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⇱ Algorithms and Problem Solving with Advanced Techniques | Coursera

Algorithms and Problem Solving with Advanced Techniques

Algorithms and Problem Solving with Advanced Techniques

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn how to solve complex problems using dynamic programming techniques.

  • Understand greedy algorithms and their application in optimization problems.

  • Master graph algorithms, including BFS, DFS, and shortest path techniques.

  • Gain expertise in bitwise operations to solve optimization and number-based problems.

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Recently updated!

May 2026

Assessments

7 assignments

Taught in English

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This course is part of the Data Structures & Algorithms in JavaScript Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course

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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will master advanced problem-solving techniques and learn to approach algorithmic challenges with a structured mindset. By covering key techniques like dynamic programming, greedy algorithms, and graph theory, this course will equip you with the skills to solve complex problems with confidence. You'll learn how to break down large problems into smaller subproblems and optimize your solutions for efficiency and scalability. The course starts with an in-depth exploration of dynamic programming, where you will implement various problems and learn how to select the most effective strategies for solving them. Then, you'll move into greedy algorithms and their applications, followed by a deep dive into graph algorithms. The course covers graph traversal methods like BFS and DFS, as well as more complex topics like Dijkstra's algorithm, Bellman-Ford, and minimum spanning trees. Each section is accompanied by practical problems to reinforce your learning. As you progress, you'll develop critical thinking skills and a deep understanding of algorithmic principles. This course encourages hands-on problem-solving, with each problem designed to stretch your ability to approach problems from multiple angles. You'll gain practical experience in coding and problem-solving, ensuring you're well-prepared for real-world technical challenges. The course is ideal for those who already have basic programming knowledge and want to deepen their understanding of algorithms and problem-solving techniques. Whether you’re preparing for technical interviews or looking to enhance your algorithmic thinking, this course is tailored to boost your skill set. It is especially suitable for computer science enthusiasts, developers, and those aspiring to excel in technical fields. By the end of the course, you will be able to apply dynamic programming, greedy algorithms, graph traversal methods, and backtracking to solve complex problems, optimize algorithms, and enhance your problem-solving efficiency.

In this module, we will introduce you to dynamic programming as a technique to break complex problems into simpler subproblems. You will learn how to apply both top-down and bottom-up approaches to solve various challenges, optimizing solutions using previously computed results.

What's included

17 videos2 readings1 assignment

17 videos•Total 193 minutes
  • Introduction to Dynamic Programming•12 minutes
  • Dynamic Programming Implementation•4 minutes
  • Top Down vs Bottom Up•9 minutes
  • Practice Problem 1 - Climbing Stairs•10 minutes
  • Practice Problem 2 - Jump Game•14 minutes
  • Practice Problem 3 - Coin Change•13 minutes
  • Practice Problem 4 - Target Sum•14 minutes
  • Practice Problem 5 - Longest Increasing Subsequence•13 minutes
  • Practice Problem 6 - Longest Common Subsequence•12 minutes
  • Practice Problem 7 - House Robber•14 minutes
  • Practice Problem 8 - House Robber II•8 minutes
  • Practice Problem 9 - 0-1 Matrix•11 minutes
  • Practice Problem 10 - Integer Replacement•9 minutes
  • Practice Problem 11 - Decode Ways•16 minutes
  • Practice Problem 12 - Partition Equals Subset Sum•14 minutes
  • Practice Problem 13 - Longest Palindromic Substring•11 minutes
  • Practice Problem 14 - Min Cost Climbing Stairs•8 minutes
2 readings•Total 20 minutes
  • Introduction to the Course 'Algorithms and Problem Solving with Advanced Techniques'•10 minutes
  • Full Specialization Resource•10 minutes
1 assignment•Total 15 minutes
  • Dynamic Programming - Assessment•15 minutes

In this module, we will explore the concept of greedy algorithms, a class of algorithms that make locally optimal choices at each step. You will learn to identify when greedy strategies can provide optimal or near-optimal solutions to a problem.

What's included

1 video1 assignment

1 video•Total 13 minutes
  • Introduction to Greedy Algorithms•13 minutes
1 assignment•Total 15 minutes
  • Greedy Algorithms - Assessment•15 minutes

In this module, we will dive into the world of graphs, examining their various types and structures. You will learn graph traversal techniques such as BFS and DFS and explore advanced algorithms like Dijkstra’s and Bellman-Ford for shortest paths, as well as Prim’s for minimum spanning trees.

