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Florian Juengermann
You have some programming experience and now want to take your skills to a new level? Learning algorithms and mastering algorithmic thinking is by far the most effective way of rapidly improving as a developer and problems solver.That's why I will teach you the most interesting and useful algorithms in this course. (I intentionally skipped sorting algorithms as they are so over-discussed and rarely need to be implemented by yourself).For each algorithm or topic, I give a concise explanation, example and implementation outline. Then it's your turn to apply the new learned algorithm to solve real problems. For that, I hand-picked tasks from programming websites. When you struggle with an issue and need help, I answer every question and provide personal feedback for your problems.Sign up now and begin a new chapter in your programming world.
Register for this course and see more details by visiting:
OpenCourser.com/course/ejtaai/algorithms
What's inside
Learning objectives
- Find efficient solutions to algorithmic programming problems.
- Analyse why code runs slowly and significantly improve run time.
- Apply most important algorithms to solve real problems.
- Find efficient solutions to algorithmic programming problems.
- Analyse why code runs slowly and significantly improve run time.
- Apply most important algorithms to solve real problems.
Syllabus
Setup and Introduction
Course Introduction
Quick Introduction to Runtime Analysis and big-O notation
Quick Introduction to C++ for Competitive Programmers
Read more
Syllabus
Setup and Introduction
Course Introduction
Quick Introduction to Runtime Analysis and big-O notation
Quick Introduction to C++ for Competitive Programmers
Read more
Confirm that you understood the different graph search methods.
Are you a shortest path expert?
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Covers runtime analysis and Big-O notation, which are essential for optimizing code and understanding algorithm efficiency
Explores graph algorithms like Depth-First Search, Breadth-First Search, and Dijkstra's, which are frequently encountered in technical interviews
Teaches dynamic programming techniques, including memorization and application to problems like the Knapsack Problem, which are useful for optimization
Includes a quick introduction to C++ for competitive programmers, which may require learners to learn a new language
Requires learners to apply newly learned algorithms to solve real problems on programming websites, which may require additional accounts
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Reviews summary
Algorithms for interviews and competitive programming
According to learners, this course offers a clear and concise introduction to essential algorithms for anyone preparing for job interviews or looking to improve their competitive programming skills. Students found the explanations of core concepts like Graphs (DFS, BFS, Dijkstra, Bellman-Ford, Floyd-Warshall) and Dynamic Programming to be particularly helpful. The inclusion of practical coding problems is highlighted as a major strength, allowing learners to apply theory immediately. While many appreciate it as a solid foundation, some mention it assumes basic programming knowledge and might not delve into sufficient depth for very advanced competitors.
May lack depth for advanced learners.
"As someone with some competitive programming experience, I found it a good review, but it didn't introduce many advanced techniques."
"It covers the fundamentals well, but don't expect very complex algorithms or highly optimized competitive programming tricks."
"Good for beginners to intermediate, but advanced users might need more."
"Provides a solid intro, but further study is needed for expert level."
Hands-on problems reinforce learning.
"The coding tasks are well-selected and provide necessary practice to solidify the learned algorithms."
"Solving the problems after each section was crucial for understanding how to implement the concepts."
"I appreciated the practical nature of the exercises rather than just theoretical discussions."
"The problems challenge you to apply the algorithms effectively."
Excellent preparation for technical interviews.
"This course is highly relevant for anyone preparing for coding interviews, covering essential topics frequently asked."
"The problems chosen feel very similar to those on LeetCode and other interview platforms."
"It gave me the confidence to tackle algorithmic questions in my job search."
"Focused on practical applications useful for interviews."
Explanations of core algorithms are clear.
"The instructor explains complex algorithms like Dijkstra and Dynamic Programming in a very clear and understandable way."
"I found the explanations for graph algorithms, especially DFS and BFS, very intuitive and easy to follow."
"The dynamic programming module finally made sense to me after struggling with it in other resources."
"The explanations are concise yet comprehensive enough to grasp the core concepts."
Requires some prior programming basics.
