5 Data Structures Explained MUST KNOW (for Software Engineers)

Video Description

Check out my interview prep platform for learning the patterns! 📢 Interview Prep Platform: https://algoswithmichael.com 🎧 Join the community Discord: https://discord.gg/aVWsAaaCtT 💰 Support me on Patreon: https://www.patreon.com/michaelmuinos 🔗Follow me on LinkedIn: https://www.linkedin.com/in/michael-muinos 📂Follow me on Github: https://github.com/MichaelMuinos Data structures are the core building blocks to any program you write, no matter the tech stack. I like to think of programs as data plus algorithms plus data structures. In this video, I go over 5 data structures that you should learn with simple examples! The first data structure is a Trie which is a tree data structure. In this data structure you store characters. Each node can have up to 256 characters if you consider all numbers in the ascii table and extended. Storing characters inside of a Trie allow you to create a dictionary of words that you can later look up. Real world examples of a Trie include the usage of auto-completion and spell-checking. The second data structure is a disjoint set, also known as union-find. A disjoint set data structure tracks a set of elements into disjoint non-overlapping subsets. It has two functions: union and find. In the union function, you take two subsets and merge them together. With the find operation, you can determine if two objects are in the same subset or not. This data structure is used to solve kruskal's minimal spanning tree and to find least common ancestors; however, you can mostly just use the disjoint set to solve popular coding interview and LeetCode questions such as Number of Islands, Friends Circl, Number of Components in an Undirected Graph, and many more! The third data structure is a treemap. A treemap inherits from the SortedMap interface which extends from a Map. A treemap will store entries inside of it, but the entries will be sorted according to the keys. If you are storing integers as keys, the entries will be sorted in ascending order by the values of those keys. You can perform queries such as finding the smallest and highest entry or finding entries that are greater than or less than a number. The time and space complexity for this data structure is logarithmic. The fourth data structure is a skip list. A skip list has two different lanes: express and normal. In a skip list, your express lane will "skip" over multiple nodes in order to make lookups, deletions, and insertions much faster. A skip list can perform operations in logarithmic time while as a normal linked list can only do it in linear time. This data structure acts as a balanced binary tree, but the key difference is that a skip list is better in a distributed environment where you have potentially many threads accessing the structure. The fifth and final data structure is a B-Tree. A B-Tree is a self-balancing search tree where you have keys inside of nodes. Each key stores data where the order of the tree is the maximum amount of children each node can have. The primary use of B-Trees are in storage engines functioning as the the underlying structure for default indexes on relational database tables. A B-Tree is much more efficient than a regular tree because in a regular binary tree you have the chance for your tree to degrade. ►Follow me on Twitter: https://twitter.com/MichaelMuinos ►Follow me on Github: https://github.com/MichaelMuinos ---------------------------------------------------- Hot Coffee by Ghostrifter Official https://soundcloud.com/ghostrifter-of... Creative Commons — Attribution-ShareAlike 3.0 Unported — CC BY-SA 3.0 Free Download / Stream: http://bit.ly/2CMqiz0 Music promoted by Audio Library https://youtu.be/Y30VQPGtxOs