University of California San Diego

Algorithmic Toolbox

This course is part of Data Structures and Algorithms Specialization

Taught in English

Some content may not be translated

Neil Rhodes
Daniel M Kane
Michael Levin

Instructors: Neil Rhodes

515,609 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.6

(12,372 reviews)

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91%

Intermediate level
Some related experience required
40 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Essential algorithmic techniques

  • Design efficient algorithms

  • Practice solving algorithmic interview problems

  • Implement efficient and reliable solutions

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Assessments

25 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(12,372 reviews)

|

91%

Intermediate level
Some related experience required
40 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Data Structures and Algorithms Specialization
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There are 6 modules in this course

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 videos8 readings1 quiz2 programming assignments

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 videos4 readings3 quizzes1 programming assignment1 ungraded lab

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 videos9 readings5 quizzes1 programming assignment

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 videos5 readings8 quizzes1 programming assignment

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 videos2 readings6 quizzes1 programming assignment

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

What's included

8 videos2 readings2 quizzes1 programming assignment

Instructors

Instructor ratings
4.4 (2,184 ratings)
Neil Rhodes
University of California San Diego
7 Courses679,094 learners
Daniel M Kane
University of California San Diego
5 Courses661,778 learners
Michael Levin
University of California San Diego
7 Courses698,938 learners

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