What Should I Learn First: Data Structures or Algorithms?
Data structure and algorithms are an integral part of computer science. All the enthusiasts, at some point in time, learn these two important topics. They are different yet very much interrelated topics. This interrelation brings out the big question that needs to be answered: “What should I learn first – data structures or algorithms?”
In this article, we will be focusing on this problem and try to give you a solution along with an overall view of both the possibilities. Firstly, before understanding what is the first priority for you to learn, it is a must that you learn what is a data structure and what is algorithm first.
Table of Contents
- What is data Structure?
- What is Algorithm?
- Why Data Structures and Algoithms are Important to Learn?
- How are Data Structures and Algorithms related?
- Merits and Demerits of Learning Data Structure First
- Merits and Demerits of Learning Algorithms First
- What Should you Learn First? – Conclusion
What is Data Structure?
A data structure is the mathematical or logical model of an organization of data. In short, a data structure is a way to organize data in a form that is accessible to computers. It allows the processing of a large amount of data in a relatively short period of time. The main purpose of using data structures is to reduce time and space complexities. An efficient data structure makes use of minimum memory space and takes the minimal possible time to execute.
If we think to learn data structure first then we have to cover the below topics that are mostly used in computer science:
Types of Data Structure:
- Arrays: An array is a collection of elements of the same type placed in contiguous memory locations.
- Linked Lists: It is a linear data structure, in which the elements are not stored at contiguous memory locations and the elements are linked with each other.
- Stacks: Follow LIFO (Last In First Out) principle. In this, the last element in the stack will be removed first.
- Queues: It follows the FIFO principle (First In First Out), in this, the first element stored is removed first.
- Hash Tables: This is a type of data structure that stores values which have keys related to each of them.
- Trees: It is a data structure in which data is organized hierarchically and linked together. Some Examples are the Binary Search tree, Binary tree, Splay tree, AVL Tree, etc.
- Heaps: It is a specialized tree-based data structure, also called binary heap in which the tree is a complete binary tree and the data is stored.
- Graphs: It consists of a set of nodes and edges connecting each other.
What is Algorithm?
Now, let’s switch to the other side of the coin and see what is meant by the term algorithm. Algorithms are well-defined sets of instructions designed that are used to solve problems or perform a task. To explain in simpler terms, it is a set of operations performed in a step-by-step manner to execute a task.
Consider a simple algorithm to multiply two numbers:
- Take two number inputs
- Multiply numbers using the * operator
- Display the result
We can see that to perform the multiplication task, we have to follow the operation in a step-by-step manner. Any step taken in a different manner, or any different step, both will not lead to the expected result.
If we think to learn algorithms first then we have to cover the below topics that are mostly used in computer science:
- Analysis of Algorithms
- Searching and Sorting
- Greedy Algorithms
- Dynamic Programming
- Pattern Searching
- Divide and Conquer
- Geometric Algorithms
- Mathematical Algorithms
- Bit Algorithms
- Graph Algorithms
- Randomized Algorithms
- Branch and Bound
To learn more about algorithms refer to our article: “Introduction to algorithm“.
Why Data Structures and Algorithms Are Important to Learn?
It is natural to have second thoughts about any decision, as can be the case with learning DSA. You can easily find yourself in a dilemma as to why you should learn DSA? Don’t worry, we got your cold feet covered. Data structure and Algorithms help in understanding the nature of the problem at a deeper level and thereby a better understanding of the world.
Why should you learn Data Structure?
Data structures are the building block of more complex problems. In general, data structures implement the physical forms of abstract data types. They play an important role in the design of efficient software and in the implementation of algorithms. In addition to using data structures, it is also important to choose the right data structure for each task. Choosing an inappropriate data structure may result in slow runtimes or inefficient code.
Below are some examples that how data structures are used:
- Storing data: Data structures are used to store records in a database management system by specifying a collection of attributes and their structures.
- Managing resources and services: Core operating system (OS) resources and services are enabled through the use of data structures such as linked lists for memory allocation, file directory management, and file structure trees, as well as process scheduling queues.
- Data exchange: Data structures organize information that is shared among applications, such as TCP/IP packets.
- Ordering and sorting: Data structures provide efficient ways of sorting objects like binary search trees, also called ordered or sorted binary trees. Priority queues data structures organize items according to their priority etc.
- Indexing: Databases often use even more sophisticated data structures, such as B-trees, to index objects.
- Searching: Indexes created using binary search trees, B-trees or hash tables speed the ability to find a specific sought-after item.
- Scalability: Big data applications use data structures for allocating and managing data storage across distributed storage locations, ensuring scalability and performance.
Why should you learn Algorithms?
