Guide to Learn and Master Data Structures and Algorithms

Many programmers recognize the importance of data structures and algorithms early in their careers because having a strong understanding of these concepts helps them grow as smart programmers. At the same time, interviewers often use DSA questions to evaluate candidates during the interview process. This provides a simple way to test problem-solving skills, coding skills, and clarity of thought. In other words, knowledge of data structures and algorithms is important not only for advancing as a programmer, but also for demonstrating your skills to potential employers.

To truly master algorithms and data structures, it's important to have proper guidance and a solid learning plan. If you're just starting your journey in DSA, you may have questions like:

  • How can I develop a long-term interest in algorithms and data structures?
  • How can I create a continuous learning plan to prepare for coding interviews?
  • What are the most important concepts and problem-solving approaches that I should focus on?
  • What are some key activities that will help me learn efficiently?
  • How can I prepare for a coding interview in a short amount of time?

If you're looking for answers to these questions, this blog is for you. By answering these questions and creating a solid plan, you'll be able to become proficient in algorithms and data structures and succeed in coding interviews.

Step 1: Developing long-term motivation

It's important to stay motivated when learning data structures and algorithms. Here are some things to consider:

  • Having the ability to design efficient algorithms is a valuable skill in the 21st century.
  • You may have ups and downs on your learning journey, but once you have a basic understanding of DSA, your problem-solving skills will improve to the next level.
  • Never give up! If you feel like giving up on a problem, remind yourself to try one more time. These moments can be defining moments in your DSA learning journey.
  • Mastering coding problem-solving is an ongoing process that involves learning from mistakes. Think of every error or bug as a new learning opportunity.

By keeping these ideas in mind, you can stay motivated and continue making progress in your DSA learning journey. Blog to explore: Why should we learn Data Structure and Algorithms?

Step 2: Understanding data structures and algorithms syllabus for coding interview

Now our first goal should be to prepare a list of essential topics in data structure and algorithm. We can also focus on real-life applications to understand the use case of algorithms.

Algorithmic and mathematical thinking

Algorithmic puzzles, Brainteaser, Counting, Summation, Numbers theory, Permutation, Mathematical induction, Recurrence relations, Bit manipulation, Probability, etc.

Important math topics for learning data structures and algorithms

Blog to explore: How to develop algorithmic thinking?

Fundamentals of programming

Data types, Variables, Operators, Expressions, Control statements, Loops, Functions, Pointers and References, Arrays, Strings, Memory management, Fundamental of OOPS, etc.

Fundamentals of algorithms

Linear data structures

Blog to explore: Introduction to Data Structures

Properties, Structure, Implementation, Basic operations, Patterns of iterative and recursive problem-solving and Real-life applications

  • Array: 1D Array, 2D Array, String, Dynamic Array
  • Linked List: Singly linked list, Doubly linked list, Circular linked list
  • StackQueue, and Dequeue

Non-linear data structures

  • Binary Tree: Properties and Structure, Recursive DFS Traversals, Iterative DFS traversal, BFS traversal, Basic operations, Patterns of problem-solving using BFS and DFS traversals, etc.
  • Binary Search Tree: Properties and Structure, BST building, Basic BST operations, BST sort, Patterns of problem-solving in BST, Self-balancing BST, Real-life applications, Comparison with Hash Table.
  • Heap: Properties and Structure, Heap building, Basic heap operations, Heap sort, Patterns of Problem solving, Real-life applications
  • Dictionary and Priority queue: Implementation using array, linked list, BST, heap (Priority queue) and Hash Table (Dictionary), time complexity comparisons of basic operations, and Real-life applications
  • Advanced-Data Structures (Properties, Structure, Basic operations, Patterns of problem-solving and Real-life applications): Trie, Segment Tree, binary indexed tree, K-Dimensional Tree, n-ary tree, suffix tree
  • Graph: Properties, Structure, Types, Adjacency matrix representation, Adjacency list representation, BFS traversal and its properties, DFS traversal and its properties, Problem-solving patterns using DFS and BFS, Topological sorting, Shortest path algorithms, Minimum spanning tree, etc.

