Clear all DSA rounds,

By mastering these 20 DSA patterns

1. Fast and Slow Pointer
- Cycle detection method
- O(1) space efficiency
- Linked list problems

2. Merge Intervals
- Sort and merge
- O(n log n) complexity
- Overlapping interval handling

3. Sliding Window
- Fixed/variable window
- O(n) time optimization
- Subarray/substring problems

4. Islands (Matrix Traversal)
- DFS/BFS traversal
- Connected component detection
- 2D grid problems

5. Two Pointers
- Dual pointer strategy
- Linear time complexity
- Array/list problems

6. Cyclic Sort
- Sorting in cycles
- O(n) time complexity
- Constant space usage

7. In-place Reversal of Linked List
- Reverse without extra space
- O(n) time efficiency
- Pointer manipulation technique

8. Breadth First Search
- Level-by-level traversal
- Uses queue structure
- Shortest path problems

9. Depth First Search
- Recursive/backtracking approach
- Uses stack (or recursion)
- Tree/graph traversal

10. Two Heaps
- Max and min heaps
- Median tracking efficiently
- O(log n) insertions

11. Subsets
- Generate all subsets
- Recursive or iterative
- Backtracking or bitmasking

12. Modified Binary Search
- Search in variations
- O(log n) time
- Rotated/specialized arrays

13. Bitwise XOR
- Toggle bits operation
- O(1) space complexity
- Efficient for pairing

14. Top 'K' elements
- Use heap/quickselect
- O(n log k) time
- Efficient selection problem

15. K-way Merge
- Merge sorted lists
- Min-heap based approach
- O(n log k) complexity

16. 0/1 Knapsack (Dynamic Programming)
- Choose or skip items
- O(n * W) complexity
- Maximize value selection

17. Unbounded Knapsack (Dynamic Programming)
- Unlimited item choices
- O(n * W) complexity
- Multiple item selection

18. Topological Sort (Graphs)
- Directed acyclic graph
- Order dependency resolution
- Uses DFS or BFS

19. Monotonic Stack
- Maintain increasing/decreasing stack
- Optimized for range queries
- O(n) time complexity

20. Backtracking
- Recursive decision-making
- Explore all possibilities
- Pruning with constraints

Best DSA Resources: 👇
https://topmate.io/coding/886874

All the best 👍👍

When you're studying DSA, you probably think, "This won't be directly used in the actual work of a company, so why am I even doing this?"

And in life, where will this even come in handy? Well, it won't be useful directly, but the hard work you're putting in—sitting day and night solving questions—that habit of working hard will pay off.

It's not really about DSA, but about the effort you're willing to give that will decide which company you land your internship or placement in ❤️

