Important topics of Object Oriented Programming System
1. Classes and Objects:
-> Basics of defining classes and creating objects.
-> Class members: attributes (properties) and methods (functions).
2. Inheritance:
-> Creating a new class by inheriting properties and methods from an existing class.
-> Superclasses (base classes) and subclasses (derived classes).
3. Polymorphism:
-> Ability to take multiple forms.
-> Method overriding and method overloading.
4. Encapsulation:
-> Hiding the internal details of a class and providing a controlled interface.
-> Access modifiers: public, private, protected.
5. Abstraction:
-> Simplifying complex reality by modeling classes based on real-world entities.
-> Abstract classes and interfaces.
6. Constructors and Destructors:
-> Special methods for initializing and cleaning up objects.
-> Constructor overloading.
7. Method Access and Modifiers:
-> Public, private, protected, and package-private access modifiers.
-> Static methods and variables.
A few advanced topics :-
Composition and Aggregation:
Combining objects to create more complex structures.
Has-a and Is-a relationships.
Object Relationships:
Association, aggregation, and composition.
One-to-one, one-to-many, and many-to-many relationships.
Interfaces:
Defining contracts that classes must adhere to.
Multiple interface implementation.
Polymorphic Behavior:
Achieving flexibility through polymorphism.
Method overriding and dynamic method binding.
Inheritance vs. Composition:
Comparing and choosing between inheritance and object composition.
Design Patterns:
Common solutions to recurring design problems.
Examples: Singleton, Factory, Observer, etc.
Exception Handling:
Handling errors and exceptions gracefully in OOP.
Try-catch blocks.
Object Serialization:
Converting objects into a format suitable for storage or transmission.
Reading and writing objects to/from files.
Garbage Collection:
Automatic memory management to reclaim unused memory.
Mark and sweep, reference counting, and generations.
UML (Unified Modeling Language):
A visual language for modeling software systems.
Class diagrams, sequence diagrams, and use cases.
Method Overriding vs. Method Overloading:
Understanding the differences between these two concepts.
Abstract Classes vs. Interfaces:
Comparing and contrasting abstract classes and interfaces in OOP.
Encapsulation Benefits:
Discussing the advantages of encapsulation, such as data protection and code organization.
P.S - These are just the name of topics which you should be aware of. You can get enough articles on every topic just on a Google search.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
1. Classes and Objects:
-> Basics of defining classes and creating objects.
-> Class members: attributes (properties) and methods (functions).
2. Inheritance:
-> Creating a new class by inheriting properties and methods from an existing class.
-> Superclasses (base classes) and subclasses (derived classes).
3. Polymorphism:
-> Ability to take multiple forms.
-> Method overriding and method overloading.
4. Encapsulation:
-> Hiding the internal details of a class and providing a controlled interface.
-> Access modifiers: public, private, protected.
5. Abstraction:
-> Simplifying complex reality by modeling classes based on real-world entities.
-> Abstract classes and interfaces.
6. Constructors and Destructors:
-> Special methods for initializing and cleaning up objects.
-> Constructor overloading.
7. Method Access and Modifiers:
-> Public, private, protected, and package-private access modifiers.
-> Static methods and variables.
A few advanced topics :-
Composition and Aggregation:
Combining objects to create more complex structures.
Has-a and Is-a relationships.
Object Relationships:
Association, aggregation, and composition.
One-to-one, one-to-many, and many-to-many relationships.
Interfaces:
Defining contracts that classes must adhere to.
Multiple interface implementation.
Polymorphic Behavior:
Achieving flexibility through polymorphism.
Method overriding and dynamic method binding.
Inheritance vs. Composition:
Comparing and choosing between inheritance and object composition.
Design Patterns:
Common solutions to recurring design problems.
Examples: Singleton, Factory, Observer, etc.
Exception Handling:
Handling errors and exceptions gracefully in OOP.
Try-catch blocks.
