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Субтитры: Английский, Испанский

Чему вы научитесь

  • Check

    Essential algorithmic techniques

  • Check

    Design efficient algorithms

  • Check

    Practice solving algorithmic interview problems

  • Check

    Implement efficient and reliable solutions

Приобретаемые навыки

Dynamic ProgrammingDebuggingSoftware TestingAlgorithmsComputer Programming

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Субтитры: Английский, Испанский

Программа курса: что вы изучите

5 ч. на завершение

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.

6 видео ((всего 48 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
6 видео
Solving the Sum of Two Digits Programming Challenge (screencast)6мин
Solving the Maximum Pairwise Product Programming Challenge: Improving the Naive Solution, Testing, Debugging13мин
Stress Test - Implementation8мин
Stress Test - Find the Test and Debug7мин
Stress Test - More Testing, Submit and Pass!8мин
5 материала для самостоятельного изучения
Companion MOOCBook10мин
What background knowledge is necessary?10мин
Optional Videos and Screencasts10мин
Maximum Pairwise Product Programming Challenge10мин
1 практическое упражнение
Solving Programming Challenges20мин
5 ч. на завершение

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!

12 видео ((всего 77 мин.)), 3 материалов для самостоятельного изучения, 4 тестов
12 видео
Coming Up3мин
Problem Overview3мин
Naive Algorithm5мин
Efficient Algorithm3мин
Problem Overview and Naive Algorithm4мин
Efficient Algorithm5мин
Computing Runtimes10мин
Asymptotic Notation6мин
Big-O Notation6мин
Using Big-O10мин
Course Overview10мин
3 материала для самостоятельного изучения
3 практического упражнения
Growth rate10мин
4 ч. на завершение

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.

10 видео ((всего 56 мин.)), 1 материал для самостоятельного изучения, 3 тестов
10 видео
Car Fueling7мин
Car Fueling - Implementation and Analysis9мин
Main Ingredients of Greedy Algorithms2мин
Celebration Party Problem6мин
Efficient Algorithm for Grouping Children5мин
Analysis and Implementation of the Efficient Algorithm5мин
Long Hike6мин
Fractional Knapsack - Implementation, Analysis and Optimization6мин
Review of Greedy Algorithms2мин
1 материал для самостоятельного изучения
2 практического упражнения
Greedy Algorithms10мин
Fractional Knapsack10мин
7 ч. на завершение


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!

20 видео ((всего 157 мин.)), 5 материалов для самостоятельного изучения, 6 тестов
20 видео
Linear Search7мин
Binary Search7мин
Binary Search Runtime8мин
Problem Overview and Naïve Solution6мин
Naïve Divide and Conquer Algorithm7мин
Faster Divide and Conquer Algorithm6мин
What is the Master Theorem?4мин
Proof of the Master Theorem9мин
Problem Overview2мин
Selection Sort8мин
Merge Sort10мин
Lower Bound for Comparison Based Sorting12мин
Non-Comparison Based Sorting Algorithms7мин
Random Pivot13мин
Running Time Analysis (optional)15мин
Equal Elements6мин
Final Remarks8мин
5 материала для самостоятельного изучения
5 практического упражнения
Linear Search and Binary Search10мин
Polynomial Multiplication15мин
Master Theorem10мин
Quick Sort15мин
Рецензии: 967Chevron Right


начал новую карьеру, пройдя эти курсы


получил значимые преимущества в карьере благодаря этому курсу


стал больше зарабатывать или получил повышение

Лучшие отзывы о курсе Algorithmic Toolbox

автор: SGJan 20th 2017

I liked the fact that the algorithms are not just the introductory searching and sorting algorithms. The assignments are fairly difficult (I have decent scripting experience), but not impossibly so.

автор: MMSep 29th 2017

good course, I like the fact you can use a lot of languages for you programming exercises, the content is really helpful, I would like to have more indications from the grading system to save time.



Alexander S. Kulikov

Visiting Professor
Department of Computer Science and Engineering

Michael Levin

Computer Science

Neil Rhodes

Adjunct Faculty
Computer Science and Engineering

Pavel Pevzner

Department of Computer Science and Engineering

Daniel M Kane

Assistant Professor
Department of Computer Science and Engineering / Department of Mathematics

О Калифорнийский университет в Сан-Диего

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О Национальный исследовательский университет "Высшая школа экономики"

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more. Learn more on www.hse.ru...

О специализации ''Структуры и алгоритмы данных'

This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problems and will implement about 100 algorithmic coding problems in a programming language of your choice. No other online course in Algorithms even comes close to offering you a wealth of programming challenges that you may face at your next job interview. To prepare you, we invested over 3000 hours into designing our challenges as an alternative to multiple choice questions that you usually find in MOOCs. Sorry, we do not believe in multiple choice questions when it comes to learning algorithms...or anything else in computer science! For each algorithm you develop and implement, we designed multiple tests to check its correctness and running time — you will have to debug your programs without even knowing what these tests are! It may sound difficult, but we believe it is the only way to truly understand how the algorithms work and to master the art of programming. The specialization contains two real-world projects: Big Networks and Genome Assembly. You will analyze both road networks and social networks and will learn how to compute the shortest route between New York and San Francisco (1000 times faster than the standard shortest path algorithms!) Afterwards, you will learn how to assemble genomes from millions of short fragments of DNA and how assembly algorithms fuel recent developments in personalized medicine....
Структуры и алгоритмы данных

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