C++ Programming Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to C++
| | |-- Setting Up Development Environment (IDE: Code::Blocks, Visual Studio, etc.)
| | |-- Compiling and Running C++ Programs
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators (Arithmetic, Relational, Logical, Bitwise)
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| |
| |-- Jump Statements
| | |-- Break, Continue
| | |-- Goto Statement
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments (Pass by Value, Pass by Reference)
| | |-- Return Statement
| |
| |-- Function Overloading
| | |-- Overloading Functions with Different Parameters
| |
| |-- Scope and Lifetime
| | |-- Local and Global Scope
| | |-- Static Variables
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Member Functions and Data Members
| |
| |-- Constructors and Destructors
| | |-- Constructor Types (Default, Parameterized, Copy)
| | |-- Destructor Basics
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Protected Access Specifier
| | |-- Virtual Base Class
| |
| |-- Polymorphism
| | |-- Function Overriding
| | |-- Virtual Functions and Pure Virtual Functions
| | |-- Abstract Classes
| |
| |-- Encapsulation and Abstraction
| | |-- Access Specifiers (Public, Private, Protected)
| | |-- Getters and Setters
| |
| |-- Operator Overloading
| | |-- Overloading Operators (Arithmetic, Relational, etc.)
| | |-- Friend Functions
|
|-- Advanced C++
| |-- Pointers and Dynamic Memory
| | |-- Pointer Basics
| | |-- Dynamic Memory Allocation (new, delete)
| | |-- Pointer Arithmetic
| |
| |-- References
| | |-- Reference Variables
| | |-- Passing by Reference
| |
| |-- Templates
| | |-- Function Templates
| | |-- Class Templates
| |
| |-- Exception Handling
| | |-- Try-Catch Blocks
| | |-- Throwing Exceptions
| | |-- Standard Exceptions
|
|-- Data Structures
| |-- Arrays and Strings
| | |-- One-Dimensional and Multi-Dimensional Arrays
| | |-- String Handling
| |
| |-- Linked Lists
| | |-- Singly and Doubly Linked Lists
| |
| |-- Stacks and Queues
| | |-- Stack Operations (Push, Pop, Peek)
| | |-- Queue Operations (Enqueue, Dequeue)
| |
| |-- Trees and Graphs
| | |-- Binary Trees, Binary Search Trees
| | |-- Graph Representation and Traversal (DFS, BFS)
|
|-- Standard Template Library (STL)
| |-- Containers
| | |-- Vectors, Lists, Deques
| | |-- Stacks, Queues, Priority Queues
| | |-- Sets, Maps, Unordered Maps
| |
| |-- Iterators
| | |-- Input and Output Iterators
| | |-- Forward, Bidirectional, and Random Access Iterators
| |
| |-- Algorithms
| | |-- Sorting, Searching, and Manipulation
| | |-- Numeric Algorithms
|
|-- File Handling
| |-- Streams and File I/O
| | |-- ifstream, ofstream, fstream
| | |-- Reading and Writing Files
| | |-- Binary File Handling
|
|-- Testing and Debugging
| |-- Debugging Tools
| | |-- gdb (GNU Debugger)
| | |-- Valgrind for Memory Leak Detection
| |
| |-- Unit Testing
| | |-- Google Test (gtest)
| | |-- Writing and Running Tests
|
|-- Deployment and DevOps
| |-- Version Control with Git
| | |-- Integrating C++ Projects with GitHub
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- Using Jenkins or GitHub
| |
|   |--Free courses
| | |--imp.i115008.net/kjoq9V
|   |   |--imp.i115008.net/5bmnKL
|   |   |--Microsoft Documentation
|   |   |--Udemy Course

Join @free4unow_backup for more free resources

ENJOY LEARNING 👍👍

Essential programming language for Android app development 👇👇

1. Java: Java has been the traditional and most widely used programming language for Android app development. It is the official language for Android development and provides a robust set of tools and libraries for building Android apps.

2. Kotlin: Kotlin is a modern, concise, and expressive programming language that has gained popularity among Android developers. It is fully interoperable with Java and offers many features that make Android app development more efficient and less error-prone.

3. C++: While not as commonly used as Java or Kotlin, C++ can be used for developing performance-critical parts of an Android app, such as game engines or graphics-intensive applications.

4. Python: Although not typically used for building full-fledged Android apps, Python can be used for scripting, automation, and data processing tasks in Android development.

5. JavaScript: JavaScript can be used in combination with frameworks like React Native or NativeScript to build cross-platform mobile apps that run on both Android and iOS devices.

Overall, Java and Kotlin are the most essential programming languages for Android app development, with Kotlin gaining popularity as a more modern and efficient alternative to Java.

Free Resources to learn App Development 👇👇

Developing Android Apps with Kotlin

Udemy

Android Basics in Kotlin

Advanced Android with Kotlin

Join @free4unow_backup for more free resources.

ENJOY LEARNING👍👍

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

List of most asked Programming Interview Questions.

Are you preparing for a coding interview? This tweet is for you. It contains a list of the most asked interview questions from each topic.

Arrays

- How is an array sorted using quicksort?
- How do you reverse an array?
- How do you remove duplicates from an array?
- How do you find the 2nd largest number in an unsorted integer array?

Linked Lists

- How do you find the length of a linked list?
- How do you reverse a linked list?
- How do you find the third node from the end?
- How are duplicate nodes removed in an unsorted linked list?

Strings

- How do you check if a string contains only digits?
- How can a given string be reversed?
- How do you find the first non-repeated character?
- How do you find duplicate characters in strings?

Binary Trees

- How are all leaves of a binary tree printed?
- How do you check if a tree is a binary search tree?
- How is a binary search tree implemented?
- Find the lowest common ancestor in a binary tree?

