical background. Provides a solid foundation for further study in the field.
Provides an in-depth treatment of algorithms on trees and graphs, focusing on their design, analysis, and applications. Suitable as a textbook or reference for graduate students and researchers.
A collection of graph theory problems and solutions, covering a wide range of topics. Serves as a valuable resource for students seeking practice and deeper understanding.
Covers advanced topics in combinatorial optimization, including NP-completeness, approximation algorithms, and combinatorial game theory. Serves as a valuable resource for graduate students and researchers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c0rcpc/advanced
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Effort
2 to 4 hours per week for 6 weeks
Level
Intermediate
Via
edX
Institution
IMT
Instructors
Vincent Gripon
Patrick Meyer
Nicolas Farrugia
Carlos Eduardo Rosar Kos Lassance
Ghouti Boukli Hacene
Language
English
Transcript
English
Good to know
Develops data structures, graph theory algorithms, and combinatorial game theory, which are essential knowledge for developers, data scientists, and analysts
Taught by Vincent Gripon, Patrick Meyer, Nicolas Farrugia, Carlos Eduardo Rosar Kos Lassance, and Ghouti Boukli Hacene, who are known for their work in algorithms, data structures, and graph theory
Explores graph theory and algorithms in a practical setting by challenging learners to develop a faster route through a maze
Covers advanced concepts like NP-Completeness, Traveling Salesman Problem, and combinatorial game theory, which are valuable for computer science students and professionals
Requires learners to have a basic understanding of programming and data structures, which may pose a barrier for beginners
Emphasizes problem-solving and algorithmic thinking, which are transferable skills that can benefit learners in various fields
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