What's included

41 videos1 assignment

41 videos•Total 274 minutes
  • What are Graphs?•6 minutes
  • Directed vs Undirected Graphs•5 minutes
  • Weighted vs Unweighted Graphs•5 minutes
  • Terms of Graphs Part 1•4 minutes
  • Types of Graphs Part 1•4 minutes
  • Types of Graphs Part 2•8 minutes
  • Implementing Graphs Part 1•6 minutes
  • Implementing Graphs Part 2•8 minutes
  • Implementing Graphs Part 3•4 minutes
  • Adjacency Matrix Demonstration•8 minutes
  • Adjacency List Implementation Demonstration•5 minutes
  • Introduction to Traversals•7 minutes
  • BFS Working•9 minutes
  • BFS Implementation•9 minutes
  • BFS Property #1•8 minutes
  • BFS over Binary Weighted Graphs•7 minutes
  • Introduction to DFS•7 minutes
  • DFS Iterative Implementation•6 minutes
  • DFS Recursive Implementation•4 minutes
  • DFS Important Properties•5 minutes
  • Cycle Detection Part 1•9 minutes
  • Cycle Detection Part 2•4 minutes
  • Cycle Detection Part 3•4 minutes
  • Cycle Detection Implementation•7 minutes
  • What is Topological Sorting?•4 minutes
  • Topological Sorting Example 1•4 minutes
  • Single Source Shortest Path Algorithm•4 minutes
  • Djikstra's Algorithm•7 minutes
  • Djikstra's Algorithm Implementation•14 minutes
  • Introduction to Bellman-Ford Algorithm•4 minutes
  • Bellman-Ford Algorithm Working•7 minutes
  • Bellman-Ford Algorithm Implementation•6 minutes
  • Introduction to Minimum Spanning Tree•3 minutes
  • Prim's Algorithm•5 minutes
  • Practice Problem 1 - Course Schedule•10 minutes
  • Practice Problem 2 - Number of Islands•12 minutes
  • Practice Problem 3 - Find the Town Judge•9 minutes
  • Practice Problem 4 - Surrounded Regions•13 minutes
  • Practice Problem 5 - Number of Enclaves•9 minutes
  • Practice Problem 6 - Network Delay Time•7 minutes
  • Practice Problem 7 - Flood Fill•6 minutes
1 assignment•Total 15 minutes
  • Graphs - Assessment•15 minutes

In this module, we will cover the powerful backtracking technique, enabling you to explore all potential solutions to problems by undoing steps when necessary. Through hands-on examples like N Queens and Combination Sum, you will develop the skills to apply backtracking in algorithm design.

What's included

5 videos1 assignment

5 videos•Total 54 minutes
  • What is Backtracking?•14 minutes
  • Backtracking Example 1•8 minutes
  • Practice Problem 1 - Subsets•8 minutes
  • Practice Problem 2 - Combination Sum•12 minutes
  • Practice Problem 3 - N Queens•11 minutes
1 assignment•Total 15 minutes
  • Backtracking - Assessment•15 minutes

In this module, we will introduce you to bitwise operations, which allow you to manipulate numbers at the bit level. You will learn how to leverage bit magic to solve problems efficiently, including detecting duplicates, reversing integers, and counting bits.

What's included

10 videos1 reading3 assignments

10 videos•Total 79 minutes
  • Introduction to Bitwise Operators•5 minutes
  • Common Bitwise Operators•7 minutes
  • Practice Problem 1 - Single Number•6 minutes
  • Practice Problem 2 - Counting Bits•10 minutes
  • Practice Problem 3 - Sum of Two Integers•8 minutes
  • Practice Problem 4 - Missing Numbers•5 minutes
  • Practice Problem 5 - Divide Two Integers•12 minutes
  • Practice Problem 6 - Reverse Integer•7 minutes
  • Practice Problem 7 - Number of 1 Bits•7 minutes
  • Practice Problem 8 - Find the Duplicate Number•12 minutes
1 reading•Total 10 minutes
  • Conclusion to the Course ' Algorithms and Problem Solving with Advanced Techniques'•10 minutes
3 assignments•Total 90 minutes
  • Bitmagic - Assessment•15 minutes
  • Full Course Assessment•60 minutes
  • Full Course Practice Assessment•15 minutes

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Frequently asked questions

This course covers advanced algorithmic techniques such as dynamic programming, greedy algorithms, graph theory, backtracking, and bit manipulation. These techniques are fundamental for solving complex problems efficiently and are widely applicable in fields like software development, data science, and competitive programming. By mastering these methods, learners can optimize solutions for real-world challenges and improve their problem-solving skills.

"Algorithms and Problem Solving with Advanced Techniques" introduces learners to essential algorithmic strategies. The course emphasizes dynamic programming, greedy algorithms, graph algorithms, backtracking, and bit manipulation. It provides both theoretical understanding and practical implementation of these methods, allowing learners to solve advanced computational problems effectively.

After completing this course, you will be equipped with the knowledge and skills to solve complex algorithmic problems. You will understand how to apply dynamic programming, graph traversal, backtracking, and bitwise techniques to optimize solutions for real-world scenarios. This will enhance your problem-solving capabilities, especially for coding interviews and competitive programming.

To get the most out of this course, you should have a basic understanding of programming concepts such as variables, loops, and arrays. Familiarity with simple algorithms and data structures like sorting and searching will also be helpful, but is not mandatory. The course will cover advanced topics in depth, so prior experience with basic algorithmic techniques is recommended but not required.

This course is ideal for anyone looking to deepen their understanding of algorithms and improve their problem-solving skills. It is especially useful for software developers, aspiring engineers, and students preparing for coding interviews or programming competitions. If you're interested in mastering advanced techniques and tackling complex computational problems, this course is for you.

The course consists of approximately 10 hours of video content, making it a relatively short and intensive program. Depending on your pace and how much time you spend on practice problems, it can be completed in a few days to a week.

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,