Graph

- How to detect a cycle in a directed graph?
- How to detect a cycle in an undirected graph?
- Find the total number of strongly connected components?
- Find whether a path exists between two nodes of a graph?
- Find the minimum number of swaps required to sort an array.

Dynamic Programming

1. Find the longest common subsequence?
2. Find the longest common substring?
3. Coin change problem?
4. Box stacking problem?
5. Count the number of ways to cover a distance?

20 LeetCode Coding Patterns you should follow for coding interview preparation :

1. Sliding Window
2. Two Pointers
3. Binary Search
4. Fast and Slow Pointers
5. Merge Intervals
6. Top K Elements
7. K-way Merge
8. Breadth-First Search (BFS)
9. Depth-First Search (DFS)
10. Backtracking
11. Dynamic Programming (DP)
12. Kadane's Algorithm
13. Knapsack Problem
14. Tree Depth-First Search
15. Tree Breadth-First Search
16. Topological Sort
17. Trie
18. Graph - Bipartite Check
19. Bitwise XOR
20. Sliding Window - Optimal

Mastering these patterns will give you a solid foundation to tackle a wide variety of coding problems, ranging from arrays and strings to graphs and trees.

By internalizing these patterns, you'll become more efficient at solving LeetCode problems and increase your chances of acing coding interviews.

Best DSA Resources: 👇
https://topmate.io/coding/886874

All the best 👍👍

Standing out in your career has never been harder.

It’s also never been more important.

95.5% of employers believe that standing out is important for career advancement (Forbes).

Here are 8 traits to help you stand out in your career:

1. Always Learning
↳ Keeps your skills sharp and shows you’re invested in growth.

2. Taking Initiative
↳ Demonstrates leadership potential and a proactive mindset.

3. Being Honest
↳ Builds trust with colleagues and superiors, setting you apart.

4. Staying Curious
↳ Drives new ideas and shows you’re always looking for solutions.

5. Being Dependable
↳ Establish you as a reliable person that others can count on.

6. Adapting to Change
↳ Shows resilience and the ability to thrive in dynamic environments.

7. Communicating Clearly
↳ Enhances collaboration and makes your ideas stand out.

8. Showing Empathy
↳ Builds strong connections and promotes a positive work culture.


Visibility is not an advantage anymore.

It’s a necessity.

Master it, or risk blending in.

✅Meta interview questions : Most asked in last 30 days

1. 1249. Minimum Remove to Make Valid Parentheses

2. 408. Valid Word Abbreviation

3. 215. Kth Largest Element in an Array

4. 314. Binary Tree Vertical Order Traversal

5. 88. Merge Sorted Array

6. 339. Nested List Weight Sum

7. 680. Valid Palindrome II

8. 973. K Closest Points to Origin

9. 1650. Lowest Common Ancestor of a Binary Tree III

10. 1. Two Sum

11. 791. Custom Sort String

12. 56. Merge Intervals

13. 528. Random Pick with Weight

14. 1570. Dot Product of Two Sparse Vectors

15. 50. Pow(x, n)