Object Serialization:
Converting objects into a format suitable for storage or transmission.
Reading and writing objects to/from files.
Garbage Collection:
Automatic memory management to reclaim unused memory.
Mark and sweep, reference counting, and generations.
UML (Unified Modeling Language):
A visual language for modeling software systems.
Class diagrams, sequence diagrams, and use cases.
Method Overriding vs. Method Overloading:
Understanding the differences between these two concepts.
Abstract Classes vs. Interfaces:
Comparing and contrasting abstract classes and interfaces in OOP.
Encapsulation Benefits:
Discussing the advantages of encapsulation, such as data protection and code organization.
P.S - These are just the name of topics which you should be aware of. You can get enough articles on every topic just on a Google search.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best 👍👍
❤2👍2
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Eligibility:- Students ,Freshers & Working Professionals
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Learn Top Career Opportunities In The Data Science Industry
Become a Successful Data Scientist In Top MNCs
Eligibility:- Students ,Freshers & Working Professionals
📅 Date & Time:- November 23, 2024, at 7 PM
🎟️ 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 𝐟𝐨𝐫 𝐅𝐑𝐄𝐄👇:
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👍3
Junior Developer:
- I will never learn if I rely on Chatgpt. Maybe I can try writing this code on my own?
Mid Level Developer
- I'll only turn to chatGPT when I'm really stuck
Senior Developer
- How many R's are in the word strawberry?
- I will never learn if I rely on Chatgpt. Maybe I can try writing this code on my own?
Mid Level Developer
- I'll only turn to chatGPT when I'm really stuck
Senior Developer
- How many R's are in the word strawberry?
👍14
Here is how you can explain your project in an interview
When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:
➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.
➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got. This helps show the project was a success and highlights your contribution.
➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- If you worked with a team, talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?
➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. How did it help you grow professionally? What new skills did you gain, and what would you do differently next time?
➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- Know why you chose the project, what your role was, what decisions you made, and how the results compared to what you expected.
- Be clear on the scope of the project whether it was a long-term effort or a quick task.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.
Remember, 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆. You might have done great work, but if you don’t explain it well, it’s hard for the interviewer to understand your impact. So, practice explaining your projects with clarity.
When you’re in an interview, it’s super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:
➤ 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
➤ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
➤ 𝗣𝗿𝗼𝗽𝗼𝘀𝗲𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
➤ 𝗬𝗼𝘂𝗿 𝗥𝗼𝗹𝗲:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure it’s clear whether you were leading the project, a key player, or supporting the team.
➤ 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
➤ 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got. This helps show the project was a success and highlights your contribution.
➤ 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:
- If you worked with a team, talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the team’s success?
➤ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁:
- Reflect on what you learned from the project. How did it help you grow professionally? What new skills did you gain, and what would you do differently next time?
➤ 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- Know why you chose the project, what your role was, what decisions you made, and how the results compared to what you expected.
- Be clear on the scope of the project whether it was a long-term effort or a quick task.
- If there’s a pause after you describe the project, don’t hesitate to ask if they’d like more details or if there’s a specific part they’re interested in.
Remember, 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆. You might have done great work, but if you don’t explain it well, it’s hard for the interviewer to understand your impact. So, practice explaining your projects with clarity.
👍12❤3👏1
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Role :- Data Analyst
Job Location:- Bangalore/Hyderabad
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For 0 To 2 Years👇 :-
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❤1
Leetcode patterns you should definitely checkout to Learn DSA(Java) from scratch
1️⃣ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
• Search in Rotated Sorted Array (Problem #33)
• Product of Array Except Self (Problem #238)
• Find the Missing Number (Problem #268)
2️⃣Two Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
• Trapping Rain Water (Problem #42)
• Longest Substring Without Repeating Characters (Problem #3)
• Squares of a Sorted Array (Problem #977)
3️⃣In-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
• Remove Nth Node From End of List (Problem #19)
• Reorder List (Problem #143)
4️⃣Fast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
• Happy Number (Problem #202)
• Subarray Sum Equals K (Problem #560)
• Intersection of Two Linked Lists (Problem #160)
5️⃣Merge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
• Non-overlapping Intervals (Problem #435)
• Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://news.1rj.ru/str/crackingthecodinginterview
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
1️⃣ Arrays: Data structures, such as arrays, store elements in contiguous memory locations. They are versatile and useful for a wide variety of purposes.