"While it says for those with programming experience, a basic understanding of data structures like trees and arrays is definitely helpful."
"Beginners might struggle if they don't have a solid foundation in at least one programming language (preferably C++ for the examples)."
"It moves at a decent pace, assuming you're familiar with basic coding syntax and logic."
"Might be too fast-paced without pre-existing knowledge."
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 for Job Interviews and Competitive Programming with these
activities:
Review Big-O Notation
Show steps
Reviewing Big-O notation will help you better understand the runtime analysis of algorithms covered in the course.
Browse courses on
Big O Notation
Show steps
-
Read articles and watch videos explaining Big-O notation.
-
Practice determining the Big-O complexity of simple code snippets.
-
Review examples of common time complexities like O(1), O(log n), O(n), O(n log n), and O(n^2).
Review Introduction to Algorithms
Show steps
Referencing 'Introduction to Algorithms' will provide a deeper understanding of the algorithms discussed in the course.
View
Introduction to Algorithms, fourth edition
on Amazon
Show steps
-
Read the chapters related to graph algorithms and dynamic programming.
-
Work through the examples and exercises in the book.
-
Compare the book's explanations with the course materials.
Cracking the Coding Interview
Show steps
Reviewing 'Cracking the Coding Interview' will provide additional practice problems and interview tips.
View
Cracking the Coding Interview: 189 Programming...
on Amazon
Show steps
-
Read the chapters related to graphs, dynamic programming, and other relevant algorithms.
-
Solve the practice problems in the book.
-
Compare your solutions with the book's solutions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Pair Programming on Algorithm Problems
Show steps
Participating in pair programming sessions will allow you to learn from your peers and improve your problem-solving skills.
Show steps
-
Find a partner who is also taking the course or interested in algorithms.
-
Choose a set of algorithm problems to work on together.
-
Take turns coding and explaining your approach to each problem.
Solve Graph Problems on LeetCode
Show steps
Practicing graph problems on LeetCode will reinforce your understanding of graph algorithms and improve your problem-solving skills.
Show steps
-
Select a set of graph problems on LeetCode (e.g., BFS, DFS, shortest paths).
-
Attempt to solve each problem independently.
-
Analyze the solutions and optimize your code for efficiency.
Implement a Shortest Path Visualizer
Show steps
Implementing a shortest path visualizer will solidify your understanding of shortest path algorithms and provide a practical application of the concepts.
Show steps
-
Choose a programming language and a GUI library.
-
Implement Dijkstra's algorithm or Bellman-Ford algorithm.
-
Create a visual representation of a graph and highlight the shortest path.
-
Allow users to input their own graphs and visualize the results.
Create a Dynamic Programming Tutorial
Show steps
Creating a tutorial on dynamic programming will deepen your understanding of the topic and help you explain it to others.
Show steps
-
Choose a specific dynamic programming problem (e.g., Knapsack, Fibonacci).
-
Explain the problem and the dynamic programming approach to solve it.
-
Provide code examples and step-by-step explanations.
-
Create visuals to illustrate the dynamic programming process.
Review Big-O Notation
Show steps
Reviewing Big-O notation will help you better understand the runtime analysis of algorithms covered in the course.
Browse courses on
Big O Notation
Show steps
Show steps
Show steps
- Read articles and watch videos explaining Big-O notation.
- Practice determining the Big-O complexity of simple code snippets.
- Review examples of common time complexities like O(1), O(log n), O(n), O(n log n), and O(n^2).
Review Introduction to Algorithms
Show steps
Referencing 'Introduction to Algorithms' will provide a deeper understanding of the algorithms discussed in the course.
View
Introduction to Algorithms, fourth edition
on Amazon
Show steps
Show steps
Show steps
- Read the chapters related to graph algorithms and dynamic programming.
- Work through the examples and exercises in the book.
- Compare the book's explanations with the course materials.
Cracking the Coding Interview
Show steps
Reviewing 'Cracking the Coding Interview' will provide additional practice problems and interview tips.
View
Cracking the Coding Interview: 189 Programming...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters related to graphs, dynamic programming, and other relevant algorithms.
