# Coursera: Algorithmic Toolbox

with  Michael Levin, Daniel M Kane, Alexander S. Kulikov, Pavel Pevzner and Neil Rhodes
The 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).

## Syllabus

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.

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!

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.

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!

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.

Dynamic Programming 2
In this module, we continue practicing implementing dynamic programming solutions.

18 Student
reviews
Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 6 weeks long

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## Reviews for Coursera's Algorithmic Toolbox 3.7 Based on 18 reviews

• 5 stars 50%
• 4 stars 11%
• 3 stars 17%
• 2 star 6%
• 1 stars 17%

Did you take this course? Share your experience with other students.

• 1
5.0 2 years ago
by partially completed this course, spending 8 hours a week on it and found the course difficulty to be hard.
One of the best Computer Science algorithm courses (and hopefully, entire specialization) on Coursera's new platform. Here's why:

- The course supports programming assignments in multiple languages: C, C++, Python, Java. You can implement your algorithms in all 4 languages and learn all of them. They have automatic grader for all 4 languages.

- Your algorithms need to be optimized to pass the assignments, not just creating output correctly. The grader was designed to test you on 3 criteria: Correct Answer, Time Limit and Memory Limit. This course really forces learners to implement the best algorithms possible, not just a working algorithm.

- The materials presented are very well-designed. You can tell that tons of efforts have been put into developing the videos, the slides, the assignments.

- Talking about the assignments, each week there is one problem set that consists of 4 or 5 smaller problems. These problems range from easy (d
One of the best Computer Science algorithm courses (and hopefully, entire specialization) on Coursera's new platform. Here's why:

- The course supports programming assignments in multiple languages: C, C++, Python, Java. You can implement your algorithms in all 4 languages and learn all of them. They have automatic grader for all 4 languages.

- Your algorithms need to be optimized to pass the assignments, not just creating output correctly. The grader was designed to test you on 3 criteria: Correct Answer, Time Limit and Memory Limit. This course really forces learners to implement the best algorithms possible, not just a working algorithm.

- The materials presented are very well-designed. You can tell that tons of efforts have been put into developing the videos, the slides, the assignments.

- Talking about the assignments, each week there is one problem set that consists of 4 or 5 smaller problems. These problems range from easy (discussed in lectures, you only need to write code) - medium (not hard, but you need to design your own algorithm) - advanced (hard, and you'll need to design your own algorithm. They are not trivial. They are really challenging, and you'll spend a lot of time doing them, especially the advanced problems, but you'll learn a lot of things in the process.

- About algorithms itself, this course introduces recursion, naive and efficient algorithms in the first lesson. Next, it teaches greedy algorithms, divide-and-conquer and last but not least: dynamic programming. For each topic, there is one problem set consisting of 4-5 problems ranging from easy to advanced.

- The discussion forums are so lively with people constantly discussing correct solutions, different approaches to the problem, algorithms efficiency, even differences between languages! I have learnt so much from the forum, note that it is just the first week. The discussion thread for Programming assignment 1 alone, in just a few days, there were already 100 posts from everyone. Why? Because the last problem is so challenging, everyone turns to the forum to help each other. At first, I passed the advanced problem, but my algorithm running time was borderline. One fellow classmate suggests a brilliant improvement to my algorithm, and I was able to reduce it to 1% of the original running time! Crazy!

- The instructor is very active in the forum. He helped a lot of people with their problems, very frequently.

The drawback of this course is that it has pay-access (you will not be able to submit assignments without payment, however you can view them). However, I think it is very justifiable. It is very easy to tell that tons of effort, money, time and expertise have been put into making this course possible and it is well-worth the price tag.
44 people found
5.0 2 years ago
partially completed this course.
Awesome course! Strongly recommend for people who want to learn algorithms from the ground up. However, basic programming will not be taught. So if you want to fare easily in this course, you should have basic ideas of programming in C++ Python Java. All the people in the discussion forums all have programming experience, so if you're completely new please don't enroll because if you ask basic questions, it's hard to get an answer because everyone is busy discussing about algorithm efficiency, memory usage, etc.

Strongly recommend!!! Another amazing MOOC from UCSD!!!
27 people found
4.0 2 months ago
by completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
I recommend this course, but it is not without its flaws. It has a lot of instructors and thus the quality of lectures varies a bit.

All of them do at least a passable job(all of them are top notch researchers by the way). The more engaging one, in my opinion, is Dr. Pavel Pevzner, he is very energetic and presents some interesting examples in the bioinformatics context, it's a shame that he teaches very few modules. The least engaging for me was also the one that presents the majority of the modules: Alexander S. Kulikov. The quality of his modules varies a lot. At times he seems like wasn't prepared to record the lecture and just confuses himself in the explanation. But overall, you can learn from it if you make the effort.

There were very few in-video quizzes and it is something that helps me keep on my toes while watching the lectures, so I think they could've added more. The assignment quizzes were not very great either, most of the questions do not prese
I recommend this course, but it is not without its flaws. It has a lot of instructors and thus the quality of lectures varies a bit.

