Unlocking the Hidden Gold: How Data Structures and Algorithms Supercharge MNCs to Success
Technology and innovation are leading the way for positive changes in this quickly evolving world. Industries need more efficient programmers to solve complex industry-related problems. A programmer with basic Data structures and algorithms (DSA) knowledge can understand and solve programming issues better.
DSA is the basis on which computer science is built. You can study a data structures course and become an expert programmer. Before you start learning DSA, it is essential to understand how it changes the different sectors and its various applications.
Understanding data structures and algorithms
Every computer program stores, fetches and utilizes the data according to its needs. Data structure refers to the place or space in which we store and manage this data to use it effectively. Algorithms are a set of given steps the system takes to get user input and display the outcome after solving a problem.
DSA enables programmers to develop and optimize efficient computer programs. Data structures help store and maintain data, while algorithms develop problem-solving methods. We can effectively optimize and maintain the code using data structures with algorithms in software development.
DSA is an integral part of the development process. DSA can work well with any programming language. Even though a language gets modified, the basics of DSA remain the same for all. DSA is compatible with all programming languages, which is a boon for software development processes.
Benefits of DSA in software development
The choice of data structures and algorithms decides the efficiency of software development. You need to connect data structures and algorithms suitable to each other. It can have benefits like -
• Efficiency in time and speed
Good knowledge gained from a data structures course helps ensure that the software you develop meets the time and speed requirements. Clients always prefer time and cost-effective software solutions, which is possible with DSA. Basic knowledge of DSA guides programmers to have smooth app development followed by better data storage and retrieval.
• Advance your career
A thorough knowledge of front and back-end programming is vital for a programmer. Along with it, their proficiency in DSA is also needed. Software giants like Amazon, Google, Meta, and Microsoft require developers who understand DSA. Hence, learning DSA helps find better-paying jobs in such companies.
Most programmers make the mistake of skipping through DSA while learning software development. However, they later find out the importance of DSA and how it helps solve significant problems quickly. It is better to learn DSA along with software development to ensure you understand every aspect of the development process.
• A better understanding of programming
Using DSA, you can understand complex programming concepts, which helps with problem-solving. A knowledge of DSA helps the programmers choose the most suitable ones to solve the problem encountered. It will help you quickly identify the concern and address it.
A good programmer should know which data structure can help manage memory efficiently and which algorithm better fits a given problem. There are libraries in specific languages, so developers can quickly implement them in solutions.
A data structures course can help you learn how to develop effective solutions without wasting much time.
• Develop problem-solving
Through DSA, you can develop advanced problem-solving skills. You may set yourself a step further from others with the knowledge you possess in DSA. It can be beneficial in interviews, where you can impress recruiters with your advanced problem-solving skills gained in a data structures course.
DSA in real-world problems
• Efficiency
DSA enables programmers to create and manage resources such as memory and time. You can minimize search time and memory allocation through DSA, significantly increasing performance. For example, a nicely designed binary search algorithm can help improve the search performance in a vast dataset.
• Problem-solving
You can use DSA to break down a complex problem into smaller parts that are easy to manage. It helps simplify the analysis and find a possible solution. DSA creates a systematic problem-solving process for such problems. DSA helps in tracking the patterns of the problems and designing an action plan to solve them.
• Scalability
With the increase in data volumes, it becomes vital to handle complex operations. With the help of DSA, developers can adequately maintain and utilize data. For example, the data a big platform such as social media generates is massive in quantity.
We can maintain smooth operations under such massive loads using balanced trees, hash tables, or other DSA concepts. You can practice using DSA in such real-life applications in a data structures and algorithms full course.
DSA in day-to-day life
Let's look into some examples of DSA.
1. Data storage in tabular form
The array data structure has a fixed length. It stores identical data. An array is primarily responsible for storing elements in tabular form. For example, an array helps store the contacts in a phone in tabular form.
2. Photo viewer
A Singly Linked List is a popular linear data structure that functions with nodes. There are two parts to a node, known as data and pointer. A single node stores data, and its pointer has the address of the connecting node. Therefore, a Singly Linked List can help present photos in a slide-show format.
3. Boarding a bus
Queue data structures are presented in the FIFO format. FIFO refers to 'First-In, First-Out.' Application of a similar type of queue format is seen in buses while boarding. Other examples include a server replying to requests or passing an e-mail.
4. Social media applications
The graph data structure stores a series of edges and vertices connected to each other. This type of data structure is highly useful in social media apps and Google Maps.
5. Books on a shelf
Sorting algorithms such as Quick Sort, Binary Sort, Selection Sort, Insertion Sort, etc. help understand how to arrange books on a shelf. Searching algorithms help locate books on a shelf. Google Maps uses the shortest path first algorithm to find the shortest path.
Why is DSA important for interviews?
Technical skills
As tech companies search for candidates with solid technical skills, DSA knowledge is helpful. It displays the candidate's skill in analyzing problems, creating solutions, and applying them. Recruiters seek technically skilled candidates to choose the most suitable DSA for each project.
