Iterative and Recursive approaches are important for solving data structures and algorithms problems. An iterative approach is about repeatedly executing some code statements using a loop, and a recursive method involves solving the problem using the smaller sub-problems.
The code structure of a well-designed algorithm using data structure is just like a good house design. So learning algorithms require a good understanding of data structures properties, implementation techniques, and efficiency of critical operations. The fact is: Data Structures are the core building blocks of algorithms and real-life applications.
Programmers face these 10 challenges in learning data structure and algorithms for cracking the coding interview - 1) A lot of popular perceptions about DSA 2) Lack of continuous learning 3) Dependencies of the DSA topics 4) Complex explanation of the DSA concepts 5) Memorising Concept and Solutions 6) How to think of a solution idea? 7) How to write the correct code? 8) Fear of maths logic 9) Lack of coding interview skills 10)Poor collaboration and doubt resolution
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.
A step-by-step guide to master data structures and algorithms and crack the coding interview. This could help programmers prepare a step-by-step learning plan for the coding interview preparation. Explore and Enjoy!
There can be four reasons to learn data structures and algorithms: 1) An algorithm is a technology in itself 2) It is at the core of library functions and APIs 3) It is important for cracking the coding interview 4) Algorithms are beautiful! This blog will help you develop a long-term motivation for learning DSA.
There could be various patterns of dynamic programming problems. In practice, there are two popular categories of problems that can be solved using dynamic programming: 1) Optimization problems and 2) Counting problems.
This blog highlights some popular problem-solving strategies for solving problems in DSA. Learning to apply these strategies could be one of the best milestones for the learners in mastering data structure and algorithms. Later we will write a separate blog on each problem-solving approach. Enjoy learning, Enjoy algorithms!
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 visualisation. This could help us learn several problem-solving strategies in coding.
These are some critical reasons to study sorting algorithms: 1) It can help us to learn analysis of algorithms and various problem-solving approaches 2) Sorting can work as a problem-solving approach to solve several coding problems 3) We can learn code optimization techniques and variations in boundary conditions using sorting.
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.
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