Top 10 Common Difficulties in Learning Data Structure and Algorithms

Why do programmers find difficulties in learning data structure and algorithms?

We all might agree that we have entered the golden age of artificial intelligence, however, no AI or machine learning project is easy to implement and comes without challenges. One of the core problems is when students wish to make a career in the tech space, they often face difficulties in learning data structure and algorithms. Without having proper knowledge of data structure and algorithms, a programmer isn't efficient enough to write the right code for its software.

Moreover, it's not just from the application point of view, but data structures and algorithms are often used to test candidates in a job interview. Interviews, in general, give data structure and algorithms problems to solve to test the candidate's problem-solving and analytical skills.

However, we have never tried to understand why programmers face this challenge of learning data structure and algorithms. Analytics Insight presents to you the most common problems of data structure and algorithms making learning difficult.

No Continuous Learning

Programming is such a field that requires continuous learning. Similar to any other professional stream, it starts from basic to advanced level. Remember, we would get stuck in complex, tricky Algebra and Statistical projects. Eventually, we would end up memorizing the solutions. In programming too, programmers would memorize the solutions. However, during interviews, interviewers ask questions and test candidates throwing real-world use cases. That's when the majority of programmers fail.

The Myth of Data Structure and Algorithms

There is a widespread notion and a myth that data structure and algorithms are a tricky subject and difficult to learn. Freshers who wish to join the programming stream, already have this notion that demotivates them to learn data structure and algorithms. However, it's not the case.

Once you start learning to apply problem-solving skills, you will be able to master and crack any interview. Nonetheless, data structure and algorithms are the core of programming and numerous real-life applications.

Black Box Problem

Black Box is a problem that is applicable in terms of applications of AI models and machine learning projects. However, it is a hurdle in terms of learning data structure and algorithms as well. Research and experts state that technologies like artificial intelligence, create negative emotions and fear of a tool or object behaving almost like a human. This is known as the uncanny valley.

Hence, programmers too, have this feeling of the uncanny valley, which plays with their mind. This can again demotivate them to learn and keep a positive attitude towards data structure and algorithms.

Need of Multiple Skills

Learning data structure and algorithms is not just about having expertise in this particular field. It is about multiple skills at one time such as understanding the problem statement, creating the right algorithms, and converting it into program code. Further, comprehending real-life applications is also crucial to have a long-lasting interest.

Overlapping Topics

Subjects in data structure and algorithms are interconnected to each other. The majority of freshers in programming tend to get muddled with subjects and sometimes learn the advanced ones before the basic ones. Here, it is always better to build a house when the foundation is strong. So, follow the standard curriculum that enlists topics in proper order.

Introduction of New Technologies and Programs

If we have a look at certain programming languages and open- source frameworks, they are quite new and fresh. For example, TensorFlow was released in February 2017 whereas PyTorch in October 2017. Web application frameworks such as Ruby and Python-based Django are 14 and 13 years respectively. Not to forget, regular updates of these frameworks. To have proper learning of data structure and algorithms, programmers have to be updated about these new technologies.

Poor Support for Resolving Doubts

While we know that it is challenging to learn data structure, programmers need certain support and guidance whenever they get stuck in problem-solving. Lack of support is also one of the reasons programmers don't know the best way to learn data structures and algorithms. We should have strategies and policies set up to help programmers know the best way to learn data structures and algorithms for interviews.

Unclear Method of Teaching

It is one of the common and significant issues to learn data structures. Educators tend to use complex methods of teaching data structures and algorithms, which programmers don't understand. Most of them skip those topics and move ahead. Educators should find out an easy way to help programmers gain interest in the topic and produce effective programmers.

Lack of Patience

While learning data structures and algorithms, programmers tend to forget that data is gathered and then fed to train the algorithms. The results of how well an algorithm is performing might take time. Most of the programmers find this entire process daunting and develop a sense of negativity. However, it's important to know, for anything, to show results, we need to be patient and trust the process.

Lack of Interview Skills

During an interview, everyone is nervous, even I have been for almost all interviews. Nonetheless, writing a code in a nervous environment can lead to mistakes. Further, programmers should be good in communication skills as well – the first thing that interviewee will take note of. Most of the programmers lack the necessary communication skills and focus only on coding skills. The best way to learn data structures and algorithms for interviews is by having both technical and communication skills.