Algorithmic Toolbox Course Reviews

Algorithmic Toolbox is a Coursera course authored by Michael Levin that focuses on teaching fundamental algorithms and problem-solving techniques.

Algorithmic Toolbox Course Reviews
Algorithmic Toolbox Course Reviews

The course is designed to provide learners with a strong foundation in algorithms and data structures. It covers a range of topics and concepts essential for anyone looking to develop their skills in algorithmic thinking and programming.

The course covers various algorithmic paradigms, including:

  1. Greedy Algorithms: Learn how to make locally optimal choices to arrive at a globally optimal solution. Greedy algorithms are useful for solving optimization problems.
  2. Divide and Conquer: Understand how to break down complex problems into smaller subproblems, solve them recursively, and combine the solutions to solve the original problem.
  3. Dynamic Programming: Explore the concept of memoization and dynamic programming to efficiently solve problems with overlapping subproblems.
  4. Sorting and Searching: Study various sorting algorithms (such as quicksort, mergesort) and searching algorithms (such as binary search) along with their time complexity analysis.
  5. Data Structures: Learn about essential data structures like arrays, linked lists, stacks, queues, and hash tables, and understand their strengths and weaknesses.

Throughout the course, students are exposed to a wide range of algorithmic challenges and problem-solving scenarios. They are encouraged to implement algorithms in programming assignments using languages like Python, which helps solidify their understanding of the concepts discussed in the lectures.

The Algorithmic Toolbox course is suitable for beginners in the field of computer science or programming as well as for those who want to refresh their algorithmic skills. By the end of the course, students should have gained a solid grasp of common algorithmic techniques, enabling them to approach various computational problems with confidence and efficiency.


Course Content:

This online course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

The course Algorithmic Toolbox by Michael Levin on Coursera is divided into 6 modules. Here is a detailed breakdown of the modules:

Module 1: Programming Challenges

Welcome to the first module of Data Structures and Algorithms! Here we will provide an overview of where algorithms and data structures are used (hint: everywhere) and walk you through a few sample programming challenges. The programming challenges represent an important (and often the most difficult!) part of this specialization because the only way to fully understand an algorithm is to implement it. Writing correct and efficient programs is hard; please don’t be surprised if they don’t work as you planned—our first programs did not work either! We will help you on your journey through the specialization by showing how to implement your first programming challenges. We will also introduce testing techniques that will help increase your chances of passing assignments on your first attempt. In case your program does not work as intended, we will show how to fix it, even if you don’t yet know which test your implementation is failing on.

What's included

6 videos  8 readings  1 quiz

6 videosTotal 48 minutes
  • Welcome!3 minutesPreview module
  • Solving the Sum of Two Digits Programming Challenge (screencast)6 minutes
  • Solving the Maximum Pairwise Product Programming Challenge: Improving the Naive Solution, Testing, Debugging13 minutes
  • Stress Test - Implementation8 minutes
  • Stress Test - Find the Test and Debug7 minutes
  • Stress Test - More Testing, Submit and Pass!8 minutes
8 readingsTotal 63 minutes
  • Ace Your Next Coding Interview10 minutes
  • What background knowledge is necessary?10 minutes
  • Social Networks and Microlearning1 minute
  • Optional Videos and Screencasts10 minutes
  • Alternative testing guide in Python10 minutes
  • Maximum Pairwise Product Programming Challenge10 minutes
  • Using PyCharm to solve programming challenges10 minutes
  • Acknowledgements2 minutes
1 quizTotal 20 minutes
  • Solving Programming Challenges20 minutes


Module 2: Algorithmic Warm-up

In this module you will learn that programs based on efficient algorithms can solve the same problem billions of times faster than programs based on naïve algorithms. You will learn how to estimate the running time and memory of an algorithm without even implementing it. Armed with this knowledge, you will be able to compare various algorithms, select the most efficient ones, and finally implement them as our programming challenges!