Algorithm design techniques

  • Dynamic Programming: Time memory tradeoff, optimization and combinatorial problems, Designing recursive solution of a DP problem, Top-down approach, Bottom-up approach, Bit-masking, Problem-solving patterns, Divide and conquer vs Dynamic programming, etc.
  • Greedy Algorithms: Greedy choice property, Problem solving patterns, DP vs Greedy algorithms
  • Backtracking: Exhaustive Search, Recursive and Iterative Backtracking, Branch and Bound
  • String Algorithms: String matching and Patterns of problem solving
  • Mathematical Algorithms: Numbers theoretic algorithms, Bitwise algorithms, Randomised algorithms

Coding interview preparation

  • Continuous practice of coding questions
  • Profile and resume building
  • Preparation via mock Interview
  • Company-specific research of interview process
  • Behavioral interview preparation

Top data structures and algorithms concepts for coding interview preparation

Step 3: Collecting the best resources for learning and practice

Our next step should be to gather the best learning resources: books, blog posts, talks, PDFs, courses, etc. The internet can be a great tool for this. It's important to follow authentic sources and organize them according to the syllabus we've outlined. Here is the list of some best learning resources:

Data structures and algorithms resources for beginners

  • Book: Algorithms Unlocked by Thomas Coreman, MIT Press
  • Book: Algorithmic Puzzles by Anany and Maria Levitin, Oxford University Press
  • Course: Mathematics for computer science by MIT Open Courseware

Resources for learning basic and advanced programming concepts

  • Book: Think Python (How to Think Like a Computer Scientist)
  • Book: C++ Primer
  • Book: Introduction to Programming in Java
  • Course: Programming Paradigms by Stanford
  • Course: Programming Abstraction by Stanford
  • Course: Programming Methodology by Stanford
  • Book: Clean Code by Robert Martin

Data structures and algorithms resources for advanced learners

  • Algorithms by CLRS (Book): Complexity analysis, Divide and Conquer, Sorting, Linear Data Structures, Hash Table, Heap, BST, Graph, Dynamic Programming, Greedy Algorithms, String Algorithms
  • The Algorithm Design Manual by Steven Skiena (Book): Complexity analysis, Sorting and Searching, Linear and Non-linear Data Structures, Backtracking and Dynamic Programming
  • Algorithms by Robert Sedgewick (Book): Complexity analysis, Sorting, Stack, Queue, Priority Queue, Hash Table, Binary Search Tree, Graph, String Algorithms
  • Course: Introduction to Algorithms by MIT Open Courseware

For competitive programming

  • Project Euler
  • Codeforces

For data structures and algorithms interview

Step 4: Finding good mentors and communities

Having a good mentor can be incredibly valuable for anyone looking to improve their data structures and algorithms (DSA) skills. A good mentor should be knowledgeable about DSA concepts and can offer guidance on things like building a strong foundation in DSA fundamentals, mastering problem-solving strategies, clarifying doubts or uncertainties, and preparing for coding interviews.

In addition to working with a mentor, you might also consider forming a study group with other people who have similar goals. This can be a great way to learn from one another and support each other as you work towards your DSA goals. Finally, joining online communities or groups can be a great way to learn from the experiences of others and get help with DSA-related questions.

Step 5: Preparing a continuous learning and Interview preparation plan

We assume that any professional or student is ready to give minimum 10 hours/week strategically. So, here is a proposed time management plan that one can customize quickly based on personal learning needs.

Weekdays

  • 1 hour for reading and understanding the concept.
  • 2 hours for solving problems on paper with pseudo-code.
  • 2 hours for solving problems with well-written code.

Weekends

  • 4 hours for solving more challenging problems and reviewing solutions.
  • 1 hour for reviewing and consolidating the concepts learned during the week.

Remember, this is just a suggestion and you can adjust the schedule based on your own learning needs and preferences. The key is to find a balance that works for you and stick to it consistently.

8-Week revision and DSA interview preparation plan

This is recommended for learners who want to revise DSA concepts and sharpen problem-solving skills. Such learners should have basic problem-solving skills in data structure and algorithms and good knowledge of programming concepts.

  • Minimum estimated time = 10 x 8 = 80 Hours
  • Minimum coding problems to practise = 75
  • Concept revision and problem-solving research = 15 Hours
  • Problem-solving on paper with pseudocode = 25 Hours (at least 50 coding questions)
  • Problem-solving with coding practice = 25 Hours (at least 25 coding questions)
  • Behavioural interview preparation + Mock interviews = 15 Hours

16-Week DSA interview preparation plan

This is recommended for intermediate learners who want to learn DSA concepts and sharpen problem-solving skills. Such learners should have a basic understanding programming concepts.

