What Should I Learn First Data Structure or Algorithm?


Heather Bennett

What Should I Learn First: Data Structure or Algorithm?

When it comes to learning programming, one of the most common questions that beginners ask is whether they should learn data structures or algorithms first. Both data structures and algorithms are fundamental concepts in computer science, and understanding them is crucial for building efficient and optimized programs.

Understanding Data Structures

Data structures are a way of organizing and storing data in a computer’s memory. They provide a means for efficiently accessing and manipulating data, which is essential for solving complex problems. Some commonly used data structures include arrays, linked lists, stacks, queues, trees, and graphs.


  • An array is a collection of elements that are stored in contiguous memory locations.
  • Elements in an array can be accessed using their index values.

Linked Lists:

  • A linked list is a collection of nodes where each node contains both data and a reference to the next node.
  • Linked lists are dynamic, meaning that they can grow or shrink during program execution.

Understanding Algorithms

An algorithm is a step-by-step procedure or set of rules for solving a specific problem. It defines the logic behind how a task should be performed.

Algorithms can be classified into different categories based on their efficiency and complexity. Some commonly used algorithms include sorting algorithms (such as bubble sort, insertion sort, merge sort), searching algorithms (such as linear search, binary search), and graph traversal algorithms (such as depth-first search, breadth-first search).

Which One Should You Learn First?

The order in which you learn data structures and algorithms depends on your learning style and goals. However, many experts recommend starting with data structures before diving into algorithms. Here are a few reasons why:

  • Understanding the Basics: Learning data structures first helps you understand how to store and organize data efficiently, which is essential for solving complex problems.
  • Prerequisite Knowledge: Many algorithmic concepts and techniques rely on a solid understanding of data structures. Without this foundation, it can be challenging to grasp the underlying principles of algorithms.
  • Building Blocks: Data structures serve as building blocks for implementing efficient algorithms. By familiarizing yourself with different data structures, you gain the necessary tools to design and optimize algorithms.

That being said, it’s important to note that data structures and algorithms are interconnected. You’ll often find yourself learning both simultaneously as you progress in your programming journey.

The Learning Path

If you’re just starting out, here’s a suggested learning path:

  1. Begin with basic programming concepts like variables, loops, conditional statements, and functions.
  2. Once you have a solid understanding of programming fundamentals, move on to learning data structures like arrays and linked lists.
  3. With a good grasp of data structures, start exploring different types of algorithms such as sorting or searching algorithms.
  4. As you gain more experience, challenge yourself with more complex data structures like trees and graphs along with advanced algorithms.


Your learning journey may differ based on your specific goals or the requirements of the projects you’re working on. The key is to practice regularly and apply what you learn in real-world scenarios to solidify your understanding.


To become a proficient programmer, it’s essential to have a good understanding of both data structures and algorithms. While the order in which you learn them may vary, starting with data structures provides a solid foundation for understanding and implementing efficient algorithms. Remember, practice and hands-on experience are key to mastering these concepts.

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