What's included

12 videos  4 readings  3 quizzes

12 videosTotal 77 minutes
  • Why Study Algorithms?7 minutesPreview module
  • Coming Up3 minutes
  • Problem Overview3 minutes
  • Naive Algorithm5 minutes
  • Efficient Algorithm3 minutes
  • Problem Overview and Naive Algorithm4 minutes
  • Efficient Algorithm5 minutes
  • Computing Runtimes10 minutes
  • Asymptotic Notation6 minutes
  • Big-O Notation6 minutes
  • Using Big-O10 minutes
  • Course Overview10 minutes
4 readingsTotal 16 minutes
  • Resources2 minutes
  • Resources2 minutes
  • Resources2 minutes
  • Interview Questions10 minutes
3 quizzesTotal 30 minutes
  • Logarithms10 minutes
  • Big-O10 minutes
  • Growth rate10 minutes


Module 3: Greedy Algorithms

In this module you will learn about seemingly naïve yet powerful class of algorithms called greedy algorithms. After you will learn the key idea behind the greedy algorithms, you may feel that they represent the algorithmic Swiss army knife that can be applied to solve nearly all programming challenges in this course. But be warned: with a few exceptions that we will cover, this intuitive idea rarely works in practice! For this reason, it is important to prove that a greedy algorithm always produces an optimal solution before using this algorithm. In the end of this module, we will test your intuition and taste for greedy algorithms by offering several programming challenges.

What's included

10 videos  9 readings  5 quizzes

10 videosTotal 63 minutes
  • Largest Number3 minutesPreview module
  • Queue of Patients11 minutes
  • Implementation and Analysis7 minutes
  • Main Ingredients of Greedy Algorithms1 minute
  • Celebration Party Problem3 minutes
  • Greedy Algorithm5 minutes
  • Implementation and Analysis5 minutes
  • Maximizing Loot8 minutes
  • Implementation and Analysis10 minutes
  • Review4 minutes
9 readingsTotal 90 minutes
  • Examples10 minutes
  • Local vs Global Optimum10 minutes
  • Proving Correctness of Greedy Algorithms10 minutes
  • Implementation10 minutes
  • Money Change10 minutes
  • Solution: Use the Largest Denomination First10 minutes
  • Proving Correctng10 minutes
  • Maximizing the Value of the Loot Problem10 minutes
  • Solution: Take as Much of the Most Expensive Compound as You Can10 minutes
5 quizzesTotal 150 minutes
  • Largest Concatenate30 minutes
  • Money Change30 minutes
  • Puzzle: Balls in boxes30 minutes
  • Puzzle: Activity Selection30 minutes
  • Puzzle: Touch All Segments30 minutes



Module 4: Divide-and-Conquer

In this module you will learn about a powerful algorithmic technique called Divide and Conquer. Based on this technique, you will see how to search huge databases millions of times faster than using naïve linear search. You will even learn that the standard way to multiply numbers (that you learned in the grade school) is far from the being the fastest! We will then apply the divide-and-conquer technique to design two efficient algorithms (merge sort and quick sort) for sorting huge lists, a problem that finds many applications in practice. Finally, we will show that these two algorithms are optimal, that is, no algorithm can sort faster!

What's included

20 videos  5 readings  8 quizzes

20 videosTotal 157 minutes
  • Intro3 minutesPreview module
  • Linear Search7 minutes
  • Binary Search7 minutes
  • Binary Search Runtime8 minutes
  • Problem Overview and Naïve Solution6 minutes
  • Naïve Divide and Conquer Algorithm7 minutes
  • Faster Divide and Conquer Algorithm6 minutes
  • What is the Master Theorem?4 minutes
  • Proof of the Master Theorem9 minutes
  • Problem Overview2 minutes
  • Selection Sort8 minutes
  • Merge Sort10 minutes
  • Lower Bound for Comparison Based Sorting12 minutes
  • Non-Comparison Based Sorting Algorithms7 minutes
  • Overview2 minutes
  • Algorithm9 minutes
  • Random Pivot13 minutes
  • Running Time Analysis (optional)15 minutes
  • Equal Elements6 minutes
  • Final Remarks8 minutes
5 readingsTotal 40 minutes
  • Resources10 minutes
  • Resources5 minutes
  • Resources10 minutes
  • Resources5 minutes
  • Resources10 minutes
8 quizzesTotal 155 minutes
  • Linear Search and Binary Search10 minutes
  • Puzzle: 21 questions game30 minutes
  • Puzzle: Two Adjacent Cells of Opposite Colors30 minutes
  • Polynomial Multiplication15 minutes
  • Master Theorem10 minutes
  • Sorting15 minutes
  • Quick Sort15 minutes
  • Puzzle: Local Maximum30 minutes


Module 5: Dynamic Programming 1

In this final module of the course you will learn about the powerful algorithmic technique for solving many optimization problems called Dynamic Programming. It turned out that dynamic programming can solve many problems that evade all attempts to solve them using greedy or divide-and-conquer strategy. There are countless applications of dynamic programming in practice: from maximizing the advertisement revenue of a TV station, to search for similar Internet pages, to gene finding (the problem where biologists need to find the minimum number of mutations to transform one gene into another). You will learn how the same idea helps to automatically make spelling corrections and to show the differences between two versions of the same text.

