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
Using discussion forums
Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
We're super small and self-funded with only 2-3 people developing technical video content. Our mission is to make high-quality courses available at super low prices.
The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.
We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.
It is a hard trade-off.
Thank you for your patience and understanding!
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.
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.
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.
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 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.
Insertion and Lookup are operations which are very fast in a Binary Search Tree. See how they work and understand their performance and complexity.
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.
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.
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.
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.
Let's build a real heap in Java!
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.
Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: longtime Flipkart employee too, and IIT Guwahati alum
We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!
We hope you will try our offerings, and think you'll like them :-)