Why Every Developer Should Learn Data Structures and Algorithms

What is data structure?

Data structure is a way to organize data in a form that is accessible to computers. It allows the processing of large amounts 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.

Types of data structures:

• Arrays: this is a collection of elements of the same type placed in contiguous memory locations.
• Linked lists: 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 the LIFO(Last In First Out) principle where the last element in the stack will be removed first.
• Queues: follow the FIFO(First In First Out) where the first element stored is removed first.
• Hash Tables: store values that have keys related to each of them.
• Trees: data is organized hierarchically and linked together. e.g. Binary Search Tree, Binary Tree, etc.
• Heaps also called binary heap: is ****a specialized tree-based data structure in which the tree is a complete binary tree and the data is stored.
• Graphs: consists of a set of nodes and edges connecting each other

What is an algorithm?

An algorithm is a set of operations performed in a step-by-step manner to execute a task.

Concepts to understand in algorithms:

• Analysis of algorithms: this is the determination of the amount of time and space resources required to execute it.
• Searching and sorting: Searching algorithms are designed to retrieve an element from any data structure where it is used. Sorting algorithms are used to rearrange a given array or list of elements according to a comparison operator on the elements.
• Greedy algorithms: an approach for solving a problem by selecting the best option available at the moment.
• Dynamic programming: Mostly it’s used for optimization where we have problems that can be divided into similar sub-problems so that their results can be reused.
• Pattern searching: is used to find a pattern or substring from another bigger string.
• Backtracking: a technique whose goal is to use brute force to find solutions to a problem. I
• Divide and conquer: breaks down a problem into two or multiple sub-problems until they become simple enough to be solved directly.
• Geometric algorithms: are designed to solve geometric problems.
• Mathematical algorithms: a set of steps that can be used to solve a mathematical computation.
• Bit algorithms: used to perform operations at the bit level or manipulate bits in different ways.
• Graph algorithms: non-linear data structure consisting of vertices and edges.
• Randomized algorithms: a technique that uses a source of randomness as part of its logic.
• Branch and bound: a technique used for problem-solving.

Why you should learn data structure?

Data structures are the building blocks of more complex problems, they implement the physical forms of abstract data types and play an important role in the design of efficient software and in the implementation of algorithms.

• Here are some use cases:
• • Storing data: storing records in a database management system by specifying a collection of attributes and their structures.
  • Managing resources and services: core operating system 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 trees, also called ordered or sorted binary trees. Priority queues data structures organize items according to their priorities.
  • Indexing: Databases often use sophisticated data structures such as B-trees to index objects.
  • Searching: Indexes created using binary search trees, B-trees, or hash tables speed up 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 you should learn algorithms?

An algorithm is a set of instructions or actions that are performed within a limited amount of space and time to provide a solution to a particular issue. They are used in programming to get the desired result and can be invoked in any language.

How are data structures and algorithms related?

1. A data structure is an entity that contains information used by algorithms.
2. A data structure allows you to store elements in memory and provides functions for manipulating the elements stored.
3. Some data structures are more suitable for solving specific problems.
4. We implement algorithms using data structures that allow you to store the data you will use to solve the problem.

For example: If we need to sort a list of numbers then we can use an array data structure to store the numbers and apply the sorting algorithm like quicksort, insertion sort, etc to sort the array.

Merits of learning data structure first

1. Understanding how data structure helps in storing the data efficiently in the storage device.
2. Understanding how data structure provides the convenience of retrieving the data from the storage devices and provides effective processing on small as well as large amounts of data.
3. 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.
4. You’ll see modifications can be easily done in large data by choosing the best-suited data structure.
5. You’ll know the properties of each structure, with their advantages and disadvantages which will help in problem-solving eventually.

Demerits of learning data structure first

1. Going deep into data structures only without the knowledge of algorithms can make the concepts hard
2. There are advanced data structures that need some prior knowledge about the algorithm
3. 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 of learning algorithms first

1. You’ll learn how to break the problem into smaller subproblems that can be converted into a program easily.
2. You’ll learn how algorithms will help programmers to implement the solution to the problem easily.
3. You’ll learn how algorithms can reduce the time complexity to solve any particular task.
4. You’ll learn about how the logical implementation of the program is written in steps to solve the problem.
5. It becomes easy for any programmer to debug the code easily and identify the logical error in the program.

Demerits of learning algorithms first

1. As most problems are related to the data, its retrieval or processing and modification before having any prior knowledge of data structure cannot be applied directly.
2. It will be very difficult to learn and apply the algorithm without having any data structure.
3. The concept of the most efficient algorithm for a problem will not be met in every scenario

To conclude data structures and algorithms are dependent on each other, a well-suited data structure to apply algorithms, and similarly, we apply algorithms to the data structure.

Data structures are the building blocks of algorithms and algorithms are the platforms upon which data structures are applied and tested.

With all these cases and after weighing the pros and cons it is important you start learning data structures first but do not dig deep into it without the knowledge of algorithms. The two are both necessary for the best results hence it is best they are studied along side.