  • Minimum estimated time = 10 x 16 = 160 Hours
  • Minimum coding problems to practise = 150
  • Concept learning/revision and problem-solving research = 30 Hours
  • Problem-solving on paper with pseudocode = 50 Hours (at least 100 coding questions)
  • Problem-solving with coding practice = 50 Hours (at least 50 coding questions)
  • Behavioural interview preparation + Mock interviews = 30 Hours

Step 6: Starting continuous learning activities

Concept learning and revision

It's important to learn concepts in the right order to fully understand them. One helpful tip is to create a self-note and use visual notes to organize your ideas. This makes it easier to revise later.

It might also be beneficial to have a brainstorming session with a friend or coworker to discuss each topic. And if you ever feel stuck on any DSA concepts, don't hesitate to mark them for later review or seek guidance from a mentor. Remember, the key is to keep learning and never give up!

Problem-solving research

One of the best ways to improve your DSA problem-solving skills is by identifying patterns through coding challenges. It's important to consider which problem-solving strategies are most important to learn and what the best approach is for solving a specific coding problem.

Before diving into problem-solving, we recommend taking the time to thoroughly analyze various approaches by working through 3-4 coding problems. This will help you get a better understanding of the different techniques and approaches you can use to solve coding questions.

Blog to explore: Popular Problem-Solving Approaches in Data Structures and Algorithms

Top 20 algorithms to learn data structures and algorithms

Problem-solving on paper and coding practice

Practicing your DSA problem-solving skills on paper or a whiteboard can be really helpful. Visualizing solutions, creating pseudo-codes, and outlining the steps of your algorithms can all help you design more efficient and effective solutions.

Using paper or a whiteboard is great because it allows you to easily sketch out your ideas and make changes as needed. Give it a try and see how it works for you!

Top 10 problem solving approches for cracking the coding interview

After you've designed a pseudo-code, it's time to translate it into actual code using the programming language of your choice. It's also important to thoroughly test your code using a variety of input and output data, edge cases, and boundary conditions to make sure it's working correctly.

  • Does the code produce the correct output for all possible inputs?
  • Can the code be further optimized? It's always a good idea to try to improve the efficiency of your code. If you have time, take another look at the solution and see if there's a better way to implement it.
  • How can the readability of the code be improved? Making sure your code is well-organized and easy to understand is important for you and anyone else who might need to read and maintain it in the future.

Blog to explore: Steps of problem-solving in Data Structure and Algorithms

Steps of problem solving in data structures and algorithms

Resolving critical doubts

It's totally normal to have questions and doubts when you're learning algorithms and data structures. But don't let these doubts hold you back! Use them as opportunities to deepen your understanding and take your learning to the next level. If you're struggling to resolve a doubt on your own, there are a few ways to get some help:

  • Talk to a friend or colleague about it
  • Ask a mentor or course instructor for guidance
  • Post your question in a coding or learning community
  • Make a note of the doubt and come back to it later with a fresh perspective. Sometimes, doubts may be related to concepts that you haven't learned yet, and it can be helpful to revisit them once you have more context.

Don't be afraid to ask for help. We all have to start somewhere, and seeking guidance is a great way to move forward and continue learning.

Behavioral interview preparation

Behavioral interview is an important part of the job interview process, especially for programmers who are trying to land their dream job. But you know what? A lot of programmers don't give behavioral interviews the attention they deserve, and don't do much (if any) preparation for them. Some people might even think that technical interviews are more important, but that's not true. Behavioral interviews can be just as important as technical interviews.

During a behavioral interview, the interviewer wants to know how you make decisions and handle critical situations. This is really important because it helps them figure out if you're the right fit for the company and the role. So it's crucial to put in some effort and prepare for behavioral interviews. Show them your skills and abilities, and you'll be one step closer to your dream job!