LeetCode Problems:
• Search in Rotated Sorted Array (Problem #33)
• Product of Array Except Self (Problem #238)
• Find the Missing Number (Problem #268)
2️⃣Two Pointers: In Two Pointers, two pointers are maintained in the collection and can be manipulated to solve a problem efficiently.
LeetCode problems:
• Trapping Rain Water (Problem #42)
• Longest Substring Without Repeating Characters (Problem #3)
• Squares of a Sorted Array (Problem #977)
3️⃣In-place Linked List Traversal: As an explanation, in-place traversal is a technique for modifying linked list nodes without using extra space.
LeetCode Problems:
• Remove Nth Node From End of List (Problem #19)
• Reorder List (Problem #143)
4️⃣Fast & Slow Pointers: This pattern uses two pointers to traverse a sequence at different speeds (fast and slow), often used to detect cycles or find a specific position in the sequence.
LeetCode Problems:
• Happy Number (Problem #202)
• Subarray Sum Equals K (Problem #560)
• Intersection of Two Linked Lists (Problem #160)
5️⃣Merge Intervals: This pattern involves merging overlapping intervals in a collection, often used in problems dealing with intervals or ranges.
LeetCode problems:
• Non-overlapping Intervals (Problem #435)
• Minimum Number of Arrows to Burst Balloons (Problem #452)
Join for more: https://news.1rj.ru/str/crackingthecodinginterview
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
👍4
Tips for Google Interview Preparation
Now that we know all about the hiring process of Google, here are a few tips which you can use to crack Google’s interview and get a job.
Understand the work culture at Google well - It is always good to understand how the company works and what are the things that are expected out of an employee at Google. This shows that you are really interested in working at Google and leaves a good impression on the interviewer as well.
Be Thorough with Data Structures and Algorithms - At Google, there is always an appreciation for good problem solvers. If you want to have a good impression on the interviewers, the best way is to prove that you have worked a lot on developing your logic structures and solving algorithmic problems. A good understanding of Data Structures and Algorithms and having one or two good projects always earn you brownie points with Amazon.
Use the STAR method to format your Response - STAR is an acronym for Situation, Task, Action, and Result. The STAR method is a structured way to respond to behavioral based interview questions. To answer a provided question using the STAR method, you start by describing the situation that was at hand, the Task which needed to be done, the action taken by you as a response to the Task, and finally the Result of the experience. It is important to think about all the details and recall everyone and everything that was involved in the situation. Let the interviewer know how much of an impact that experience had on your life and in the lives of all others who were involved. It is always a good practice to be prepared with a real-life story that you can describe using the STAR method.
Know and Describe your Strengths - Many people who interview at various companies, stay shy during the interviews and feel uncomfortable when they are asked to describe their strengths. Remember that if you do not show how good you are at the skills you know, no one will ever be able to know about the same and this might just cost you a lot. So it is okay to think about yourself and highlight your strengths properly and honestly as and when required.
Discuss with your interviewer and keep the conversation going - Remember that an interview is not a written exam and therefore even if you come up with the best of solutions for the given problems, it is not worth anything until and unless the interviewer understands what you are trying to say. Therefore, it is important to make the interviewer that he or she is also a part of the interview. Also, asking questions might always prove to be helpful during the interview.
Now that we know all about the hiring process of Google, here are a few tips which you can use to crack Google’s interview and get a job.
