Comparison of Sorting Algorithms
Comparison of Sorting Algorithms

Comparison of sorting algorithms based on different parameters helps us choose an efficient sorting approach. In this blog, we have covered these concepts: 1) What is comparison based sorting? 3) Which sorting is best in terms of time complexity? 3) How to compare sorting algorithms in terms of properties like in-place, stability, etc.?

Tips and Motivation to Master Problem Solving
Tips and Motivation to Master Problem Solving

We all want to develop problem-solving skills in math, programming, computer science, data structures and algorithms, and other fields. However, as we move forward, we may face various challenges. Sometimes we handle these situations smartly, but sometime we may get stuck. In this blog, we will present solutions and motivation to tackle some of these scenarios.

Difference Between Recursion and Iteration
Recursion vs Iteration Comparison

Understanding the difference between iteration and recursion is important for learning various solving techniques in programming. If we compare iterative and recursive approaches, one thing is common: Repeated execution of instructions until the task is done. But there are differences in terms of implementation, execution, analysis, thought process, etc.

Guide to Learn Data Structures and Algorithms for Interviews
Guide to Master DSA and Crack the Coding Interview

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.

Steps of Problem Solving in Data Structures and Algorithms
Steps of Problem Solving in Data Structures and Algorithms

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.

Top Reasons for Failure in Data Structures and Algorithms
Top Reasons for Failure in Data Structures and Algorithms

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.

Why Data Structures and Algorithms are Important?
Importance of Learning Data Structures and Algorithms

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.

Data Structure Types, Classification and Real-life Applications
What is Data Structure? Types, Classification and Applications

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.

Which Problems can be Solved Using Dynamic Programming?
Types of Problems Solved Using Dynamic Programming

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.

Problem-Solving Approaches in Data Structures and Algorithms
Popular Approaches to Solve Coding Problems in DSA

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.

How to Develop Algorithmic Thinking in DSA?
Guide to Develop Algorithmic Thinking in DSA

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 do we need to learn different sorting algorithms?
Why do We Need Sorting Algorithms?

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.

How to Get Started with Competitive Programming?
Guide for Beginners to Start Competitive Programming

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.

Step by Step Learning Plan for Coding Interview
Learning Plan for Coding Interview Success

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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