Code optimization
Solving a problem using the right solution is not the only thing recruiters assess in a candidate in interviews. The employers look for experts who can optimize the solution. DSA helps you build better analysis to develop properly optimized code. Candidates who can create code that functions correctly stand out in interviews.
New technologies
New frameworks and technologies are developing frequently. DSA helps you easily adapt to these new and challenging technologies. DSA is the basics everyone should know. Therefore, solid DSA knowledge helps developers grasp and use them in any language and framework.
Types of data structures
There are many basic and advanced data structures you can use in a program or an application. Data structures are present in the pattern of lists, queues, tables, etc. Data structures help properly process and retrieve data.
They are of two forms:
1. Linear data structures
Linear data structures arrange the data elements in a linear pattern. Every element connects with the adjacent element. Some examples include queue, stack, array, linked list, and more. We further divide linear data structures into static and dynamic data structures.
Static data structure
Status data structures contain fixed memory length. It helps easily control the elements. Searching for an element is easier in a static data structure compared to a dynamic data structure. The array represents a popular example here.
Dynamic data structure
We can modify the memory length in a dynamic data structure, as it does not possess a fixed length. A dynamic data structure can handle complex codes or programs, which makes it more effective than a static data structure. Stack and queues are some examples.
2. Non-linear data structures
Non-linear data structures never arrange the data elements in a sequence or an orderly manner. These elements are connected over several layers, so it is difficult to process them at one time. Trees and graphs are some examples.
Also, read Essential Data Structures: Must-Know Basics for Every Programmer.
Types of Algorithms
Different problems require different algorithmic methods of solving. You can learn many types of algorithms from a suitable data structures course. We have enlisted the popular ones below.
• Brute force algorithms
This is an essential and simple algorithm. Brute force is a direct approach to addressing an issue. More specifically, it refers to using every possible method to determine the optimum solution.
• Recursive algorithm
This algorithm breaks down a difficulty into smaller sub-parts of similar types. It loops through the problem repeatedly until it's solved. The Fibonacci series, number factorial, DFS graph, and tower of Hanoi are typical examples of recursive algorithms.
It is further divided into
Divide and conquer algorithm
The divide and conquer algorithm breaks down a problem into two parts for a faster solution. The first part consists of breaking down the problem into smaller, identical sub-problems. The second part involves solving the smaller sub-problems individually and adding the outcomes to produce a combined solution.
Merge Sort, Binary Search, and Quick Sort are examples of divide-and-conquer problems.
Dynamic programming algorithm
This algorithm stores the outcome of a similar problem that is already solved. It saves time in solving similar problems again with a new procedure. It is also called the memorization method. After dividing the problem into sub-problems, we store its outcome for the future.
Floyd Warshall Algorithm, Weighted Job Scheduling, and Knapsack Problem are examples of dynamic programming algorithms.
Greedy algorithm
This type of algorithm solves the problem in parts. We use the part that produces instant benefit for the algorithm, hence the term' greedy algorithm'. The algorithm does not consider the previous choices.
Prim's Algorithm, Huffman Coding, and Kruskal's Algorithm are examples of greedy algorithms.
Backtracking algorithm
In this type of algorithm, we solve a problem using one method at a time. We have a methodological approach to reach the solution. If we fail to deliver a solution at any point, we backtrack to the previous point. Therefore, this algorithm builds a problem one at a time.
The m-coloring problem, Rat in Maze Problem, and N Queen Problem are common examples of the backtracking algorithm.
• Randomized algorithm
This algorithm uses a random number to find the expected result. The idea is to use a degree of randomness in the process or logic of the algorithm. The random number that provides instant benefit is chosen.
Quicksort is a problem that is solved using randomized algorithms.
• Sorting algorithm
Sorting algorithms arrange the data in a descending or ascending order. It effectively sorts the data in a particular order. Some examples are insertion sort, bubble sort, quick sort, selection sort, and merge sort.
• Searching algorithm
This algorithm searches a certain element from any sorted or unsorted data structure. Examples - linear search and binary search.
• Hashing algorithm
Hashing algorithms are similar to searching algorithms; however, they have an index containing a key ID. It is called a key-value pair. The algorithms provide specific data with a key. Password verification uses a hashing algorithm.
To learn more, you must enroll in a data structures and algorithms full course.
Use of DSA in Industries
Most sectors use data structures to manage and maintain huge chunks of data. Data structures play an essential part in the overall process as they help store, manage, manipulate, and use data effectively. Data structures are an integral part of engineering, CS, and mathematics.
Here are some of the common examples:
The IT sector properly stores and manipulates its data using data structures like trees, linked lists, and arrays.
The medical sector uses them to display patient data like prescriptions or past data through trees and graph data structures.
Stack, queues, and other data structures help track banking operations and valuations in the finance industry.
The manufacturing industry uses trees and graphs to explain the supply-chain relationships and manufacturing methods.
Data structures and algorithms help write and develop efficient codes for various operations in the computer science sector. DSA helps develop operating systems, video games, web browsers, and databases.
In the data science field, DSA helps build and evaluate models for decision-making, natural language processing (NLP), image recognition, and other activities in the artificial intelligence and machine learning domain.