16. 65. Valid Number

17. 227. Basic Calculator II

18. 560. Subarray Sum Equals K

19. 71. Simplify Path

20. 200. Number of Islands

21. 236. Lowest Common Ancestor of a Binary Tree

22. 347. Top K Frequent Elements

23. 498. Diagonal Traverse

24. 543. Diameter of Binary Tree

25. 1768. Merge Strings Alternately

26. 2. Add Two Numbers

27. 4. Median of Two Sorted Arrays

28. 7. Reverse Integer

29. 31. Next Permutation

30. 34. Find First and Last Position of Element in Sorted Array

31. 84. Largest Rectangle in Histogram

32. 146. LRU Cache

33. 162. Find Peak Element

34. 199. Binary Tree Right Side View

35. 938. Range Sum of BST

36. 17. Letter Combinations of a Phone Number
37. 125. Valid Palindrome

38. 153. Find Minimum in Rotated Sorted Array

39. 283. Move Zeroes

40. 523. Continuous Subarray Sum

41. 658. Find K Closest Elements

42. 670. Maximum Swap

43. 827. Making A Large Island

44. 987. Vertical Order Traversal of a Binary Tree

45. 1757. Recyclable and Low Fat Products

46. 1762. Buildings With an Ocean View

47. 2667. Create Hello World Function

48. 5. Longest Palindromic Substring

49. 15. 3Sum

50. 19. Remove Nth Node From End of List

51. 70. Climbing Stairs

52. 80. Remove Duplicates from Sorted Array II

53. 113. Path Sum II

54. 121. Best Time to Buy and Sell Stock

55. 127. Word Ladder

56. 128. Longest Consecutive Sequence

57. 133. Clone Graph

58. 138. Copy List with Random Pointer

59. 140. Word Break II

60. 142. Linked List Cycle II

61. 145. Binary Tree Postorder Traversal

62. 173. Binary Search Tree Iterator

63. 206. Reverse Linked List

64. 207. Course Schedule

65. 394. Decode String

66. 415. Add Strings

67. 437. Path Sum III

68. 468. Validate IP Address

70. 691. Stickers to Spell Word

71. 725. Split Linked List in Parts

72. 766. Toeplitz Matrix

73. 708. Insert into a Sorted Circular Linked List

74. 1091. Shortest Path in Binary Matrix

75. 1514. Path with Maximum Probability

76. 1609. Even Odd Tree

77. 1868. Product of Two Run-Length Encoded Arrays

78. 2022. Convert 1D Array Into 2D Array

Top Coding Interview Resources to prepare for Microsoft, Amazon, Meta, Apple, Adobe, VMware, Visa, Twitter, LinkedIn, JP Morgan, Goldman Sachs, Oracle and Walmart 👇👇 https://topmate.io/coding/951517

All the best 👍👍

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://t.me/free4unow_backup

ENJOY LEARNING 👍👍

C++ Programming Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to C++
| | |-- Setting Up Development Environment (IDE: Code::Blocks, Visual Studio, etc.)
| | |-- Compiling and Running C++ Programs
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators (Arithmetic, Relational, Logical, Bitwise)
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| |
| |-- Jump Statements
| | |-- Break, Continue
| | |-- Goto Statement
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments (Pass by Value, Pass by Reference)
| | |-- Return Statement
| |
| |-- Function Overloading
| | |-- Overloading Functions with Different Parameters
| |
| |-- Scope and Lifetime
| | |-- Local and Global Scope
| | |-- Static Variables
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Member Functions and Data Members
| |
| |-- Constructors and Destructors
| | |-- Constructor Types (Default, Parameterized, Copy)
| | |-- Destructor Basics
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Protected Access Specifier
| | |-- Virtual Base Class
| |
| |-- Polymorphism
| | |-- Function Overriding
| | |-- Virtual Functions and Pure Virtual Functions
| | |-- Abstract Classes
| |
| |-- Encapsulation and Abstraction
| | |-- Access Specifiers (Public, Private, Protected)
| | |-- Getters and Setters
| |
| |-- Operator Overloading
| | |-- Overloading Operators (Arithmetic, Relational, etc.)
| | |-- Friend Functions
|
|-- Advanced C++
| |-- Pointers and Dynamic Memory
| | |-- Pointer Basics
| | |-- Dynamic Memory Allocation (new, delete)
| | |-- Pointer Arithmetic
| |
| |-- References
| | |-- Reference Variables
| | |-- Passing by Reference
| |
| |-- Templates
| | |-- Function Templates
| | |-- Class Templates
| |
| |-- Exception Handling
| | |-- Try-Catch Blocks
| | |-- Throwing Exceptions
| | |-- Standard Exceptions
|
|-- Data Structures
| |-- Arrays and Strings
| | |-- One-Dimensional and Multi-Dimensional Arrays
| | |-- String Handling
| |
| |-- Linked Lists
| | |-- Singly and Doubly Linked Lists
| |
| |-- Stacks and Queues
| | |-- Stack Operations (Push, Pop, Peek)
| | |-- Queue Operations (Enqueue, Dequeue)
| |
| |-- Trees and Graphs
| | |-- Binary Trees, Binary Search Trees
| | |-- Graph Representation and Traversal (DFS, BFS)
|
|-- Standard Template Library (STL)
| |-- Containers
| | |-- Vectors, Lists, Deques
| | |-- Stacks, Queues, Priority Queues
| | |-- Sets, Maps, Unordered Maps
| |
| |-- Iterators
| | |-- Input and Output Iterators
| | |-- Forward, Bidirectional, and Random Access Iterators
| |
| |-- Algorithms
| | |-- Sorting, Searching, and Manipulation
| | |-- Numeric Algorithms
|
|-- File Handling
| |-- Streams and File I/O
| | |-- ifstream, ofstream, fstream
| | |-- Reading and Writing Files
| | |-- Binary File Handling
|
|-- Testing and Debugging
| |-- Debugging Tools
| | |-- gdb (GNU Debugger)
| | |-- Valgrind for Memory Leak Detection
| |
| |-- Unit Testing
| | |-- Google Test (gtest)
| | |-- Writing and Running Tests
|
|-- Deployment and DevOps
| |-- Version Control with Git
| | |-- Integrating C++ Projects with GitHub
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- Using Jenkins or GitHub
| |
|   |--Free courses
| | |--imp.i115008.net/kjoq9V
|   |   |--imp.i115008.net/5bmnKL
|   |   |--Microsoft Documentation
|   |   |--Udemy Course