Understand the work culture at Google well - It is always good to understand how the company works and what are the things that are expected out of an employee at Google. This shows that you are really interested in working at Google and leaves a good impression on the interviewer as well.
Be Thorough with Data Structures and Algorithms - At Google, there is always an appreciation for good problem solvers. If you want to have a good impression on the interviewers, the best way is to prove that you have worked a lot on developing your logic structures and solving algorithmic problems. A good understanding of Data Structures and Algorithms and having one or two good projects always earn you brownie points with Amazon.
Use the STAR method to format your Response - STAR is an acronym for Situation, Task, Action, and Result. The STAR method is a structured way to respond to behavioral based interview questions. To answer a provided question using the STAR method, you start by describing the situation that was at hand, the Task which needed to be done, the action taken by you as a response to the Task, and finally the Result of the experience. It is important to think about all the details and recall everyone and everything that was involved in the situation. Let the interviewer know how much of an impact that experience had on your life and in the lives of all others who were involved. It is always a good practice to be prepared with a real-life story that you can describe using the STAR method.
Know and Describe your Strengths - Many people who interview at various companies, stay shy during the interviews and feel uncomfortable when they are asked to describe their strengths. Remember that if you do not show how good you are at the skills you know, no one will ever be able to know about the same and this might just cost you a lot. So it is okay to think about yourself and highlight your strengths properly and honestly as and when required.
Discuss with your interviewer and keep the conversation going - Remember that an interview is not a written exam and therefore even if you come up with the best of solutions for the given problems, it is not worth anything until and unless the interviewer understands what you are trying to say. Therefore, it is important to make the interviewer that he or she is also a part of the interview. Also, asking questions might always prove to be helpful during the interview.
👍4
DSA INTERVIEW QUESTIONS AND ANSWERS
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subnoscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
Credits: https://news.1rj.ru/str/free4unow_backup
All the best 👍👍
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subnoscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
You can check these resources for Coding interview Preparation
Credits: https://news.1rj.ru/str/free4unow_backup
All the best 👍👍
👍7
Object-Oriented Design (OOD) Interview
Interview-ready:
Tools and courses to help you prepare for OOD interviews.
Educative:
Interactive learning paths for mastering design patterns and OOD principles.
Codemia io
They have recently added OOAD sections with many questions like parking lot design, vending machine design and much more.
Head First Design Patterns Book:
An engaging book that simplifies complex design patterns with practical examples.
Interview-ready:
Tools and courses to help you prepare for OOD interviews.
Educative:
Interactive learning paths for mastering design patterns and OOD principles.
Codemia io
They have recently added OOAD sections with many questions like parking lot design, vending machine design and much more.
Head First Design Patterns Book:
An engaging book that simplifies complex design patterns with practical examples.
👍7
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1. Web Development ➝
◀️ https://news.1rj.ru/str/webdevcoursefree
2. CSS ➝
◀️ http://css-tricks.com
3. JavaScript ➝
◀️ http://t.me/javanoscript_courses
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9. Git and GitHub ➝
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10. Blockchain ➝
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13. English Speaking ➝
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19. Tableau ➝
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◀️ http://learn.microsoft.com/shows/java-for-beginners/
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◀️ https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019
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26. Cybersecurity ➝
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◀️ https://bit.ly/3KhPdf1
◀️ https://training.linuxfoundation.org/resources/
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◀️ http://learn.microsoft.com/training/paths/build-javanoscript-applications-typenoscript/
29. Deep Learning ➝
◀️ http://introtodeeplearning.com
30. Compiler Design ➝
◀️ http://online.stanford.edu/courses/soe-ycscs1-compilers
31. DSA ➝
◀️ http://techdevguide.withgoogle.com/paths/data-structures-and-algorithms/
32. Prompt Engineering ➝
◀️ https://www.promptingguide.ai/
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👍7❤2👏1
Mastering LinkedLists: Key Questions You Should Know
Easy:
📌 Reverse Linked List: https://lnkd.in/g7qP9-YU
📌 Merge Two Sorted Lists: https://lnkd.in/gRfC6yyF
📌 Remove Nth Node From End of List: https://lnkd.in/gGnGF75X
📌 Delete Node in a Linked List: https://lnkd.in/gqzDgFpN
📌 Palindrome Linked List: https://lnkd.in/gmEjY4gr
Medium:
📌 Add Two Numbers: https://lnkd.in/gvDxHySa
📌 Swap Nodes in Pairs: https://lnkd.in/gnhqwidB
📌 Odd Even Linked List: https://lnkd.in/gS2QpJAw
📌 Intersection of Two Linked Lists: https://lnkd.in/gpwnpK8M
📌 Rotate List: https://lnkd.in/gKg_3D34
Hard:
📌 Merge k Sorted Lists: https://lnkd.in/g7wK8H8v
📌 Reverse Nodes in k-Group: https://lnkd.in/gUNaexhD
📌 Copy List with Random Pointer: https://lnkd.in/gBWcFRKe
📌 LRU Cache: https://lnkd.in/gURyUMZK
📌 Flatten a Multilevel Doubly Linked List: https://lnkd.in/gCtgKNwn
By understanding the answers to these questions, you can build a solid foundation for solving LinkedList-related problems and tackling algorithmic challenges!
To help you discover job openings and resources, I have created a dedicated Job Posting Community. It's a great place to stay updated on the latest job opportunities!
Join Here : https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Easy:
📌 Reverse Linked List: https://lnkd.in/g7qP9-YU
📌 Merge Two Sorted Lists: https://lnkd.in/gRfC6yyF
📌 Remove Nth Node From End of List: https://lnkd.in/gGnGF75X
📌 Delete Node in a Linked List: https://lnkd.in/gqzDgFpN
📌 Palindrome Linked List: https://lnkd.in/gmEjY4gr
Medium:
📌 Add Two Numbers: https://lnkd.in/gvDxHySa
📌 Swap Nodes in Pairs: https://lnkd.in/gnhqwidB
📌 Odd Even Linked List: https://lnkd.in/gS2QpJAw
📌 Intersection of Two Linked Lists: https://lnkd.in/gpwnpK8M
📌 Rotate List: https://lnkd.in/gKg_3D34
Hard:
📌 Merge k Sorted Lists: https://lnkd.in/g7wK8H8v
📌 Reverse Nodes in k-Group: https://lnkd.in/gUNaexhD
📌 Copy List with Random Pointer: https://lnkd.in/gBWcFRKe
📌 LRU Cache: https://lnkd.in/gURyUMZK
📌 Flatten a Multilevel Doubly Linked List: https://lnkd.in/gCtgKNwn
By understanding the answers to these questions, you can build a solid foundation for solving LinkedList-related problems and tackling algorithmic challenges!
To help you discover job openings and resources, I have created a dedicated Job Posting Community. It's a great place to stay updated on the latest job opportunities!
Join Here : https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👍4
30-days learning plan to master Data Structures and Algorithms (DSA) and prepare for coding interviews.
### Week 1: Foundations and Basic Data Structures
Day 1-3: Arrays and Strings
- Topics to Cover:
- Array basics, operations (insertion, deletion, searching)
- String manipulation
- Two-pointer technique, sliding window technique
- Practice Problems:
- Two Sum
- Maximum Subarray
- Reverse a String
- Longest Substring Without Repeating Characters
Day 4-5: Linked Lists
- Topics to Cover:
- Singly linked list, doubly linked list, circular linked list
- Common operations (insertion, deletion, reversal)
- Practice Problems:
- Reverse a Linked List
- Merge Two Sorted Lists
- Remove Nth Node From End of List
Day 6-7: Stacks and Queues
- Topics to Cover:
- Stack operations (push, pop, top)
- Queue operations (enqueue, dequeue)
- Applications (expression evaluation, backtracking, breadth-first search)
- Practice Problems:
- Valid Parentheses
- Implement Stack using Queues
- Implement Queue using Stacks
### Week 2: Advanced Data Structures
Day 8-10: Trees
- Topics to Cover:
- Binary Trees, Binary Search Trees (BST)
- Tree traversal (preorder, inorder, postorder, level order)
- Practice Problems:
- Invert Binary Tree
- Validate Binary Search Tree
- Serialize and Deserialize Binary Tree
Day 11-13: Heaps and Priority Queues
- Topics to Cover:
- Binary heap (min-heap, max-heap)
- Heap operations (insert, delete, extract-min/max)
- Applications (heap sort, priority queues)
- Practice Problems:
- Kth Largest Element in an Array
- Top K Frequent Elements
- Find Median from Data Stream
Day 14: Hash Tables
- Topics to Cover:
- Hashing concept, hash functions, collision resolution (chaining, open addressing)
- Applications (caching, counting frequencies)
- Practice Problems:
- Two Sum (using hash map)
- Group Anagrams
- Subarray Sum Equals K
### Week 3: Algorithms
Day 15-17: Sorting and Searching Algorithms
- Topics to Cover:
- Sorting algorithms (quick sort, merge sort, bubble sort, insertion sort)
- Searching algorithms (binary search, linear search)
- Practice Problems:
- Merge Intervals
- Search in Rotated Sorted Array
- Sort Colors
- Find Peak Element
Day 18-20: Recursion and Backtracking
- Topics to Cover:
- Basic recursion, tail recursion
- Backtracking (N-Queens, Sudoku solver)
- Practice Problems:
- Permutations
- Combination Sum
- Subsets
- Word Search
Day 21: Divide and Conquer
- Topics to Cover:
- Basic concept, merge sort, quick sort, binary search
- Practice Problems:
- Median of Two Sorted Arrays
- Pow(x, n)
- Kth Largest Element in an Array (using divide and conquer)
- Maximum Subarray (using divide and conquer)
### Week 4: Graphs and Dynamic Programming
Day 22-24: Graphs
- Topics to Cover:
- Graph representations (adjacency list, adjacency matrix)
- Traversal algorithms (DFS, BFS)
- Shortest path algorithms (Dijkstra's, Bellman-Ford)
- Practice Problems:
- Number of Islands
Day 25-27: Dynamic Programming
- Topics to Cover:
- Basic concept, memoization, tabulation
- Common problems (knapsack, longest common subsequence)
- Practice Problems:
- Longest Increasing Subsequence
- Maximum Product Subarray
Day 28: Advanced Topics and Miscellaneous
- Topics to Cover:
- Bit manipulation
- Greedy algorithms
- Miscellaneous problems (trie, segment tree, disjoint set)
- Practice Problems:
- Single Number
- Decode Ways
- Minimum Spanning Tree
### Week 5: Review and Mock Interviews
Day 29: Review and Weakness Analysis
- Activities:
- Review topics you found difficult
- Revisit problems you struggled with
Day 30: Mock Interviews and Practice
- Activities:
- Conduct mock interviews with a friend or use online platforms
- Focus on communication and explaining your thought process
Top DSA resources to crack coding interview
👉 GeekforGeeks
👉 Leetcode
👉 DSA Steps
👉 FreeCodeCamp
👉 Coding Interviews
👉 Best DSA Resources
Join for more: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
### Week 1: Foundations and Basic Data Structures
Day 1-3: Arrays and Strings
- Topics to Cover:
- Array basics, operations (insertion, deletion, searching)
- String manipulation
- Two-pointer technique, sliding window technique
- Practice Problems:
- Two Sum
- Maximum Subarray
- Reverse a String
- Longest Substring Without Repeating Characters
Day 4-5: Linked Lists
- Topics to Cover:
- Singly linked list, doubly linked list, circular linked list
- Common operations (insertion, deletion, reversal)
- Practice Problems:
- Reverse a Linked List
- Merge Two Sorted Lists
- Remove Nth Node From End of List
Day 6-7: Stacks and Queues
- Topics to Cover:
- Stack operations (push, pop, top)
- Queue operations (enqueue, dequeue)
- Applications (expression evaluation, backtracking, breadth-first search)
- Practice Problems:
- Valid Parentheses
- Implement Stack using Queues
- Implement Queue using Stacks
### Week 2: Advanced Data Structures
Day 8-10: Trees
- Topics to Cover:
- Binary Trees, Binary Search Trees (BST)
- Tree traversal (preorder, inorder, postorder, level order)
- Practice Problems:
- Invert Binary Tree
- Validate Binary Search Tree
- Serialize and Deserialize Binary Tree
Day 11-13: Heaps and Priority Queues
- Topics to Cover:
- Binary heap (min-heap, max-heap)
- Heap operations (insert, delete, extract-min/max)
- Applications (heap sort, priority queues)
- Practice Problems:
- Kth Largest Element in an Array
- Top K Frequent Elements
- Find Median from Data Stream
Day 14: Hash Tables
- Topics to Cover:
- Hashing concept, hash functions, collision resolution (chaining, open addressing)
- Applications (caching, counting frequencies)
- Practice Problems:
- Two Sum (using hash map)
- Group Anagrams
- Subarray Sum Equals K
### Week 3: Algorithms
Day 15-17: Sorting and Searching Algorithms
- Topics to Cover:
- Sorting algorithms (quick sort, merge sort, bubble sort, insertion sort)
- Searching algorithms (binary search, linear search)
- Practice Problems:
- Merge Intervals
- Search in Rotated Sorted Array
- Sort Colors
- Find Peak Element
Day 18-20: Recursion and Backtracking
- Topics to Cover:
- Basic recursion, tail recursion
- Backtracking (N-Queens, Sudoku solver)
- Practice Problems:
- Permutations
- Combination Sum
- Subsets
- Word Search
Day 21: Divide and Conquer
- Topics to Cover:
- Basic concept, merge sort, quick sort, binary search
- Practice Problems:
- Median of Two Sorted Arrays
- Pow(x, n)
- Kth Largest Element in an Array (using divide and conquer)
- Maximum Subarray (using divide and conquer)
### Week 4: Graphs and Dynamic Programming
Day 22-24: Graphs
- Topics to Cover:
- Graph representations (adjacency list, adjacency matrix)
- Traversal algorithms (DFS, BFS)
- Shortest path algorithms (Dijkstra's, Bellman-Ford)
- Practice Problems:
- Number of Islands
Day 25-27: Dynamic Programming
- Topics to Cover:
- Basic concept, memoization, tabulation
- Common problems (knapsack, longest common subsequence)
- Practice Problems:
- Longest Increasing Subsequence
- Maximum Product Subarray
Day 28: Advanced Topics and Miscellaneous
- Topics to Cover:
- Bit manipulation
- Greedy algorithms
- Miscellaneous problems (trie, segment tree, disjoint set)
- Practice Problems:
- Single Number
- Decode Ways
- Minimum Spanning Tree
### Week 5: Review and Mock Interviews
Day 29: Review and Weakness Analysis
- Activities:
- Review topics you found difficult
- Revisit problems you struggled with
Day 30: Mock Interviews and Practice
- Activities:
- Conduct mock interviews with a friend or use online platforms
- Focus on communication and explaining your thought process
Top DSA resources to crack coding interview
👉 GeekforGeeks
👉 Leetcode
👉 DSA Steps
👉 FreeCodeCamp
👉 Coding Interviews
👉 Best DSA Resources
Join for more: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
👍11❤2
How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
👍9❤2👏1👌1
DSA question by understanding patterns
If the input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/ subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
- QuickSelect
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
𝐉𝐨𝐢𝐧 𝐭𝐡𝐢𝐬 𝐭𝐞𝐥𝐞𝐠𝐫𝐚𝐦 𝐠𝐫𝐨𝐮𝐩 𝐟𝐨𝐫 𝐩𝐫𝐞𝐦𝐢𝐮𝐦 𝐉𝐨𝐛𝐬/Notes: https://news.1rj.ru/str/getjobss
If the input array is sorted then
- Binary search
- Two pointers
If asked for all permutations/subsets then
- Backtracking
If given a tree then
- DFS
- BFS
If given a graph then
- DFS
- BFS
If given a linked list then
- Two pointers
If recursion is banned then
- Stack
If must solve in-place then
- Swap corresponding values
- Store one or more different values in the same pointer
If asked for maximum/minimum subarray/ subset/options then
- Dynamic programming
If asked for top/least K items then
- Heap
- QuickSelect
If asked for common strings then
- Map
- Trie
Else
- Map/Set for O(1) time & O(n) space
- Sort input for O(nlogn) time and O(1) space
𝐉𝐨𝐢𝐧 𝐭𝐡𝐢𝐬 𝐭𝐞𝐥𝐞𝐠𝐫𝐚𝐦 𝐠𝐫𝐨𝐮𝐩 𝐟𝐨𝐫 𝐩𝐫𝐞𝐦𝐢𝐮𝐦 𝐉𝐨𝐛𝐬/Notes: https://news.1rj.ru/str/getjobss
👍10
Step-by-Step Approach
➊ Learn One Programming Language
↓
➋ Fundamentals → Time & Space Complexity
↓
➌ Brute Force Algorithms
↓
➍ Basic Data Structures → Array, Linked List
↓
➎ Simple Search Algorithm
↓
➏ Sorting Techniques → Bubble, Selection, Insertion
↓
➐ Slightly Complex Algorithms → Recursion, DnC
↓
➑ Complex Data Structures → Stack, Queue, Tree
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
➊ Learn One Programming Language
↓
➋ Fundamentals → Time & Space Complexity
↓
➌ Brute Force Algorithms
↓
➍ Basic Data Structures → Array, Linked List
↓
➎ Simple Search Algorithm
↓
➏ Sorting Techniques → Bubble, Selection, Insertion
↓
➐ Slightly Complex Algorithms → Recursion, DnC
↓
➑ Complex Data Structures → Stack, Queue, Tree
DSA Interview Preparation Resources: https://topmate.io/coding/886874
ENJOY LEARNING 👍👍
👍7❤1🥰1
Steps to learn Data Structures and Algorithms (DSA) with Python
1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.
2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.
3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview
4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.
5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.
6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.
7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.
8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.
9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.
10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.
11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.
12. Competitive Programming: Participate in competitive programming contests. They are a great way to test your skills and improve your problem-solving abilities.
13. Stay Updated: DSA is an ever-evolving field. Stay updated with the latest trends and algorithms.
14. Contribute to Open Source: Consider contributing to open source projects. It's a great way to apply your knowledge and work on real-world code.
15. Teach Others: Teaching what you've learned to others can deepen your understanding. You can create tutorials or mentor someone.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.
2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.
3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview
4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.
5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.
6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.
7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.
8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.
9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.
10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.
11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.
12. Competitive Programming: Participate in competitive programming contests. They are a great way to test your skills and improve your problem-solving abilities.
13. Stay Updated: DSA is an ever-evolving field. Stay updated with the latest trends and algorithms.
14. Contribute to Open Source: Consider contributing to open source projects. It's a great way to apply your knowledge and work on real-world code.
15. Teach Others: Teaching what you've learned to others can deepen your understanding. You can create tutorials or mentor someone.
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
👍13❤1