Introduction

The basic idea of a data structure is to store data in a way that meets the needs of your particular application. You might be inclined to store a particular kind of data in one giant array, but it would be rather time consuming to locate a specific value if you had a significant number and depth of items. So you need to look to other options.

Depending on the application, there are a batch of other basic data structures available to help you out. The differences between them typically have to do with trade-offs between how long it takes to first populate the structure, how long it takes to add or find elements, and how large the structure is in memory.

We’ll save the specifics of data structures for more computer-science-oriented courses, but this introduction should again expand your toolbox slightly so you can identify and solve certain problems where plain old Arrays, Hashes and Sets don’t quite cut it. New structures and strategies will be particularly relevant, for instance, when you’re trying to search through a large batch of data for a particular value or plan out a strategy several moves in advance.

You’ve already had a brief introduction to algorithms over some of the other lessons and you even got to write your own Merge Sort algorithm in the last project. You’ll find that sorting algorithms are quite common. Another major area for algorithms is in search, where milliseconds count. When you’re searching through enormous troves of data, the quality of your search algorithm is incredibly important. Traversing a data tree looking for a particular element is a related problem that’s common in data intensive applications.

Luckily for you, these complex algorithmic problems have all been solved many times in the past. Understanding how they are solved will give you some great tools to apply to other (similar) problems on your own. Algorithms are really just ways of solving problems systematically. In this brief introduction, we’ll focus on a couple of algorithms that you may run into when coding on your own – breadth-first-search and depth-first-search.

Lesson overview

This section contains a general overview of topics that you will learn in this lesson.

• What is a data structure?
• What is a stack?
• What is a queue?
• What’s the difference between a stack and a queue?
• What is a stack useful for?
• What is a queue useful for?
• What’s the best way to implement stacks and queues in Ruby?
• Why bother having many different search algorithms?
• What is breadth-first-search (BFS)?
• What is depth-first-search (DFS)?
• What situations would you want to use BFS?
• What situations would you want to use DFS instead?
• When would BFS be impractical?

Assignment

Knowledge check

The following questions are an opportunity to reflect on key topics in this lesson. If you can’t answer a question, click on it to review the material, but keep in mind you are not expected to memorize or master this knowledge.

• What is the difference between a stack and a queue?
• How would you implement a queue in Ruby?
• What is a linked list? What is a node?
• Which recursive problem-solving method/algorithm design principle does binary search implement?
• What abstract data type would you use to defer/store nodes in a breadth-first tree traversal?
• What abstract data type would you use to defer/store nodes in a depth-first tree traversal?

Additional resources

This section contains helpful links to related content. It isn’t required, so consider it supplemental.

• Khan Academy’s great Algorithms Course
• Stanford’s Coursera 4-Part Algorithm Course
• Visualizing Algorithms from Mike Bostock
• Another free course on algorithms by Udacity
• A brief note on putting Sorting, Tries and Heaps into Ruby, by Ilya Grigorik
• A more detailed video on stacks and queues
• A stack overflow discussion on the relative strengths of BFS and DFS.