An algorithm is a sequence of instructions or actions that are performed within a limited amount of space-time to provide a solution to a particular issue. It is a systematic and logical approach to tackle or overcome an issue by the step-by-step process to start from scratch and develop a solution.
It is essential to have an algorithm that is both time and space-advanced and the best way to illuminate various kinds of issues is with the help of various kinds of algorithmic techniques.
Suppose, for instance, you decide to cook a new recipe, and you read the instructions and then follow each step as described in the recipe. After following the steps, you will be able to cook your food.
Algorithms in programming are used to get the desired result, these algorithms are language-independent, that is, they can be invoked in any language. As expected, the output will also be the same.
Data structure and algorithms help in understanding the nature of the problem at a deeper level and thereby a better understanding of the world.
If you want to know more about Why Data Structures and Algorithms are necessary, then you must watch this video by Mr. Sandeep Jain (CEO & Founder, GeeksforGeeks).
To understand more about their importance, you can also refer to our detailed article on: “Why Data Structures and Algorithms are important to learn?“
How are Data Structures and Algorithms related?
As we said earlier, though Data Structures and Algorithms are different, they are very much interrelated. Now let us have a look at how.
- A data structure is an entity that contains information used by algorithms.
- A data structure allows you to store elements in memory and provides functions for manipulating the elements that are stored.
- Some data structures are more suitable for solving specific problems.
- We implement an algorithm on our computer using data structures, which allow you to store the data that you will use to solve the problem.
If we need to sort a list of numbers, then we can use an array data structure to store the numbers, and apply sorting algorithms like quicksort, insertion sort, etc. to sort the array.
If we want to store only unique elements and find any element in it, then we can think to use a set data structure that keeps only unique elements and we can use a Binary search algorithm to search for an element in it.
You can also try our Data Structures and Algorithms – Self Paced courses to learn about Data Structures and Algorithms in depth.
Merits and Demerits of Learning Data Structure First
Planning to go with Data Structures first, then Algorithms? We understand you. That is why we have curated the next section to help you understand the merits and demerits of opting for Data Structures first.
Merits of Learning Data Structure first:
- You’ll learn about how data structure helps in storing the data efficiently in the storage device.
- You’ll also learn about how data structure provides the convenience of retrieving the data from the storage devices and provides effective and efficient processing on small as well as large amounts of data.
- You’ll get the idea about how choosing a proper data structure can reduce the operation cost such as retrieving or processing data. which will save the programmers’ and users’ time and money.
- You’ll see that modifications can be easily done in large data by choosing the best-suited data structure.
- You’ll know the properties of each data structure, with their advantages and disadvantages, which will help in problem-solving eventually.
Demerits of learning Data Structure first:
- Going deep into Data Structures only, without the knowledge of Algorithms can make the concepts hard.
- There are some advanced data structures like Disjoint Sets, Self-Balancing Trees, Segment Trees, Tries, etc. which need some prior knowledge about the algorithm. So before knowing their proper algorithm, it will be very difficult to implement.
- Learning only about Data Structures will not be helpful as it is not necessary that the best data structure is the most efficient for a particular scenario.
Merits and Demerits of Learning Algorithms First
Certain times, you feel how hard will it be to learn algorithms first without the knowledge of Data Structure! To help you dive into this situation, we have summarised the best of the merits and demerits of what can happen if you learn algorithms first, instead of DS.
Merits of learning algorithm first:
- You’ll learn about how to break the problem into smaller subproblems that can be converted into a program easily.
- You’ll learn how algorithms will help programmers to implement the solution to the problem easily.
- You’ll learn how algorithms can reduce the time complexity to solve any particular task.
- You’ll learn about how the logical implementation of the programs is written in steps to solve the problem.
- It becomes easy for any programmer to debug the code easily and identify the logical error in the program.
Demerits of learning algorithm first:
- As most of the problems are related to the data, its retrieval or processing, and modification, before having any prior knowledge of data structure, cannot be applied directly.
- It will be very difficult to learn and apply the algorithm without having any data structure.
- The concept of the most efficient algorithm for a problem will not be met in every scenario.
What Should You Learn First: Data Structures or Algorithms? – The Conclusion
Data structures and algorithms are dependent on each other. We use a well-suited data structure to apply algorithms and similarly, we apply algorithms to the data structure. And it is also clear from the definition that data structure stores the unstructured data in an organized form whereas algorithms are the set of instructions that a computer follows to solve a particular task.
Data structures are the building blocks of Algorithms, and Algorithms are the platforms upon which Data Structures are applied and tested.
With all the cases put forward, and after discussing the merits and demerits of each scenario, it is important that you start learning Data Structures first, but do not dig deep into it without the knowledge of Algorithms. Data Structures and Algorithms are both necessary for the best results, and hence it is best that they are studied alongside.
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