Best 5 Reasons To Study Data Structures & Algorithms in 2021

Top 5 Reasons To Study Data Structures & Algorithms
Top 5 Reasons To Study Data Structures & Algorithms

Introduction

Data Structures is one of the foundation courses in Computer Science. Essentially, it includes concepts such as stacks, queues, graphs, algorithm analysis, sorting algorithms, linked lists, and set implementations (binary search trees and hash tables). While it is true that data structures and algorithms are difficult to master, it is not an impossible feat. The key to learning these tricky concepts is to follow the right approach, and that’s what we’re going to show you in this post.

1. Data Structures & Algorithms helps in cracking product based company interviews

All product-based company recruitment drives to test the problem-solving skills of the candidate. This is essential because the problems that such companies come across daily are quite huge and complex, and they want to hire people who will be able to solve these tasks within minimal time and the least number of resources. Advanced knowledge of Data Structures and Algorithms is a clear indicator of the person’s abilities in solving complex problems in minimal time. 

Hence, such companies directly hire people who are good Data structure and algorithms. The first level of any company, that is the online test round consists of questions revolving around various data structures. Even in startups along with app/web development, DSA is extremely important. The startups initially check for basic problem-solving skills, then move on to develop skills along with data structures and algorithm design testing. 

2. Learning Data Structures helps in writing optimised code

Writing optimised code is extremely useful in companies. It is very critical for companies to hire people who can write the most optimised code as it helps in limiting the resource use of the companies. So, get in the habit of writing the most optimised solution in terms of time and space. Choose the data structure that is the best fit and require the least space and the algorithm that executes the code in the least time. 

3. Designing Algorithms

Most of the important work in the industry revolves around developing algorithms. It is important to develop the algorithm in the most optimized way possible which can only be achieved by someone who has a sound knowledge of all the major data structures and algorithms.  For instance, the job sequencing problem can be solved by various means, such as brute force method, greedy programming, and dynamic programming but we need to pick the most optimized algorithm. 

In the job sequencing problem, the main objective is to find the sequence of jobs, which is completed within their deadlines and return a maximum profit. For example:

A simple solution is to generate all subsets of the given set of jobs and check individual subsets for the feasibility of jobs in that subset. Keep track of maximum profit among all feasible subsets. This approach will give the correct solution but with greater time complexity

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To optimize it further we can use the greedy method:

  1. Sort all the jobs in decreasing order of profit.
  2. Initialize the result as the first job in sorted jobs.
  3. Do the following for the remaining n-1 jobs.
  4. If the current job can fit in the current result sequence without missing the deadline, add the current job to the result. Else ignore the current job and move further.

4. Learning Advanced Skills

One can grasp a good hold on development skills like web development, app development, machine learning etc only when the basics are clear. It is necessary to know the backend details of every technology, every code and function. For instance, if your Facebook feed is taking too long to update, you would definitely not feel like using that app. Hence it is very important for programmers to write optimised code that loads within microseconds. In machine learning, if the data is being fetched quickly then it’s the programmer behind it who has chosen an appropriate data structure. 

5. Strong Foundation in Data Structures helps you crack interviews

If the Data Structures and Algorithm base is strong, then crack switching companies for better opportunities becomes easier for you. You can simply apply for the role, practice and revisit the concepts within 15 days and appear for the interview because no matter what domain you’ve been working for or how many years of experience you hold, DSA skills are always going to be tested.

Any popular language can be used for this as Google provides supports for Java, CPP, C, Python, Golang, JavaScript, etc, and many more.

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Frequently Asked Questions

Where to practice Data structure and algorithms?

Follow anyone online platform. CodeStudio, Codechef and Leetcode are few platforms you can refer to. Make sure to start from basic data structures like an array, hashmap and then gradually move to advance data structure like tree and graph. The right pattern to follow while practising is:
1. Arrays
2. Strings
3. Linked list
4. Queue
5. Tree
6. Hashmap
7. Graph 
8. Recursion
9. Dynamic Programming

What are the popular algorithm combinations that I can practice?

Some popular algorithm combinations to practice include the following:
1. Depth First Search
2. Recursion + Memorisation
3. Hash Table + Linked List combination
4. Binary search tree modifications

What is the ideal number of problems one should practice to master data structures?

Ideally, 30 questions per topic is sufficient to attain a good grasp in the subject. The ideal flow of course should be:
1. 10 easy problems
2. 15 medium level
3. 5 difficult problems

Which is the best language to practice Data Structures & Algorithms?

C++ and Java is highly recommended and highly preferred by the recruiters. But since language has very little to do with Data structure, you can pick any language of your choice but make sure it is an OOP language. Head over here for further details. 

Conclusion

It is relatively easy to learn new programming languages while using them in projects. However, it is not the case with DSA as it needs extensive practice and holds utmost importance.

If you are not well versed with DSA, you will find it challenging to optimize the code solution for a problem. The extensive uses of DSA have made it crucial for all budding programmers to start learning it. It is also important for the employees already working in DSA to upskill themselves to keep abeam of the advancements in it.

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