Join @free4unow_backup for more free resources

ENJOY LEARNING 👍👍

30-Day Roadmap to Learn Android App Development up to an Intermediate Level

Week 1: Setting the Foundation
*Day 1-2:*
- Familiarize yourself with the basics of Android development and set up Android Studio.
- Create a simple "Hello, Android!" app and run it on an emulator or a physical device.

*Day 3-4:*
- Understand the Android project structure and layout files (XML).
- Explore activities and their lifecycle in Android.

*Day 5-7:*
- Dive into user interface components like buttons, text views, and layouts.
- Build a basic interactive app with user input.

Week 2: Functionality and Navigation
*Day 8-9:*
- Study how to handle button clicks and user interactions.
- Learn about intents and navigation between activities.

*Day 10-12:*
- Explore fragments for modular UI components.
- Understand how to pass data between activities and fragments.

*Day 13-14:*
- Practice creating and using custom views.
- Build a small project involving multiple activities and fragments.

Week 3: Data Management
*Day 15-17:*
- Learn about data storage options: SharedPreferences and internal storage.
- Understand how to work with SQLite databases in Android.

*Day 18-19:*
- Study content providers and how to share data between apps.
- Practice implementing data persistence in a project.

*Day 20-21:*
- Explore background processing and AsyncTask for handling long-running tasks.
- Understand the basics of threading and handling concurrency.

Week 4: Advanced Topics
*Day 22-23:*
- Dive into handling permissions in Android apps.
- Work on projects involving file operations and reading/writing to external storage.

*Day 24-26:*
- Learn about services and background processing.
- Explore broadcast receivers and how to respond to system-wide events.

*Day 27-28:*
- Study advanced UI components like RecyclerView for efficient list displays.
- Explore Android's networking capabilities and make API requests.

*Day 29-30:*
- Delve into more advanced topics like dependency injection (e.g., Dagger).
- Explore additional libraries and frameworks relevant to your interests (e.g., Retrofit for networking, Room for database management).
- Work on a complex project that combines your knowledge from the past weeks.

Throughout the 30 days, practice coding daily, consult Android documentation, and leverage online resources for additional guidance. Adapt the roadmap based on your progress and interests. Good luck with your Android app development journey!

Free Resources: https://t.me/appsuser

When you're studying DSA, you probably think, "This won't be directly used in the actual work of a company, so why am I even doing this?"

And in life, where will this even come in handy? Well, it won't be useful directly, but the hard work you're putting in—sitting day and night solving questions—that habit of working hard will pay off.

It's not really about DSA, but about the effort you're willing to give that will decide which company you land your internship or placement in ❤️

Preparing for an Interview?

Interviews can feel nerve-wracking, but with the right preparation, you can walk in feeling confident and ready to impress.

Here’s a 10-step checklist to ensure you're all set:

📌Research the Company: Understand its values, culture, and recent news. This shows you're genuinely interested.

📌Know the Job Role: Be clear on the job description and how your skills match.

📌Prepare Your Answers: Practice responses to common interview questions, like "Tell me about yourself" or "What are your strengths?"