All of them do at least a passable job(all of them are top notch researchers by the way). The more engaging one, in my opinion, is Dr. Pavel Pevzner, he is very energetic and presents some interesting examples in the bioinformatics context, it's a shame that he teaches very few modules. The least engaging for me was also the one that presents the majority of the modules: Alexander S. Kulikov. The quality of his modules varies a lot. At times he seems like wasn't prepared to record the lecture and just confuses himself in the explanation. But overall, you can learn from it if you make the effort.

There were very few in-video quizzes and it is something that helps me keep on my toes while watching the lectures, so I think they could've added more. The assignment quizzes were not very great either, most of the questions do not present a challenge and overall didn't contribute much to my learning experience.

One thing that I like is that the slides are very well-crafted and often besides that, there are more advanced suggested readings.

I recommend the course to anyone that wants to learn about basic programming paradigms because, despite the shortcomings, the course has some spectaculars programming assignments. They resemble very much the questions from ACM-ICPC and judge your code based on correctness, and fitting in the proposed time and space constraints(a lot of allowed languages, but starter codes only for Python, C, C++ and Java). Often the more advanced questions even required some optimizations in the code to pass the bigger test cases within the time limits, so even if your solution is optimal, asymptotically speaking, it requires tweaking the code to get a passing grade.
5.0 a year ago
completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
[+] Great lectures! [+] Constant in-video quizzes to check understanding of the current lecture [-] Really short (typically 3-5 multiple choice questions) and easy weekly quizzes, would love to have a bit more challenging questions in there, or just more examples of different problems that are solved with recently learned algorithm. But you can cover this by doing exercises from recommended textbook(s), so not a big problem [+] Nice and relevant, not too hard programming assignments, with one or two optional advanced problems each week
5 people found
5.0 3 weeks ago
by completed this course, spending 9 hours a week on it and found the course difficulty to be medium.
I am unhappily surprised by the fact that some people rate this course low because it is allegedly poor explained. In my opinion, it is probably the clearest, best explained and best structured course among all algorithmic courses I've ever taken. Lecturers explain difficult things in the simplest terms. Even I was able to understand it, although I have very poor mathematical and programming background. Well, you will need to make some efforts to understand some concepts - but algorithms are hard to study just by default!

In addition, the course is highly adjustable for each student according to their level and/or desire to go deeper into details. it is easy enough to complete 1-3 challenges needed to pass, but you can also try to solve more difficult and interesting assignments.

I also like the way how all the statements and logic transitions are proved - every important point is proved in a very clear and easy to follow manner, you don't need to have a stron
I am unhappily surprised by the fact that some people rate this course low because it is allegedly poor explained. In my opinion, it is probably the clearest, best explained and best structured course among all algorithmic courses I've ever taken. Lecturers explain difficult things in the simplest terms. Even I was able to understand it, although I have very poor mathematical and programming background. Well, you will need to make some efforts to understand some concepts - but algorithms are hard to study just by default!

In addition, the course is highly adjustable for each student according to their level and/or desire to go deeper into details. it is easy enough to complete 1-3 challenges needed to pass, but you can also try to solve more difficult and interesting assignments.

I also like the way how all the statements and logic transitions are proved - every important point is proved in a very clear and easy to follow manner, you don't need to have a strong mathematical background to understand it. In other algorithmic courses, statements are usually proved either using complex mathematical terms or aren't proved at all.

I strongly recommend this course, it is just great!
3.0 10 months ago
by partially completed this course.
I am at the last week of the course. Only problem is that lectures are not self sufficient for the course as you have to learn from other sources too. Accent of the two professors are difficult to understand. Although Programming assignments are challenging and fun to solve. Challenges are the parts from which you can learn a lot.
1.0 a year ago
is taking this course right now.
explanations are very poor

i have to read a lot and search online for other videos and tutorials to understand whats going on

not recommended
3.0 9 months ago
completed this course.
I have completed the first three classes of this series.

Algorithms is not easy to teach. Smart people aren't necessarily good teachers. Only the visiting professor from Russia, Alexander Kulikov is super clear in his thinking and conveys the right way to traverse these complex concepts. Every word he says is relevant and is necessary and meaningful.

Other profs, explain simple or unnecessarily things for a long time and they really need to focus on how to convey the complex concepts in a better way to improve this course. The programming exercises are good, though they could be even better.
5.0 12 months ago
partially completed this course.
This class is awesome with problem sets like you would expect from a top university. You already need to know how to code and to handle input and output. If you are stuck with that, look at the tutorials on hackerrank.
5.0 2 years ago
completed this course.
2 people found
5.0 2 years ago
completed this course.
5.0 2 years ago
partially completed this course.
0 person found
1.0 a year ago
is taking this course right now.
There is a reason why this course is not free and you have to pay upfront to take it. Because, if it was free, most people would drop it after the second week.

The accents are unintelligible. The explanations are poor. The course does not have any meat and bones. In the fifth week, we have barely progressed to dynamic programming.

I can't give lower than 5 stars hence the one star.

Save your time and money and take the Stanford and Princeton courses.
1 person found
1.0 a year ago
partially completed this course.
The level of difficulty and level of explanation vary greatly throughout the course. I really hate that I paid for this course, and would not recommend it to anyone. I have to sit it out, but it is boring and ill-explained.
2 people found
5.0 a year ago
by completed this course.
4.0 2 years ago
by completed this course.
3.0 2 years ago
audited this course.
0 person found