Millions of programmers are looking forward to pursuing a dream career in computer science and spending 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.

Even at the start, beginners face several difficulties in learning DSA. Some of these are very common, and one should overcome them as soon as possible to succeed in coding interviews.

After six years of teaching experience, I'm sharing the top challenges in learning data structures and algorithms.

It's a common misconception that data structures and algorithms are too complex or hard to master. Unfortunately, this negative perception is often passed down to beginners.

On the other hand, DSA is so much more than just preparing for coding interviews! If you look around, you'll see that thousands of data-intensive applications rely on efficient algorithms and various data structures. Even applications that don't require algorithms directly at the application level often rely heavily on them. For example, fast networking depends on routing algorithms and efficient caching strategies are based on data structures like hash table.

So don't let negative perceptions hold you back. With the right mindset and dedication, you can learn DSA and use it to achieve your goals.

If you want to master data structures and algorithms, you need to be consistent with your practice. When you skip revision or practice for a long time, it can affect your understanding of concepts and problem-solving ability. When you try to start again, you often have to go back to the beginning.

So it's important to have a continuous learning plan that includes various activities like self-study, revision, paper problem-solving, coding practice, discussion, doubt resolution, mock interviews, and so on.

DSA topics are interdependent, and programmers often learn concepts in a complex order, sometimes trying to learn advanced concepts before mastering the fundamentals. Here are some suggestions:

- Start with linear data structures before moving on to non-linear ones.
- Learn array and linked list before studying stack and queue.
- Start with fundamentals like time and space complexity analysis, iteration, recursion, and sorting.
- Learn dynamic programming before studying greedy algorithms.
- Understand recursion before learning backtracking and dynamic programming.
- After mastering binary tree, move on to BST, heap, and trie.
- Learn graph after tree.

The idea is simple: Follow a well-designed curriculum that organizes DSA topics in the proper order with minimum dependencies.

Programmers often get trapped by tricky wording and poor explanations, causing them to repeatedly go over the same idea or skip topics without fully exploring the concepts. This creates some critical challenges:

- Waste of time!
- Lack of interest in the subject.
- The habit of memorization.

To become proficient in DSA, it is important to focus on understanding concepts and principles behind the various data structures and algorithms, rather than simply trying to memorize them. This means we should take some time to work through problems, think critically about the solutions and analyze different approaches. By developing a reason oriented approach, you will be better equipped to apply these concepts to new problems and come up with creative solutions.

Most programmers are good at programming, but the real challenge lies in 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 enough time analyzing the problem and instead jumped straight into looking for a solution. They also avoid exploring different variations and patterns that could lead to a more efficient solution.

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

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.

To improve your understanding of math topics and analysis skills, here are few tips:

- Practice math problems related to DSA. There are many online resources available that offer practice problems and explanations.
- Take a course on discrete math, which covers many of the math topics that are relevant to DSA.
- Work on coding problems and analyze their time and space complexity using big-O notation. This will help you get comfortable with the process of analyzing algorithms.
- Collaborate with others to discuss and understand different approaches of analyzing algorithms. This can be especially helpful if you have a peer or mentor who has a strong background in math.

Most programmers find it challenging to design an efficient solution during an interview while someone closely verifies their progress. As an interviewee, we are supposed to communicate our thoughts loudly and clearly to the interviewer. The idea is simple: Writing solution code and communicating simultaneously is an essential skill.

Sometimes we 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 candidate for their company who can handle several critical situations smoothly during work.

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.

In conclusion, learning data structures and algorithms can be challenging at the start. It requires hard work, consistency, and a positive attitude. By following a well-structured learning plan and practicing with mock interviews and real-world applications, you can improve your problem-solving skills and become a more competitive job candidate.

Remember: It is normal to face difficulties and doubts while learning DSA. So don't get discouraged and keep pushing forward. With dedication and determination, you can succeed in mastering these fundamental concepts and take your career to the next level.

**Enjoy learning, Enjoy algorithms!**