📌Dress the Part: Choose professional attire that suits the company culture.

📌Bring Copies of Your Resume: Even if the interviewer has a copy, having one ready shows you're organized.

📌Know Your Resume Inside Out: Be ready to discuss your experiences, achievements, and gaps in your employment history.

📌Prepare Questions to Ask: Asking insightful questions shows you're engaged and have done your homework.

📌Practice Good Body Language: Make eye contact, sit up straight, and offer a firm handshake.

📌Be On Time: Arriving 10-15 minutes early shows punctuality and respect.

📌Stay Calm and Positive: Stay relaxed, speak clearly, and showcase your enthusiasm for the role.

Follow this checklist, and you’ll be all set to ace your interview!

Top Coding Interview Resources to prepare for Microsoft, Amazon, Meta, Apple, Adobe, VMware, Visa, Twitter, LinkedIn, JP Morgan, Goldman Sachs, Oracle and Walmart 👇👇 https://topmate.io/coding/951517

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Coding is tricky. Coding in interviews feels even harder. It’s intimidating, uncertain and hard to prepare. Here are 4 ways to do it!

1. Interview Cake: I think it is some of the best prep available and it is targeted toward weaknesses many data scientists have in algorithms and data structures: https://www.interviewcake.com/

2. Leetcode: While developed for software engineering interviews, it has a LOT of useful content for learning algorithms. For data science, I'd suggest focusing on Easy/Medium: https://leetcode.com/

3. Cracking the Coding Interview: Amazing book, sometimes referred to as CTCI. A classic and one you should have: https://cin.ufpe.br/~fbma/Crack/Cracking%20the%20Coding%20Interview%20189%20Programming%20Questions%20and%20Solutions.pdf

4. Daily Coding Problem: The book and the website are awesome. Work on a daily problem. This was my go to resource for when I was looking to stay sharp: https://www.dailycodingproblem.com/

#coding

Master DSA in 160 days
👇👇
https://gfgcdn.com/tu/TX9/

This is a very good course by Geekforgeeks, designed for freshers to help them crack coding interviews.

The best part about such courses is it helps you build consistency and discipline—two key habits that not only make DSA easier but also set you up for long-term success in your career.

Like if you need similar FREE resources in the channel

ENJOY LEARNING 👍👍

10 Ways to Speed Up Your Python Code

1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
    if x % 2 == 0:
        numbers.append(x**2)

2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.

3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.

4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.

5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.

6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.

7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.

8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.

9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.

10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.

Tips for Google Interview Preparation


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.

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

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Type of problem, while solving DSA problem in Array

❗ There are many types of problems that can be solved using arrays and different techniques in Data Structures and Algorithms. Here are some common problem types and techniques that you might encounter:

𝟏. 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐰𝐢𝐧𝐝𝐨𝐰 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are given an array and a window size, and you have to find a subarray of that size that satisfies certain conditions. You can use a sliding window technique to efficiently search through the array by maintaining a current window of fixed size and updating it as you move forward.

𝟐. 𝐓𝐰𝐨 𝐩𝐨𝐢𝐧𝐭𝐞𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you use two pointers to traverse the array from both ends and find a certain pattern or condition. For example, you can use two pointers to find a pair of elements that sum up to a target value, or to reverse an array.

𝟑. 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to sort an array in a certain way, such as in ascending or descending order, or according to certain criteria such as frequency or value. You can use sorting algorithms such as merge sort or quick sort to efficiently sort the array.


𝟒. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a specific element in the array or to search for a certain pattern. You can use searching algorithms such as binary search or linear search to efficiently search through the array.

𝟓. 𝐒𝐮𝐛𝐚𝐫𝐫𝐚𝐲 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to find a contiguous subarray that satisfies certain conditions. You can use techniques such as prefix sum or Kadane's algorithm to efficiently find the subarray with the maximum sum.


𝟔. 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬: In these problems, you are asked to count the occurrences of certain elements or to count the number of subarrays or subsequences that satisfy certain conditions. You can use techniques such as hashing or dynamic programming to efficiently count the occurrences or number of subarrays.

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

All the best 👍👍

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.

You can check these resources for Coding interview Preparation

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