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5 most common and important data structures that every data scientist should learn and master

Data structures are the building blocks of data science. They are the ways of organizing and storing data in a computer so that it can be accessed and manipulated efficiently. Data structures can affect the performance, complexity, and readability of your code. Therefore, it is important to learn the most common and useful data structures for data science. In this article, we will introduce you to 5 data structures that every data scientist should learn and how they can help you solve various data problems.

1. Stacks- Stacks are data structures that follows the Last In First Out (LIFO) principle. Elements are added and removed from the top of the stack. Stacks are efficient for implementing operations such as function calls and backtracking.

2. Queues- Queues are data structures that follows the First In First Out (FIFO) principle. Elements are added to the back of the queue and removed from the front of the queue. Queues are efficient for implementing operations such as job scheduling and message processing.

3. Trees- Trees are hierarchical data structures that consists of a set of nodes, where each node can have one or more children nodes. Trees are efficient for storing and searching data that has a hierarchical relationship, such as a file system or a directory of employees.

4. Heap- Heap is a data structure that maintains a sorted order of elements. Heaps are efficient for implementing priority queues and sorting algorithms.

5. Hash tables- Hash tables are data structures that maps keys to values. Hash tables are efficient for finding the value associated with a given key.

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5 Data Structures That Every Data Scientist Should Learn - Analytics Insight