The idea of behavioral interviews can vary from company to company, depending on their values and culture. If you're preparing for a behavioral interview, here are some tips to keep in mind:

  • Practice good communication skills
  • Read the job description more than once (the more the better!)
  • Research the industry and company
  • Prepare for common questions
  • Make your sales pitch clear
  • Pay attention to your body language
  • Come up with smart questions for the interviewers
  • Be positive, authentic, and truthful in your answers
  • Stay optimistic and embrace the challenge

It's also a good idea to have at least one strong example of:

  • An interesting technical problem you solved
  • An interpersonal conflict you overcame
  • A unique execution story about a past project
  • Leadership or ownership
  • A question about the company's product/business
  • A question about the company's engineering strategy

Keep these things in mind and you'll be well on your way to acing your behavioral interview!

Interview preparation via mock interviews

If you're preparing for a coding interview, one of the best things you can do is practice with mock interviews. A great way to do this is to find a partner with similar career goals and conduct mock interviews with each other. This gives you the opportunity to get feedback on your performance and see where you can improve.

When choosing a partner, make sure they have a basic understanding of data structures and algorithms. During the mock interview, use a timer to keep the session to about 1 hour or 45 minutes, which is about the average length of a real coding interview.

To make the mock interview as realistic as possible, it's important to properly prepare your partner and follow all the necessary steps. After the mock interview, try switching roles and taking on the role of the interviewer to get some insight into what companies are looking for in a candidate. Mock interviews are not only a great way to get feedback, but they can also help you improve your body language and communication skills. Consider recording the session and reviewing the footage to see what you can work on.

Tips for learning DSA for coding interview

Here are a few tips to help you make the most of your data structure and algorithms journey:

  • Our goal should be to cover at least 80% of the syllabus, and prioritize topics based on your strengths and weaknesses. If a topic takes you longer to understand, give it more time.
  • A weekly critical review can be really helpful for tracking your progress. As you go through each topic, ask yourself: what's the next step for improvement? How's your problem-solving ability? For a more objective view, try rating each topic on a scale of 1 to 10.
  • As you learn DSA, you'll probably come up with ideas like brute force solutions, efficient solutions, boundary conditions, time and space complexity analysis, and coding style. Keep track of any unique or essential patterns you come across, and make notes if you think they'll be helpful.
  • In the beginning, it's a good idea to focus on the top 150 coding interview questions instead of trying to solve a ton of problems. Pick some famous coding interview problems and try solving them using different techniques.
  • Make an effort to visualize critical operations, implementations, and use cases of each data structure.
  • Don't be afraid to go over the same concepts and problems multiple times. This can help you uncover hidden ideas. Focus on understanding the reasoning behind things, and don't try to memorize everything.

Tips for cracking the coding interview

If you want to succeed in coding interviews, it's important to have a well-defined strategy. This will help boost your confidence and give you a competitive edge. Remember to learn from your mistakes and always try to improve! Here are some essential tips to keep in mind:

  • After you understand the problem during the interview, don't waste time trying to think about it in your head. Get your thoughts down on paper, draw visualizations, write out different test inputs and outputs, outline how you would get the given output by hand, write out pseudocode steps, and think about how you would translate it into code.
  • Don't rush to design an optimal solution at the start. If you can, start with a brute force approach and work on improving it from there.
  • Communication is key. Keep telling the interviewer how you're trying to approach the problem and discuss any challenges you're facing. Our goal should be to get all the relevant details and hints to reach an efficient solution.
  • Remember that you're working as part of a team to solve the problem. The interviewer is looking for someone who can solve problems using good collaboration skills.
  • If you get stuck, don't get discouraged. The interviewer wants to see how you think about the problem from different angles.
  • Think out loud and explain what you know about the solution idea. Explain what you thought might work and why it won't. The interviewer might give you a hint.
  • Try to apply popular problem-solving ideas or patterns relevant to the problem. This could help you design efficient solutions quickly. So many problem-solving approaches come up, again and again, during problem-solving.
  • Use descriptive variable names and a good coding style.
  • Test solution for all the edge cases.

A final motivation

Mastering data structures and algorithms is all about having a long-term vision, putting in hard work, and being consistent. Remember this quote: 'If the mind is weak, then the situation becomes a problem; if the mind is balanced, then the situation becomes a challenge. But if the mind is strong, then the situation becomes an opportunity'.

Learning and coding can be really enjoyable, so make sure to embrace the process and have fun! With the right mindset and dedication, you'll be able to succeed in your DSA learning journey and make the most of the opportunities that come your way. All the best! Enjoy learning, enjoy coding, enjoy algorithms :)

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