There can be several reasons to learn data structures and algorithms: 1) An algorithm is a technology which helps us to improve performance by a huge margin, 2) Data structures are at the core of several library functions and APIs, 3) DSA is important for cracking the coding interview for top tech companies, 4) Algorithms are beautiful.
This is a complete step by step guide to master data structures and algorithms and crack the coding interview. In this blog, we have highlighted: 1) Syllabus for coding interview 2) List of best resources for learning dsa 3) How to prepare learning and coding interview preparation plan? 4) Critical tips to prepare for a dsa interview.
Students and professionals fail to crack coding interviews due to these learning challenges: 1) Popular myths about DSA 2) Lack of continuous learning 3) Topics dependencies 4) Complex explanations 5) Memorizing solutions 6) Thinking efficient solution 7) How to write correct code? 8) Fear of math 9) Lack of interview skills 10) Poor doubt resolution.
Understanding iteration vs recursion is one of the critical ideas in data structures and algorithms. If we compare iterative vs recursive approaches, one thing is common: Repeated execution of instructions until our task is done. But there are many differences in terms of implementation, code execution, time complexity analysis, etc.
A well-designed code using data structure is just like a design of a good house. So mastering algorithms require a good understanding of data structure definition, classification, types, implementation techniques, key operations, etc. We should also explore various real-life applications to understand the use case of data structures.
For cracking the coding interview and learning problem solving with data structures and algorithms, programmers must continuously practice the steps of coding problem solving and develop an approach to write correct and efficient code in a given time. In this blog, we have explained well-defined steps for algorithm problem solving.
There could be two popular categories of problems that can be solved using dynamic programming: 1) Optimization problem: Here we need to find an optimal solution (minimum, longest, shortest, etc.) from a large solution space 2) Counting problem: Here we need to count different ways to find all occurrences of a combinatorial pattern.
This blog highlights some popular problem solving techniques for solving coding problems. Learning to apply these strategies could be one of the best milestones in mastering data structure and algorithms and cracking the coding interview. We can categorize these strategies into two categories: Iterative approach and Recursive approach.
Algorithmic thinking is a method for solving data structure and algorithms problems based on a clear definition of the steps logically and repeatedly. The best idea would be to develop this skill independently from learning programming with proper practice and visualization. This could help us learn several problem-solving strategies in coding.
Why sorting algorithms are important in data structure? There are various reasons: 1) Sorting helps us to learn both iterative and recursive problem-solving approaches, 2) Sorting is one of the best ideas for learning time complexity analysis, code optimization techniques, etc. 3) We can solve several coding problems efficiently by sorting the input data.
Competitive Programming is a coding contest involving many participants who compete to design efficient solutions to coding problems in a given time. It is one of the great activities to enhance coding, problem-solving and analytical skills. This blog is a step-by-step guide for beginners to start a competitive programming journey.
How to prepare a learning strategy to crack the coding interview? Here are some critical steps: 1) Building motivation 2) Calculating preparation time 3) Understanding coding interview syllabus 4) Identifying learning resources 5) Starting the continuous learning 6) Learning with collaboration 7) Critical progress review 8) Preparing interview strategy
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