Byte-Sized-Chunks: Stacks, Queues, Binary Trees, Heaps

Name: Byte-Sized-Chunks: Stacks, Queues, Binary Trees, Heaps
Rating: 4.9 (25 reviews)

A visual way to master basic data structures for strong fundamentals!

Created byLoony Corn

Last updated 1/2018

English

What you'll learn

Design and implement software that use fundamental data structures
Visualise - really vividly imagine - the common data structures, and the algorithms applied to them
Understand the trade-offs, complexity and use-cases for different types of container data structures

Course content

5 sections • 23 lectures • 5h 24m total length

You, This Course, and Us!1:09
Meet The Stack - Simple But Powerful15:40
The stack is a very simple and easy to understand data structure. However it lies underneath many complicated real world problems and is incredibly useful.
Building A Stack Using Java16:53
Let's build a stack for real using Java. It'll have all the operations we're interested in - push, pop, peek, size etc. It can hold any data type, it's a generic class.
Match Parenthesis To Check A Well Formed Expression11:21
Problems which use stacks as a part of their solutions are very common in programming interviews. Matching parenthesis to check for well formed expressions is a classic interview question - let's solve this using the stack we're already implemented.
Find The Minimum Element In A Stack In Constant Time8:51
Another interview question implemented. You have space available but your processing needs to be very fast indeed. How would you keep track of the minimum element of a stack as it changes?
Meet The Queue - A Familiar Sight In Everyday Life14:11
The queue belongs to the same linear data structure family as the stack but it's behavior is very different. Queues are much more intuitive as there are plenty of real world examples where a queue is the fair and correct way of processing.
The Circular Queue - Tricky But Fast19:44
A common, fast but slightly tricky implementation of the queue is the array where the last element wraps around to the first. An interview favorite, let's see how to implement the circular queue.
Build A Queue With Two Stacks17:30
We know the stack, and we know the queue. This problem brings them together. It's possible to mimic the behavior of a queue using 2 stacks in the underlying implementation. Let's write the most efficient code possible to make this work.

Meet The Binary Tree - A Hierarchical Data Structure13:03
The binary tree is an incredibly useful hierarchical data structure. Many other, more complex data structures, use the binary tree as the foundation. Let's see what a binary tree looks like and learn some simple terminology associated with the tree.
Breadth First Traversal18:43
Traversing a binary tree can be done in variety of ways. The breadth first traversal visits and processes nodes at every level before moving on to the next. Let's visualize breadth first traversal and see how it's implemented.
Depth First - Pre-OrderTraversal14:35
Depth first traversal can be of 3 types based on the order in which the node is processed relative to it's left and right sub-trees. Pre-order traversal processes the node before processing the left and then the right sub trees.
Depth First - In-Order and Post-Order Traversal13:51
Depth first traversal can be of 3 types based on the order in which the node is processed relative to it's left and right sub-trees.

In-order traversal processes the left subtree, then the node itself and then it's right sub trees. Post-order traversal processes the node *after* it's left and right subtrees.

The algorithms are all remarkably similar and very easy once you use recursion.

Minimum Value, Maximum Depth and Mirror12:14
Find the minimum value in a binary search tree, find the maximum depth of a binary tree and mirror a binary tree. Learn to solve these problems recursively and see implementation details.
Count Trees, Print Range and Is BST14:39
Count the number of structurally unique binary trees that can be built with N nodes, print the nodes within a certain range in a binary search tree and check whether a certain binary tree is a binary *search* tree. Learn to solve these problems and understand the implementation details.
Has Path Sum, Print Paths, Least Common Ancestor14:49
Check if a path from root node to leaf node has a specified sum, print all paths from the root node to all leaf nodes and find the least common ancestor for two nodes in a binary tree. Learn to solve these problems and understand the implementation details.

The Heap Is Just The Best Way to Implement a Priority Queue17:15
Priority Queues allow us to make decisions about which task or job has the highest priority and has to be processed first. Common operations on a Priority Queue are insertion, accessing the highest priority element and removing the highest priority element.

The Binary Heap is the best implementation of the Priority Queue.
Meet The Binary Heap - It's A Tree At Heart12:39
The Binary Heap is logically a Binary Tree with specific constraints. Constraints exist on the value of a node with respect to it's children and on the shape of the tree. The heap property and the shape property determine whether a Binary Tree is really a Heap.
The Binary Heap - Making It Real With Code7:38
Let's build a real heap in Java!
The Binary Heap - Logically A Tree Really An Array17:13
The Binary Heap may logically be a tree, however the most efficient way to implement it is using an array. Real pointers from parent to child and from child to parent become implicit relationships on the indices of the array.
Heapify!19:32
How do we ensure that when we add an element or remove an element from an existing heap, that the heap property and shape property is maintained? This operation is called Heapify.
Insert And Remove From A Heap16:34
Once we understand heapify, adding and removing elements from a heap become very simple.

Requirements

Basic knowledge of programming is assumed, preferably in Java

Description

Note: This course is a subset of our much longer course 'From 0 to 1: Data Structures & Algorithms' so please don't sign up for both:-)

This is an animated, visual and spatial way to learn data structures and algorithms

Our brains process different types of information differently - evolutionarily we are wired to absorb information best when it is visual and spatial i.e. when we can close our eyes and see it
More than most other concepts, Data Structures and Algorithms are best learnt visually. These are incredibly easy to learn visually, very hard to understand most other ways
This course has been put together by a team with tons of everyday experience in thinking about these concepts and using them at work at Google, Microsoft and Flipkart

What's Covered:

Big-O notation and complexity
Stacks
Queues
Trees
Heaps

Who this course is for:

Nope! Please don't enroll for this class if you have already enrolled for our 15-hour course 'From 0 to 1: Data Structures and Algorithms'
Yep! Computer Science and Engineering grads who are looking to really visualise data structures, and internalise how they work
Yep! Experienced software engineers who are looking to refresh important fundamental concepts

Byte-Sized-Chunks: Stacks, Queues, Binary Trees, Heaps

What you'll learn

Explore related topics

Course content

Strong Fundamentals: Data Structures!8 lectures • 1hr 45min

Binary Trees4 lectures • 1hr

Binary Search Trees2 lectures • 27min

Binary Tree Problems3 lectures • 42min

Heaps6 lectures • 1hr 31min

Requirements

Description

Who this course is for: