Important Topics for DSA

1. Week 1: Foundation
- Arrays & Linked Lists: Understand how to store and manage a list of elements.
- Stacks & Queues: Learn the Last-In-First-Out (LIFO) and First-In-First-Out (FIFO) principles.
- Searching & Sorting Techniques: Master basic algorithms for finding and organizing data.

2. Week 2: Intermediate
- Trees & Graphs: Study hierarchical and network data structures.
- Hashing & Hash Tables: Learn efficient methods for data retrieval.
- Dynamic Programming: Break problems into simpler sub-problems and solve them.

3. Week 3: Advanced
- Advanced Tree & Graph Algorithms: Delve deeper into complex traversal techniques.
- Heaps & Priority Queues: Understand specialized data structures for efficient prioritization.
- Backtracking & Recursion: Tackle problems with recursive solutions.

4. Week 4: DSA Hackathon
- Participate in coding challenges to solidify your learning and apply your skills.

Few Tips for Mastering DSA

1. Start Simple: Begin with easy problems and gradually move to more complex ones.
2. Practice Regularly: Consistency is key. Solve problems daily to improve your skills.
3. Understand Concepts: Don’t just memorize algorithms. Understand how and why they work.
4. Use Resources: Take advantage of online tutorials, courses, and coding platforms.
5. Join Communities: Engage with coding communities for support, motivation, and knowledge sharing.
6. Participate in Challenges: Join hackathons and coding contests to test your skills in real scenarios.

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

DSA + DEVELOPMENT (Daily Schedule) 👨🏻‍💻

Morning:
- 9:00 AM - 10:30 AM: DSA Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: DSA Study/Review

Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest

Afternoon:
- 1:30 PM - 3:00 PM: MERN Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: MERN Development

Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest

Late Evening:
- 7:00 PM - 8:00 PM: Personal Development
- 8:00 PM - 9:00 PM: Reflect and Plan

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

Java Developer Interview ❤
It'll gonna be super helpful for YOU

𝗧𝗼𝗽𝗶𝗰 𝟭: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗹𝗼𝘄 𝗮𝗻𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
- Please tell me about your project and its architecture, Challenges faced?
- What was your role in the project? Tech Stack of project? why this stack?
- Problem you solved during the project? How collaboration within the team?
- What lessons did you learn from working on this project?
- If you could go back, what would you do differently in this project?

𝗧𝗼𝗽𝗶𝗰 𝟮: 𝗖𝗼𝗿𝗲 𝗝𝗮𝘃𝗮
- String Concepts/Hashcode- Equal Methods
- Immutability
- OOPS concepts
- Serialization
- Collection Framework
- Exception Handling
- Multithreading
- Java Memory Model
- Garbage collection

Tech Community
👉 t.me/Java_Programming_Notes

𝗧𝗼𝗽𝗶𝗰 𝟯: 𝗝𝗮𝘃𝗮-𝟴/𝗝𝗮𝘃𝗮-𝟭𝟭/𝗝𝗮𝘃𝗮𝟭𝟳
- Java 8 features
- Default/Static methods
- Lambda expression
- Functional interfaces
- Optional API
- Stream API
- Pattern matching
- Text block
- Modules

𝗧𝗼𝗽𝗶𝗰 𝟰: 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸, 𝗦𝗽𝗿𝗶𝗻𝗴-𝗕𝗼𝗼𝘁, 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗲𝘀𝘁 𝗔𝗣𝗜
- Dependency Injection/IOC, Spring MVC
- Configuration, Annotations, CRUD
- Bean, Scopes, Profiles, Bean lifecycle
- App context/Bean context
- AOP, Exception Handler, Control Advice
- Security (JWT, Oauth)
- Actuators
- WebFlux and Mono Framework
- HTTP methods
- JPA
- Microservice concepts
- Spring Cloud

𝗧𝗼𝗽𝗶𝗰 𝟱: 𝗛𝗶𝗯𝗲𝗿𝗻𝗮𝘁𝗲/𝗦𝗽𝗿𝗶𝗻𝗴-𝗱𝗮𝘁𝗮 𝗝𝗽𝗮/𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 (𝗦𝗤𝗟 𝗼𝗿 𝗡𝗼𝗦𝗤𝗟)
- JPA Repositories
- Relationship with Entities
- SQL queries on Employee department
- Queries, Highest Nth salary queries
- Relational and No-Relational DB concepts
- CRUD operations in DB
- Joins, indexing, procs, function

𝗧𝗼𝗽𝗶𝗰 𝟲: 𝗖𝗼𝗱𝗶𝗻𝗴
- DSA Related Questions
- Sorting and searching using Java API.
- Stream API coding Questions

Tech Jobs and Internships
t.me/getjobss

𝗧𝗼𝗽𝗶𝗰 𝟳: 𝗗𝗲𝘃𝗼𝗽𝘀 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗼𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀
- These types of topics are mostly asked by managers or leads who are heavily working on it, That's why they may grill you on DevOps/deployment-related tools, You should have an understanding of common tools like Jenkins, Kubernetes, Kafka, Cloud, and all.

𝗧𝗼𝗽𝗶𝗰𝘀 𝟴: 𝗕𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲
- The interviewer always wanted to ask about some design patterns, it may be Normal design patterns like singleton, factory, or observer patterns to know that you can use these in coding.