DSA can solve significant issues related to optimization and operations research industries. It can work effectively on issues occurring in transportation, finance, logistics, and other fields.
Data structures and algorithms help organize and deliver network information in systems like the web and local networks.
How do you prepare your DSA knowledge for interviews?
You can become a better programmer with DSA; it offers job roles such as data scientists or computer programmers in big tech companies. You can apply for such positions confidently through your solid knowledge of the programming basics.
However, you must be goal-oriented and constantly learn to become proficient in DSA. DSA is not an easy concept to study and requires proper planning before you start your learning. If you want to crack the coding-based interviews in top companies, a data structures and algorithms full course can be taken online.
Let us learn how you should prepare for interviews.
Basics knowledge
You need to enhance your knowledge of DSA using the basics. Starting with a small problem is a good idea, as it will help you develop a strong foundation in DSA. A decent programmer is able to write 20–30 lines of code. Therefore, you must solidify your basics before starting with complex problems.
In a coding profession, you are likely to make errors & face issues. You will develop strong coding and problem-solving skills if you practice enough with 100+ coding problems. It is better to refine your skills by revisiting basic concepts repeatedly, as you may forget them after a while.
You may develop the following skills:
- Collections
- Time and space complexities
- Logic building
- Sorting
- Arrays
You may go through some coding interview problems asked in your preferred sector or company interviews. Sites such as Leetcode provide learners with questions and competitive activities to enhance their coding skills. You should commit around 100–150 hours of studying overall to develop solid core knowledge.
Advanced knowledge
A great way to get advanced knowledge of DSA is by solving coding problems in Leetcode and other online coding sites. Begin by solving questions of medium difficulty, and try to solve them by hand initially. You may solve at least 100–150 problems before you develop advanced knowledge.
By the end, you must be able to create different solutions for a problem, understand common errors, and develop advanced debugging and coding skills. While learning, focus on the following areas and give them up to 200 hours of practice.
- DFS (Depth for Search)
- BFS (Breadth for Search)
- Tree, BST, Graph, and Heap applications
- Creating graph traversals in 10 minutes
- Executing Queue, Stack, BST, Hash, and Tree
Refined knowledge
If you want to develop more skills and become better at coding, constant practice is necessary. Even after getting a programming job, you must stay updated and constantly refine your knowledge. Practicing consistently boosts your coding speed.
At this stage, you can take up a wide variety of coding questions and try to solve them. This will help you become familiar with situations when you get stuck during coding-based interview rounds. You can expand your knowledge and try as many new and exciting concerns as you like.
In addition, enhance your knowledge of the following topics:
- Advanced recursion
- DP memoization
- Backtracking
- DP tabulations
- Knapsack
- Shortest path
- Greedy method
- Spanning-tree
- Graph partitioning
- Topological sort
Must read: Data structures or algorithms: What to learn first?
What skills are needed in coding interviews?
Problem-solving
A candidate's problem-solving skills are the foremost skills that any company judges during interviews. Through DSA questions in coding rounds, recruiters analyze whether your problem-solving skills and basic knowledge meet their standards. You must grasp complex issues, develop effective solutions, and execute them.
Logical reasoning
Candidates must think carefully, assess problems, and implement logical reasoning. Their skills to break down complex problems into smaller parts, locate trends, and develop algorithms are very important in industries. Therefore, companies ask logical questions to evaluate their logical reasoning and analytical thinking.
Scalability
Companies often deal with huge problems and need developers to create algorithms capable of handling significant data volumes. They assess your knowledge of space and time complexities and how you enhance their algorithm performance. The candidate must be skilled enough to propose scalable solutions.
Teamwork
As a developer, you must interact and communicate effectively with other developers, teams, and higher management. Therefore, you must be fit for teamwork to progress with a common software goal. You may have to work in a team with other developers in interviews and develop scalable, efficient solutions together.
Adaptability
New and emerging technologies appear regularly in the tech industries. The software development field requires you to learn and adapt to these technologies. Companies ask DSA questions based on the relevant technologies so that they can assess your willingness to learn.
Conclusion
Data structures and algorithms are very important for different sectors to prosper. You can develop your DSA knowledge and enhance your programming skills to become a better coder and eventually upgrade your role within an organization.
Companies assess their candidate's DSA knowledge and skills in interviews. They can make informed decisions while hiring by ensuring candidates can solve tough issues, develop proper solutions, and add value to their initiatives.
You may join a data structures and algorithms full course to gain a deeper understanding of the basics of programming. Many data structures courses offer relevant study modules, practical training, and interview preparations.
The DSA & System Design is an excellent program that helps learners become professional programmers. This course is useful if you are interested in coding and leveling up your efficiency. The expert mentors in the course will navigate you through complex topics and study effectively.
Learners practice in offline training centers to develop their coding skills. They can assess training facilities in Bangalore, Hyderabad, and other cities. You work on real-world projects as a programmer to develop strong problem-solving skills. The program also offers placement help to learners to get them hired in top IT firms.
IBM and Microsoft certifications are offered at the end of the course, which help learners easily gain placements. The course helps you build DSA skills and refine your coding expertise.
