The difference between average and professional programmers is their ability to write the most optimised code. One needs solid foundations in data structures and algorithms to develop this skill. Here's a step-by-step guide for you to become a **DSA Expert.**

But Although frameworks keep coming with time, DSA will always be around because it is the foundation of computer programming. And as a result, the bulk of developer and engineer interviews put a lot of emphasis on **DSA problems.**

getting the hang of DSA is sometimes difficult, which is probably the primary justification for why most college students skip it. In addition, there is the misconception that DSA is not necessary for development employment, which is undoubtedly untrue. You must study data structures and algorithms to become a top-notch computer programmer.

We believe we can learn anything if prepared to study anything and approach it methodically and sequentially. For this reason, we have created this step-by-step guide to clarify the procedure. If you abide by these instructions wholeheartedly, you have a good chance of becoming a **DSA Expert.**

When beginning with data structures and algorithms, you must first choose a programming language and become comfortable with its syntax and semantics.

Concentrating on the theory is crucial, but you must also put it into practice by creating codes.

Now the question is, which programming language to opt for? A programming language is essentially a means of communicating with a machine. It is essential to concentrate on one language and master it.

Most cutthroat professional programmers use C/C++ due to its unrivalled speed and extensive adoption. As a result, if you select C/C++, you will have access to various resources to help you.

You must plan out the concepts you'll learn to learn DSA. Here is a recommended sequence:

**Data Structure:**

- Arrays and lists
- 2D Arrays
- String
- Stack
- Tree
- Queue
- Matrix
- Graphs
- Linked list, etc.

**Algorithms:**

- Searching
- Sorting
- Prefix sum
- Suffix sum
- Hashing
- Greedy
- Backtracking
- Recursion
- Dynamic programming
- Tree and graph traversals

Once you've decided on the order, you can begin studying each subject separately.

We use algorithms and data structures to solve logic-only problems. Therefore, memorising the codes will not get you very far. Before picking up a pen or finger, you must focus on your imagination and visualise things.

The most straightforward technique to improve your logic is to illustrate the ideas and issues on paper. Continue with it even though it takes more time because it will get more superficial. Your mind will develop patterns as you continue to practise.

You can see that we employ data structures and algorithms in various ways in daily life. We are surrounded by them, whether reading books at a library or looking up words in a dictionary. Utilise these scenarios from real life as inspiration while tackling problems.

Writing effective and optimised code is the entire point of studying DSA. What does "optimised code" refer to?

Imagine working in a bank and having to compile a list of the top 20 savers out of a client base of 200 million. The bank sought to offer incentives and customised plans for its top 20 clients. Additionally, please remember that the statistics changed over time as additional clients made more significant investments in their accounts.

What strategy would you employ? How would you organise the data in the smallest amount of time?

That is where you must understand the complexities of time and space.

The time it takes for an algorithm to run is called the algorithm's temporal complexity. Similarly, space complexity defines how much memory or space it takes up.

When solving a problem, finding a solution that uses the least amount of time and space to operate should be your aim.

Although you might not initially provide the cleanest code for every issue, you will eventually do so.

Bruce Lee famously said, "I fear not the man who has practiced 10,000 kicks once, I fear the one who has practiced one kick 10,000 times." This applies to programming as well.

It frequently happens for students to practise 500 DSA problems but still perform poorly in interviews. This is known as the "breadth-only" approach.

Deep problem-solving abilities are tested by recruiters quickly and effectively. You need the depth-first strategy to get ready for that, even if it just requires resolving 50 as opposed to 500.

When you thoroughly analyse a problem, you can:

- Recognise the source of the issue.
- Solve problems faster.
- Determine which data structure or algorithm to use. Since you have often practised solving similar situations, write clean and optimised code.

dry-runIf you begin thoroughly resolving problems, you'll use various approaches and begin to recognise patterns. You can use the breadth strategy to fix as many problems as possible once you've thoroughly resolved many issues.

Dry runs are the process of manually running the code through each step to verify the value of the variables. It helps determine what follows for i = 1, i = 2, i = 3, and so forth.

Understanding how the code works benefits you in the long run and improves your logical reasoning, even though it may take some time and effort. Using pen and paper, it would help if you created dry run tables with changing table values.

Create dry run tables containing a row for each line the program runs to show how the values of the tables shift. You can experiment with several starting cases to learn various fundamental **DSA problems.**

That's where spaced repetition comes into the picture. You find the answers to a few problems today, but after 20 days, you accidentally forget them. How are you going to prevent that?

It's a method of learning where you go through the courses repeatedly at regular intervals.

You work on the problem today, review it after seven days, then after 14 days, then after a month, and so on.

Similar to how one increases their deadlift reps in the gym, the longer intervals will train your brain to remember information for longer.

This is where the depth-first technique shines. You will begin to see patterns as you work through these issues.

Say you used Binary Search to solve five problems. The Binary Search code can be isolated and practised since you will apply it repeatedly to similar problems.

Other widely used methods, such as depth-first and binary searches, can be used to practise this.

It's time to concentrate on the breadth method once you have mastered fixing the main challenges. Try to answer as many questions in a random order as you can.

Start researching typical interview questions online and attempt to provide the most efficient answers possible. Remember that improving at **DSA programming** should be your primary goal and not getting hired or getting interviews.

That was it! A step-by-step manual for improving **DSA in programming**. You will undoubtedly be in the lead if you stick to these recommendations.

Back to blog