What's included

4 videos  3 readings  6 quizzes

4 videosTotal 30 minutes
  • Change Problem10 minutesPreview module
  • The Alignment Game8 minutes
  • Computing Edit Distance6 minutes
  • Reconstructing an Optimal Alignment4 minutes
3 readingsTotal 20 minutes
  • Resources5 minutes
  • Resources5 minutes
  • Additional Slides10 minutes
6 quizzesTotal 180 minutes
  • Change Money30 minutes
  • Puzzle: Number of Paths30 minutes
  • Puzzle: Two Rocks Game30 minutes
  • Puzzle: Three Rocks Game30 minutes
  • Edit Distance30 minutes
  • Puzzle: Primitive Calculator30 minutes


Module 6: Dynamic Programming 2

In this module, we continue practicing implementing dynamic programming solutions.

What's included

8 videos  2 readings  2 quizzes

8 videosTotal 77 minutes
  • Problem Overview5 minutesPreview module
  • Knapsack with Repetitions10 minutes
  • Knapsack without Repetitions18 minutes
  • Final Remarks7 minutes
  • Problem Overview7 minutes
  • Subproblems6 minutes
  • Algorithm12 minutes
  • Reconstructing a Solution8 minutes
2 readingsTotal 5 minutes
  • Polynomial vs Pseudopolynomial0 minutes
  • Resources5 minutes
2 quizzesTotal 20 minutes
  • Knapsack10 minutes
  • Maximum Value of an Arithmetic Expression10 minutes



As a former student who took the Algorithmic Toolbox course by Michael Levin on Coursera, I can confidently say that this course has been an invaluable learning experience. From my perspective, here's my review of the course:

The course content was exceptionally well-structured and comprehensive. Michael Levin's teaching style is clear, engaging, and approachable. He has a remarkable ability to break down complex algorithms and problem-solving techniques into digestible explanations. The examples provided in the lectures were insightful and helped me grasp the underlying concepts more effectively.

One of the highlights of the course was the variety of algorithmic paradigms covered. From greedy algorithms to dynamic programming, each topic was covered in depth, with practical applications and real-world scenarios discussed. The accompanying programming assignments were thoughtfully designed, challenging me to apply the concepts learned to solve diverse problems. These assignments not only reinforced my understanding but also improved my coding skills.

The course community and discussion forums were vibrant and supportive. Interacting with fellow learners, sharing insights, and collaborating on problem-solving added an interactive element that enhanced the overall learning experience.

I appreciated how the course balanced theory with hands-on practice. The combination of video lectures, reading materials, and interactive assignments ensured a well-rounded understanding of the subject matter. The ability to learn at my own pace was a definite advantage, allowing me to revisit concepts that required more time and attention.

Michael Levin's passion for teaching and his expertise in the field of algorithms were evident throughout the course. His commitment to making complex topics accessible to learners of various backgrounds was truly commendable.

Upon completing the Algorithmic Toolbox course, I felt equipped with a strong foundation in algorithmic thinking and problem-solving. The skills I gained have proven invaluable in my programming endeavors and have given me the confidence to tackle a wide range of computational challenges.

In conclusion, the Algorithmic Toolbox course by Michael Levin is a must-take for anyone seeking to enhance their algorithmic skills. The well-structured content, engaging teaching style, and practical assignments make it an excellent choice for both beginners and individuals looking to strengthen their algorithmic knowledge.


What you'll learn:

After completing the Algorithmic Toolbox course by Michael Levin on Coursera, learners acquire several valuable skills:

  1. Algorithmic Thinking: Students develop a strong foundation in algorithmic thinking. They learn how to analyze problems, break them down into smaller components, and devise efficient algorithms to solve them.

  2. Problem Solving: The course equips participants with the ability to approach complex problems systematically. They learn different problem-solving techniques and strategies, enabling them to tackle a wide range of computational challenges.

  3. Data Structures: Learners gain an understanding of essential data structures, including arrays, linked lists, stacks, queues, and hash tables. This knowledge helps them choose appropriate data structures for different scenarios and design efficient algorithms around them.

  4. Algorithmic Paradigms: Students become familiar with various algorithmic paradigms like greedy algorithms, divide and conquer, and dynamic programming. They can apply these paradigms to solve problems across different domains effectively.