PDFs and Notes 📝
t.me/Java_Programming_Notes

Best Programming Resources: https://topmate.io/coding/886839

All the best 👍👍

𝐄𝐯𝐞𝐫𝐲 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐌𝐮𝐬𝐭 𝐊𝐧𝐨𝐰 𝐭𝐡𝐞 𝐓𝐨𝐩 𝟕 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬

An architectural pattern is a general, reusable solution to common problems in software design. It structures and organises software systems to address specific concerns like scalability, maintainability, flexibility, and efficiency.

1. Microservices Architecture:
Divides an app into small, independent services with APIs.
Example: Netflix - separate services for user management, content streaming, and recommendations.

2. Layered Architecture:
Divides an app into layers (presentation, logic, data) for specific functions.
Example: JavaEE apps - distinct layers for UI, business logic, and data access.

3. Event-Driven Architecture:
Components communicate through events for loose coupling.
Example: Airbnb uses Apache Kafka for real-time event processing like booking requests.

4. Model-View-Controller (MVC) Architecture:
Splits an app into Model (data), View (UI), and Controller (logic).
Example: Ruby on Rails apps - separation of data, interface, and user input handling.

5. Master-Slave Architecture:
One master coordinates multiple slaves' tasks.
Example: Database replication - master for writes, slaves for reads, as seen in many systems.

6. Monolithic Architecture:
Entire app bundled together as a single unit.
Example: Traditional enterprise software - all features in a single executable.

7. Service-Oriented Architecture (SOA):
App composed of reusable, loosely coupled services.
Example: Salesforce - integrated or standalone sales, support, and marketing services.

Each pattern offers unique advantages and trade-offs, depending on the project's requirements and complexities.

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

Stop blaming others when you find a bug.

Instead, focus on fixing it.

If someone introduces a bug, it is not their fault.

It is OUR fault. We made the mistake as a team. We own the code together.

The most important is to fix the problem and learn from it.

Remember that you're all in the same boat. If the boat has a hole in it, don't waste time blaming each other.

Instead, work together to fix the hole before everyone sinks.

Here are the top 16 OOP interview questions👇

1. What is the difference between a class and an object?
2. What is the difference between a static and non-static method?
3. What is the purpose of an interface in OOP?
4. Explain the 4 pillars of OOP.
5. What is the difference between a public and private constructor?
6. What is the difference between an abstract class and an interface?
7. What is the difference between a shallow copy and a deep copy?
8. What is the role of the "this" keyword in OOP?
9. What is a virtual function, and how is it implemented in OOP?
10. What is the difference between overloading and overriding a method? 11. What is an Abstract class?
12. Explain different types of constructors.
13. What is Coupling in OOP and why is it helpful?
14. What is a destructor in OOP?
15. What is a static keyword in cpp?
16. What is the difference between encapsulation and data abstraction?

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

Beginner’s Roadmap to Learn Data Structures & Algorithms

1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.

2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.

3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.

4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.

5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.

6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.

7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.

8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.

9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.

10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

25+ essential tools specifically for tech and developer students 🔖
https://t.me/AI_Best_Tools/301

How to guess the solution for DSA problems?

Yes, it is possible.

You can predict the solution for a problem by analyzing the constraints.

Curious if you need a greedy approach or a backtracking solution? Trying to decide between an O(n^3) or an O(n log n) approach? Just scroll down the LeetCode question and look at the constraints of the main element.

Wondering if you should use dynamic programming or plain recursion? Should your solution be O(n^2) or O(n)? Simply examine the constraints of the main variable.

Here's a quick guide based on the value of (n):

- If n <= 12 Time complexity can be O(n!).
- If n <= 25 Time complexity can be O(2^n).
- If n <= 100 Time complexity can be O(n^4).
- If n <= 500 Time complexity can be O(n^3).
- If n <= 10 ^ 4 Time complexity can be O(n^2).
- If n <= 10 ^ 6 Time complexity can be O(n log n).
- If n <= 10 ^ 8 Time complexity can be O(n).
- If n > 10 ^ 8 Time complexity can be O(log n) or 0(1).
- If n <= 10 ^ 9 Time complexity can be O(sqrt{n}).
- If n > 10 ^ 9 Time complexity can be O(log n) or 0(1).

Understanding these constraints can help you choose the right algorithm and improve your problem-solving efficiency.

Best DSA RESOURCES: https://topmate.io/coding/886874

All the best 👍👍

Three-Tier Architecture
__ _

Three-tier architecture is a software design pattern that separates an application into three layers: presentation, business logic, and data. This separation promotes scalability, maintainability, and flexibility.

1️⃣Presentation Layer (Client Tier)

   - Role: Manages the user interface.
   - Components: Web browsers, mobile apps.
   - Technologies: HTML, CSS, JavaScript.
  
2️⃣Business Logic Layer (Application Tier)

   - Role: Processes business logic and rules.
   - Components: Application servers.
   - Technologies: Java, .NET, Python.
  
3️⃣Data Layer (Data Tier)

   - Role: Manages data storage and retrieval.
   - Components: Database servers.
   - Technologies: SQL, NoSQL databases.