- Solve the practice problems in the book.
- Compare your solutions with the book's solutions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Pair Programming on Algorithm Problems
Show steps
Participating in pair programming sessions will allow you to learn from your peers and improve your problem-solving skills.
Show steps
Show steps
Show steps
- Find a partner who is also taking the course or interested in algorithms.
- Choose a set of algorithm problems to work on together.
- Take turns coding and explaining your approach to each problem.
Solve Graph Problems on LeetCode
Show steps
Practicing graph problems on LeetCode will reinforce your understanding of graph algorithms and improve your problem-solving skills.
Show steps
Show steps
Show steps
- Select a set of graph problems on LeetCode (e.g., BFS, DFS, shortest paths).
- Attempt to solve each problem independently.
- Analyze the solutions and optimize your code for efficiency.
Implement a Shortest Path Visualizer
Show steps
Implementing a shortest path visualizer will solidify your understanding of shortest path algorithms and provide a practical application of the concepts.
Show steps
Show steps
Show steps
- Choose a programming language and a GUI library.
- Implement Dijkstra's algorithm or Bellman-Ford algorithm.
- Create a visual representation of a graph and highlight the shortest path.
- Allow users to input their own graphs and visualize the results.
Create a Dynamic Programming Tutorial
Show steps
Creating a tutorial on dynamic programming will deepen your understanding of the topic and help you explain it to others.
Show steps
Show steps
Show steps
- Choose a specific dynamic programming problem (e.g., Knapsack, Fibonacci).
- Explain the problem and the dynamic programming approach to solve it.
- Provide code examples and step-by-step explanations.
- Create visuals to illustrate the dynamic programming process.
Career center
Learners who complete Algorithms for Job Interviews and Competitive Programming will develop knowledge and skills
that may be useful to these careers:
Competitive Programmer
Competitive Programmer
Competitive programmers participate in programming contests that involve solving algorithmic problems under time constraints. This course is specifically designed to prepare you for competitive programming by covering essential algorithms and providing hands-on practice with real problems. The focus on runtime analysis and optimization directly addresses the challenges faced in competitive programming. The course provides personalized feedback, and helps competitive programmers rapidly improve.
Algorithm Developer
Algorithm Developer
An algorithm developer researches, designs, and implements algorithms for various applications such as search engines, recommendation systems, or data analysis tools. The course provides a practical introduction to essential algorithms and algorithmic problem solving. The hands-on exercises and personal feedback are valuable for honing your algorithm design skills. If you want to become an algorithm developer, this course is a great way to master algorithmic thinking.
Software Engineer
Software Engineer
A software engineer designs and develops software applications. This often involves writing code, debugging, and working with other engineers to create functional and efficient software. The course helps build a foundation in algorithmic thinking, which is essential for optimizing code and solving complex problems in software development. Specifically, the sections on dynamic programming and graph algorithms are very useful for writing efficient software. This is a role one can be prepared for through a targeted focus on algorithms.
Data Scientist
Data Scientist
Data scientists use statistical methods, machine learning, and algorithmic techniques to analyze large datasets and extract meaningful insights. This course helps data scientists develop the necessary algorithmic skills to efficiently process and analyze data. The sections on dynamic programming and graph algorithms are particularly relevant for developing sophisticated data analysis pipelines. The focus on problem solving will also prove useful when working with messy real-world data.
Robotics Engineer
Robotics Engineer
Robotics engineers design, develop, and test robots. They use algorithms for robot control, path planning, and perception. The course helps robotics engineers improve their algorithm design skills, which are essential for creating intelligent and efficient robots. The sections on graph algorithms and shortest paths are useful for robot navigation and task planning. If you wish to be a robotics engineer, this course helps you learn to handle complex problems.
Machine Learning Engineer
Machine Learning Engineer
A machine learning engineer develops and implements machine learning models. Machine learning engineers need a strong grasp of algorithms. This course helps build this foundation, as it covers important algorithms and algorithmic thinking. The dynamic programming and graph algorithms sections are particularly useful for optimizing machine learning models and feature engineering. The course will help machine learning engineers solve complicated problems.
