Top Reasons for Failure in Data Structures and Algorithms

Millions of programmers are looking forward to pursuing a dream career in computer science and spends hundreds of hours learning data structures and algorithms. Unfortunately, some of them struggle to become good at it and fail to crack the interview. There could be several reasons, but one fundamental reason is a lack of motivation to learn DSA.

Even at the start, beginners face several difficulties in learning this subject. Some of these difficulties are very common, and one should overcome them as soon as possible to succeed in coding interviews. After five years of teaching experience, sharing here the top challenges in learning DSA.

Popular perception about DSA!

Due to bad learning practices, some programmers have developed wrong perceptions about data structures and algorithms. They popularly believe that the subject is complex and challenging to master. Unfortunately, such faith is transferred to beginners.

On another side, some programmers believe that DSA is only relevant for a coding interview. If we analyse reality, it is much more than that! For example, look around us. Thousands of data-intensive applications are powered by efficient algorithms and use cases of various data structures. Even applications that do not require algorithms directly at the application level…relies heavily upon algorithms.

For example: Does the application depend on fast networking? Computer networking relies heavily on routing algorithms. Does the application require an efficient caching strategy? These caching strategies are based on data structures and algorithms.

Lack of continuous learning

Mastering data structures and algorithms require consistency. The real problem arises when someone skips the practice or revision for a long time, which could affect conceptual understanding and problem-solving ability. When they try to start again, most of the time, they need to go from scratch.

To master DSA for interview, a programmer must prepare a continuous learning plan with various activities like self-study, revision, problem-solving on paper, coding practice, discussion, doubt resolution, mock interviews, etc.

Dependencies of topics

DSA topics are dependent on each other, and programmers often learn concepts in a complex order. Sometimes they try to learn advanced concepts before learning fundamentals. Here are some suggestions:

  • Learn linear data structure before learning non-linear data structures.
  • Learn array and linked list before learning stack and queue.
  • Time and space complexity analysis, iteration, recursion, and sorting are fundamentals to start.
  • Learn greedy algorithms after learning dynamic programming.
  • Learn backtracking after understanding the concept of recursion.
  • After learning the binary tree, one should learn BST, heap, and trie.
  • Learn graph after tree.

The idea would be simple: We must follow a well-designed curriculum organized in the proper order of DSA topics with minimum dependencies.

Complex explanation of concepts

This is one of the critical challenge in learning data structures and algorithms. Programmers often get trapped in tricky words and poor explanations. Sometimes they go through the same idea repeatedly and skip the topic without exploring the concept. It creates some critical challenges:

  • Wastage of time and money!
  • Poor subject interest.
  • The habit of memorisation.

Memorizing concepts and solutions

Memorisations never help in data structure and algorithms because there are so many things to memorise! The fact is that DSA concepts are deeply interrelated, where thinking critically and logically is the best viable option. The attitude of memorisation is also a barrier to connecting the dots and generating new solution insights. So one should avoid memorisation and follow reason oriented approach.

How to think of a solution idea?

Most programmers are good at programming, but the real challenge is thinking of an efficient solution approach. Sometimes they try to solve many problems but still struggle to identify an efficient solution. Why? The reason is simple: They did not spend proper time around the problem and jumped to look at the solution quickly. Even they avoid going through several variations and patterns.

How to write the correct code?

Sometimes programmers struggle to write the correct code. If identifying a solution is art, then designing the correct code is a craft. Here are some critical observations:

  • Correct implementation requires focus, patience, and time.
  • It involves the practice of programming concepts, implementation patterns, 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 math topics like permutations, counting, summation, logarithms, numbers theory, bits operations, recurrence, etc. We mainly use such topics to solve problems and analyse algorithms' efficiency.

Most programmers fear the analysis part unnecessarily. Fundamentally, It’s about calculating instruction count for a large input size. Little practice in analysing various code patterns can help to master this skill. In reality, 80% of the code analysis is just the analytical skill of counting and representing growth rate in terms of big-O notations.

Lack of coding interview skills

Most programmers find it challenging to design efficient solution during the interview while someone closely verifies 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. Sometimes we ignore these skills and get rejected despite good coding and problem-solving skills. The fact is that the interviewer is looking for a potential person for their company who can handle several critical situations smoothly during work.

Poor doubt resolution

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

Final motivation!

Learning Data Structures and Algorithms can be a major learning curve for any programmer. 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 one needs to master in the long run.

Enjoy learning, Enjoy algorithms!

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