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# Algorithmic Design and Techniques

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## Overview

In this course, part of the Algorithms and Data Structures MicroMasters program, you will learn basic algorithmic techniques and ideas for computational problems, which arise in practical applications such as sorting and searching, divide and conquer, greedy algorithms and dynamic programming.

This course will cover theories, including:

• 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

Module 1: Welcome
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.

Module 2: Introduction
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!

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.

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!

Modules 5 and 6: Dynamic Programming
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.

#### Taught by

Daniel Kane , Alexander S. Kulikov , Michael Levin , Pavel Pevzner and Neil Rhodes

## Reviews for edX's Algorithmic Design and Techniques 3.0 Based on 2 reviews

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• 1
Anonymous
2.0 a year ago
completed this course.
This course is a lot more work than its authors claim, and if you don't have a solid background in mathematics, you're likely to find those aspects of the syllabus poorly explained. The instructors have strong accents, are prone to labouring some simple points and glossing over others, and aren't particularly engaging, save for one or two. They're right that the opportunity to practice implementing algorithms is very valuable. But some of the code they give you is slapdash and hard to test, and don't expect anyone to help you when you get stuck. The forums are a dead zone. No one replies.
M M
4.0 3 weeks ago
by completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
The good:

- Plenty of programming assignments and rigorous testing of student submissions.

- Clear instructions for the graded assignments.

- Lectures are mostly clear and well explained and provide useful downloadable slides.

What could be better:

- The forums are inactive, don't expect help if you are struggling with the material. Most of the posts were many months old and during my time there (2 weeks) there were at most a dozen new comments.