Research Scientist
Research Scientist
Research scientists conduct research in various fields, often involving the development and application of algorithms. Many research positions require a master's degree or doctorate. This course helps research scientists develop their algorithmic problem-solving skills, which are essential for conducting innovative research. The course's material on algorithm analysis and optimization may be useful for those interested in becoming research scientists.
Game Developer
Game Developer
Game developers create video games for various platforms. They may use algorithms for game logic, artificial intelligence, pathfinding, and resource management, among other tasks. The course may be useful for game developers, because it teaches algorithms and algorithmic thinking, which can improve a game developer's performance. Dijkstra's Algorithm and Dynamic Programming, for example, can be applied to pathfinding and AI problems, respectively.
Quantitative Analyst
Quantitative Analyst
A quantitative analyst, often working in the finance industry, uses mathematical and statistical models to analyze financial markets and develop trading strategies. The course may be useful for quantitative analysts as it can help them develop efficient algorithms for data analysis and optimization. Topics such as dynamic programming can be applied to portfolio optimization and risk management. Those who wish to become quantitative analysts may find this course helpful.
Cybersecurity Analyst
Cybersecurity Analyst
Cybersecurity analysts protect computer systems and networks from cyber threats. They use algorithms for intrusion detection, malware analysis, and cryptography. The course may be useful for cybersecurity analysts, because it focuses on algorithmic problem solving. The graph algorithms and dynamic programming sections might be applicable to network analysis and cryptography. This course may help those who want to become cybersecurity analysts.
Technical Lead
Technical Lead
A technical lead is responsible for guiding a team of developers. This requires a strong understanding of algorithms and software architecture. The course helps technical leaders improve their technical skills and their ability to guide their teams in solving complex problems. The course's material on algorithm analysis and optimization can be applied to software architecture. Those who want to become technical leads may find this course helpful.
Database Administrator
Database Administrator
Database administrators are responsible for maintaining and optimizing databases. This often involves designing efficient data structures, query optimization, and performance tuning. This course helps database administrators develop the algorithmic skills necessary for optimizing database performance. Knowing about data structures, such as those used in graph algorithms, can make a database administrator more effective.
Technology Consultant
Technology Consultant
Technology consultants advise organizations on how to use technology to improve their business processes and achieve their goals. This often involves analyzing complex business problems and developing technology solutions. The course helps technology consultants improve their problem-solving skills and their understanding of algorithms. The course's focus on runtime analysis directly benefits those who wish to become technology consultants.
Project Manager
Project Manager
Project managers plan, execute, and close projects. This requires an understanding of the technical aspects of the project. The course may be useful for project managers, because it helps them understand the algorithms that are being used in the project. The course can also help project managers to communicate with the developers on their team. This course may be useful for one who wishes to become a project manager, and who seeks to improve their communication skills.
Faculty
Faculty
Faculty members at colleges and universities teach and conduct research. Many faculty positions require a master's degree or doctorate. This course may be useful for faculty, as it provides a deeper understanding of algorithms. With this course, professors can teach programming with greater insight into the material. This course may be valuable for one who wishes to become faculty.
For more career information including salaries, visit:
OpenCourser.com/course/ejtaai/algorithms
Reading list
We've selected two 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 for Job Interviews and Competitive Programming.
Comprehensive textbook on algorithms, covering a wide range of topics with detailed explanations and rigorous analysis. It's a standard reference in computer science education and provides a strong foundation for understanding and designing algorithms. While it covers more than just interview-specific algorithms, it's an invaluable resource for in-depth study and a great reference. It is commonly used as a textbook at academic institutions.
Focuses specifically on preparing for coding interviews, with a strong emphasis on data structures and algorithms. It provides a practical approach with numerous example problems and solutions, making it ideal for interview preparation. It directly addresses the course's goal of improving algorithmic problem-solving skills for job interviews. This book is commonly used by industry professionals.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ejtaai/algorithms
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