Algorithm vs. Data Structure: Concepts and Types, How and When to Learn

"When I study coding, I always hear about how important data structures and algorithms are—but what's the exact difference?"

You’ve probably heard terms like "data structures" and "algorithms," but understanding exactly what they mean or their differences might still feel confusing. You might have also heard that preparing for coding tests (especially for enterprise or tech companies) requires solving lots of questions involving data structures and algorithms. But what exactly does this entail?

Today, let's clearly define data structures and algorithms, highlight their differences, and explore how you can efficiently approach learning them for coding interviews.​

Algorithm = Data Structure + Problem-Solving Procedures

​Let's start by quickly defining an algorithm:

An algorithm is essentially a combination of a data structure and a procedure for solving a particular problem.

If a data structure is about organizing and storing data efficiently, then an algorithm is about using that structured data effectively to solve specific problems.

Here's an easy example!

Data structure (example): Adjacency List (one method of storing graph data)

Problem-Solving Procedure (Example):

Given a graph, store it in an adjacency list and apply Dijkstra's Algorithm to find the shortest path efficiently (with O(E log V) time complexity.

Data structure = framework for organizing data

Data structures are like frameworks or tools that help organize and manage data clearly and efficiently.

They determine how your data is stored, accessed, and modified. With the right data structure, tasks such as inserting, deleting, searching, and sorting data become more efficient. For instance, a binary search tree significantly improves the efficiency of searching within sorted data.​

Why data structures matter

Coding interviews heavily emphasize data structures because choosing the right data structure can greatly affect how quickly and efficiently you solve a given problem. That’s why it’s important to thoroughly understand data structures and practice solving diverse problems using them.

Classification of Data Structures

Data structures vary significantly, but they typically fall into two main categories: linear and non-linear.

1️⃣ Linear Data Structures

Linear data structures store data sequentially in a straight line, with each element directly connected in a single path. They’re perfect for scenarios requiring sequential processing.

• Arrays

• Linked Lists

• Stacks

• Queues

2️⃣ Non-Linear Data Structures

Non-linear structures store data in hierarchical or interconnected ways, creating multiple paths between elements.

• Trees

• Graphs

💡 Quick tip to remember the difference:

• Algorithm: Method for solving problems using that data.

• Data Structure: Framework for organizing data.

Key Terms for Algorithms and Data Structures

Algorithm-related keywords:

• Algorithm:
  A logical, step-by-step procedure for solving a particular problem.

• Time Complexity:
  Measures how quickly an algorithm runs based on input size.

  • Examples:

    • O(1): Constant time

    • O(log n): Logarithmic time

    • O(n): Linear time

    • O(n²): Quadratic time (e.g., nested loops)

• Space Complexity:
  Measures how much memory an algorithm requires.

  • Examples:

    • O(1): Minimal extra memory

    • O(n): Linear memory

    • O(n²): Quadratic memory

Data structure-related keywords:

• Array: Stores data in contiguous memory, accessed via indices.

• Linked List: Each node contains data and a reference to the next node.

• Stack: Last-in-first-out (LIFO) structure—items are added/removed from the top.

• Queue: First-in-first-out (FIFO) structure—items are added at the rear, removed from the front.

• Hash Table: Stores key-value pairs, allowing rapid lookups using hash functions.

• Tree: A hierarchical structure with nodes connected by parent-child relationships.

• Graph: Nodes (vertices) connected by edges, representing various relationships.

Understanding these basic terms will greatly help your algorithm and data structure studies.​

How Should You Approach Studying Algorithms and Data Structures?

Should you dive straight into algorithms and data structures right away?

Here’s a quick guide based on your current skill level:

1️⃣ Absolute beginner? → Not Yet!

• Recommended approach:
  If you’re new to coding, focus first on basic programming fundamentals.
  (Examples: Variables, conditionals, loops, functions)

2️⃣ Comfortable with programming basics? → Start with Data Structures

• Recommended approach:
  If you're familiar with programming basics, it's a great time to start learning data structures. They’re the foundation for understanding algorithms.

• Recommended learning order:
  Arrays → Linked Lists → Stacks & Queues → Trees & Graphs

• Goal:
  Understand the basics of each data structure and practice simple implementation problems.

3️⃣ Preparing for coding interviews? → Learn both simultaneously!

• Recommended approach:
  When preparing specifically for coding tests, it’s important to study data structures and algorithms together. Understand clearly how algorithms solve problems, choose the right data structure to implement them, and practice efficient implementations.

4️⃣ Not sure about your current level?

• Recommended approach:
  Use Codetree's Gap-Check to assess your skills accurately and receive personalized learning recommendations!

• Bonus:
  Get tailored course recommendations based on your skill level, goals, learning style, and schedule.

Take Your Time and Grow Step-by-Step

Algorithms and data structures are core elements of coding, but your approach should align with your current skills. Rather than rushing to learn everything at once, set clear, manageable goals for your current stage. With Codetree’s structured approach, you'll efficiently master data structures and algorithms, becoming well-prepared for coding interviews and beyond!