Top 10 difficulties in learning Data Structures and Algorithms for Interview

The data structure and algorithm provide a set of approaches to the programmer for handling the data efficiently. If the programmer does not know about data structure and algorithms, they may not write efficient and correct code for their software. It is a fundamental building block of computer science and critical for tech interviews and career growth. Let’s understand its importance from two different points of view :

Tech Interview point of view :  The algorithms and data structure can be used to test both freshers and professionals in the tech industry. During the interview process, it provides a simple approach to evaluating problem-solving skills, basic computer science knowledge, clarity of thought, and analytical skills.

Application point of view :  Data structure and algorithms are at the heart of several real-life software. If we observe closely, The Application programming interface (API) hides the implementation of DSA. A good programmer understands the API internal details, including data structure and Algorithms, and makes the correct decisions.

But starting the learning journey in DSA requires the awareness of several challenges which one needs to overcome. After more than five years of teaching experience with students and professionals, we would like to share the top 10 learning challenges in the Data structure and Algorithms.

The Popular Perception of DSA!

Due to poor learning practices, some programmers have developed wrong perceptions about the data structure and algorithms. They have a popular belief that the subject is complex and challenging to master. Unfortunately, such faith transferred to a new programmer, which could impact their motivation to learn the subject.

Some programmers believe that the subject is only relevant for the tech interview’s success. Still, the reality is much more than that :  it is a core problem-solving skill in computer science and part of several real-life applications.

Lack of Continuous Learning

Mastering data structure and algorithms require consistency. The real problem arises when we skip the practice for a long duration, affecting our conceptual depth and knowledge. When we try to start again, we need to go from scratch. A lot of programmers are struggling to maintain long-term consistency and interest.

To master the subject, in the long run ,  a programmer needs to prepare a continuous learning plan with various activities like study, revision, problem-solving on paper, coding practice, community discussion, doubt resolution, mock interview, etc.

Dependencies of the Topics in DSA

Topics in data structure and algorithms are dependent on each other, and programmers often learn concepts in a complex order. Sometimes we learn advanced topics before learning fundamentals. For example :

  • We should learn greedy algorithms after learning dynamic programming.
  • We should learn linear data structure before non-linear data structure.
  • Before learning stack and queue, we should learn array and linked list.
  • Complexity analysis, recursion, and sorting are fundamentals to start with.
  • We should learn BST, heap, and trie after learning the binary tree. Similarly, we should learn graph after tree.

The idea would be simple :  we should follow the well-designed curriculum organized in the proper order of topics with minimum dependencies.

Complex Explanation of DSA Concepts

It is one of the critical issues in learning, especially in data structure and algorithms. Programmers often get trapped in tricky words and poor explanations. They need to go through the same idea repeatedly and even sometimes they skip the topic without exploring the concept. It creates three critical challenges:

  • Wastage of precious resources :  time!
  • Insufficient subject interest.
  • The habit of complex explanations creates issues during the interview.

Memorizing Concepts and Solutions

Memorisations never help in data structure and algorithms because there are a lot of things to memorize! It is a comfort zone where one resists understanding the reason behind the idea and avoids formulating a solution/code to the new problem. The fact is that concepts or ideas are deeply interrelated, where thinking critically and logically is the best viable option. The attitude of memorization is also a barrier to connecting dots and generating new solution insights. So we should avoid memorization and follow reason oriented approach.

You can explore this blog: How to develop algorithmic thinking?

How to Think of a Solution Idea?

Most programmers are good at implementing the solution, but the real challenge is thinking about correct and efficient solutions. Sometimes they try to solve so many problems to improve this skill but still struggle to identify the correct solution. Why? The reason would be simple - they avoid exploring solution depth, ignore boundary conditions, and fail to find several solution patterns. You can explore this blog: Steps of Problem-Solving in Data Structure and Algorithms

How to Write a Correct Code?

Sometimes programmers struggle to write the correct code. If identifying a solution is an “art,” then designing a correct code is a “craft” that also requires multiple skills. Here are some critical observations :

  • Correct implementation requires focus, patience, and time.
  • It involves the practice of programming concepts, various programming errors, proper initialization, base cases, boundary cases, and most importantly — a good coding style.

Fear of Math and Logic!

Data structure and algorithms require the understanding of basic math topics like permutations, counting, summation, numbers theory, bits operations, recurrence, etc. We mainly use such topics in analyzing and comparing the efficiency of the algorithms.

Most programmers fear the analysis part unnecessarily. But most of the time, the reality is just the opposite — It’s about comparing the instruction count for a large value of input size. Little practice can help to master this skill over a long time. 80% of the code analysis is just the analytical skill of counting!

Lack of Coding Interview Skills

It isn’t easy to write working code during the interview while someone is closely verifying the progress. Even as an interviewee, we are supposed to communicate our thoughts loud and clearly to the interviewer. Writing solution code and communicating simultaneously is an essential skill, and we need to develop it with focus gradually.

Some critical skills for the interview success  are communication, collaboration, research skills, mastering behavioral interviews, etc. Sometimes we often ignore these skills and get rejected despite having good coding and problem-solving skills. The fact is  that  the interviewer is looking for a potential person for his/her company who can handle several critical situations smoothly during work.

Explore this blog: Step by Step Guide for Cracking the Coding Interview

Poor Collaboration and Doubt Resolution

There is immense importance of discussion or brainstorming in learning problem-solving in DSA. Sometimes we get stuck into critical doubts, which need an immediate solution to move forward. Even in the starting phase of coding, programmers feel unsure of what kind of question they should ask and get help. So, it would be necessary for programmers to form a group or join a community where they help each other resolve their doubts.

Final motivation!

Learning Data Structures and Algorithms can be a major learning curve for any computer science student. So, we need not worry about the small failures in problem-solving because everyone has to go through them in the starting journey. No one is perfect here, and everyone is learning and evolving! Developing an attitude of never giving up would help when something is not understandable. In simple words: Data Structure and Algorithms are life skills that one needs to master in the long run.

You can explore this blog: why should we learn data structures and algorithms?

Enjoy learning, Enjoy coding, Enjoy algorithms!

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