Data Structures every data scientist should know

data structures

Data structures are the basic building blocks of data. They define how data is organized and accessed. As a data scientist, it is important to be familiar with all the different data structures so you can choose the best one for the task at hand. In this article, we will discuss some of the most important data structures that every data scientist should know.

What is a data structure?

A data structure is a way of organizing data so that it can be efficiently accessed and manipulated. There are many different types of data structures, each with its own strengths and weaknesses. The type of data structure you use will depend on the type of data you are working with and the operations you need to perform on it.
Data structures are used to store data in a computer’s memory. When you choose a data structure, you must also consider the algorithms that will be used to manipulate the data. The time and space complexity of an algorithm is affected by the choice of data structure.
There are two main types of data structures: linear and nonlinear. Linear data structures are those that can be traversed in a single sequence, such as an array or a linked list. Nonlinear data structures are those that cannot be traversed in a single sequence, such as a tree or a graph.

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Linear and non-linear data structures

Data structures can be broadly classified into two types: linear and non-linear. Linear data structures are those that are arranged in a linear sequence, such as an array. Non-linear data structures are those that are not arranged in a linear sequence, such as a linked list.

Linear data structures

Some of the most important linear data structures are:

  • Array: An array is a collection of elements that are stored in contiguous memory locations. This means that each element in an array is assigned a specific index, and all the elements are stored next to each other in memory.
    One advantage of using arrays is that they can be easily indexed, which means that elements can be accessed quickly. However, one downside of using arrays is that they have a fixed size, which means that once an array has been created, its size cannot be changed.
  • Linked Lists: A linked list is a data structure that consists of a group of nodes. Each node contains two fields: a data field and a reference field. The reference field stores the address of the next node in the list.
  • Queue: A queue is a linear data structure that allows elements to be added and removed from only two ends, called the front and the back of the queue. A queue is often described as first in, first out (FIFO).
  • Stack: A stack is a linear data structure that allows elements to be added and removed from only one end, called the top of the stack. A stack is often described as last in, first out (LIFO).

When to use a linear data structure

Linear data structures are best used when you need to access the elements in a sequential order. For example, if you were implementing a queue, you would want to use a linear data structure so that you could easily access the element that was inserted first.


Linear structures are best used:

  • When you need to access the elements in a specific order
  • When you need to be able to index the elements of the data structure
  • When you need to be able to search through the data structure for a specific element

Non-linear data structures

Non-linear data structures are those that are not arranged in a linear sequence, such as a linked list.

Some of the most important non-linear data structures are:

  • Linked list: A linked list is a data structure that consists of a group of nodes. Each node contains two fields: a data field and a link field. The link field contains the address of the next node in the list.
  • Tree: Trees are non-linear data structures that store data in a hierarchical form. A tree has a root node, which is the topmost node in the tree. The root node has child nodes, which are connected to the root node by edges. The child nodes can have their own child nodes, and so on.
  • Graph: A graph is a non-linear data structure that consists of nodes and edges. Nodes are connected to each other by edges. Graphs can be represented using an adjacency matrix or an adjacency list.
  • Hash table: A hash table is a data structure that maps keys to values. A key is mapped to a value by hashing the key and using the resulting hash code to index into an array.
  • Heap: A heap is a tree-based data structure that satisfies the heap property: the value of each node is greater than or equal to the value of its children. Heaps are used to implement priority queues.

When to use a non-linear data structure

Non-linear data structures are used when the relationship between the data elements is not linear. This means that the data elements are not arranged in a sequential order.

Linear structures are best used:

  • When the data elements have a natural sequential order
  • When the data elements need to be accessed in a specific order
  • When the data structure needs to be traversed from start to finish
  • When the relationship between data elements is not linear

Foundational building blocks

There are many other important data structures, but these are some of the most fundamental ones that every developer and data scientist should know. With a strong understanding of these data structures, you’ll be well on your way to becoming a master of algorithms and problem-solving!

Please feel free to share this article if you found it helpful, and be sure to check out our other blog posts for more coding tips and tricks. Happy coding!

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