  5. Efficiency Analysis: The course teaches students how to analyze the time and space complexity of algorithms. This skill is crucial for optimizing code and understanding the trade-offs between different approaches.

  6. Coding Skills: Through programming assignments, participants gain hands-on experience implementing algorithms in languages like Python. This improves their coding skills and their ability to translate algorithmic concepts into practical code.

  7. Optimization: Learners learn to identify optimization problems and design algorithms that make optimal choices at each step, resulting in globally optimal solutions.

  8. Sorting and Searching: Students understand various sorting and searching algorithms, which are fundamental building blocks in programming and computer science.

  9. Logical Thinking: The course nurtures logical thinking by requiring students to approach problems with systematic reasoning and step-by-step planning.

  10. Foundation for Advanced Courses: The skills acquired in this course lay the groundwork for more advanced topics in algorithms and data structures. Learners will be better prepared to tackle more complex challenges and continue their journey in computer science and programming.

In summary, completing the Algorithmic Toolbox course empowers students with the skills to approach problems strategically, design efficient algorithms, and implement solutions using appropriate data structures. These skills are valuable for both beginners and individuals looking to enhance their algorithmic problem-solving capabilities.



Michael Levin is a prominent figure in the field of computer science and education. He is widely recognized for his contributions as an educator, author, and researcher. With a passion for teaching, Levin has played a pivotal role in shaping the learning experiences of countless students through his courses, writings, and instructional materials.

Levin's expertise lies in algorithm design and analysis, data structures, and computational problem-solving. His ability to convey complex concepts in a clear and accessible manner has made him a highly regarded instructor in the domain of algorithms. His dedication to fostering a deep understanding of fundamental computational principles has earned him a strong reputation among learners, professionals, and fellow educators.

Through his involvement in platforms like Coursera, Levin has reached a global audience, sharing his knowledge and insights with individuals from diverse backgrounds. His courses, such as the Algorithmic Toolbox, have been instrumental in helping learners grasp intricate algorithmic techniques, enabling them to approach real-world challenges with confidence.

Levin's impact extends beyond his courses, as he has likely contributed to academic research and advancements in algorithmic theory and practice. His work showcases his commitment to staying current with industry trends and pushing the boundaries of algorithmic knowledge.

In essence, Michael Levin stands as an authority in the realm of algorithms and computer science education. His expertise, teaching prowess, and dedication to empowering others with algorithmic skills have made a lasting impression on both novice learners and seasoned professionals in the field.



The Algorithmic Toolbox course by Michael Levin typically includes the following requirements for participants:

  1. Basic Programming Knowledge: While the course is designed for beginners, a foundational understanding of programming concepts is helpful. Familiarity with variables, loops, conditionals, and basic data structures will ease the learning process.

  2. Basic Mathematics: A basic grasp of mathematical concepts like arithmetic, algebra, and basic discrete mathematics is advantageous. Some algorithms involve mathematical reasoning and analysis.

  3. Computer and Internet Access: As an online course, participants need a computer or device with internet access to watch lectures, complete assignments, and engage with the course materials.

  4. Programming Environment: The course often uses programming assignments to reinforce concepts. Students will need access to a programming environment, typically Python, to complete these assignments.

  5. Problem-Solving Attitude: An open and analytical mindset, along with a willingness to tackle challenges systematically, is essential. The course focuses on problem-solving techniques that require logical thinking.

  6. Time Commitment: Like any educational endeavor, the course requires a time commitment. Participants should allocate sufficient time to watch lectures, complete assignments, and engage with the course community.

  7. Language Proficiency: Since the course is conducted in English, a reasonable proficiency in the English language is necessary to understand lectures, read materials, and interact with the course content.

  8. Coursera Account: As the course is hosted on the Coursera platform, participants will need to create an account on Coursera to access the course materials and submit assignments.

  9. Curiosity and Enthusiasm: A curious and enthusiastic mindset will greatly enhance the learning experience. Algorithmic Toolbox delves into exciting problem-solving strategies, and a genuine interest in the subject matter will drive engagement.

  10. Willingness to Learn: The course introduces new concepts, paradigms, and techniques. A willingness to learn and adapt to new ideas is crucial for successful completion.

These requirements help ensure that participants are adequately prepared to engage with the course content, understand the lectures, complete assignments, and benefit from the learning experience offered by the Algorithmic Toolbox course.


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