Best practices for writing SQL queries:
1- Filter Early, Aggregate Late: Apply filtering conditions in the WHERE clause early in the query, and perform aggregations in the HAVING or SELECT clauses as needed.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause before deploying the code.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of correlated subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries, it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries, It will unnecessary increase runtime. In fact some databases don't even allow you to do that.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
1- Filter Early, Aggregate Late: Apply filtering conditions in the WHERE clause early in the query, and perform aggregations in the HAVING or SELECT clauses as needed.
2- Use table aliases with columns when you are joining multiple tables.
3- Never use select *, always mention list of columns in select clause before deploying the code.
4- Add useful comments wherever you write complex logic. Avoid too many comments.
5- Use joins instead of correlated subqueries when possible for better performance.
6- Create CTEs instead of multiple sub queries, it will make your query easy to read.
7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
8- Never use order by in sub queries, It will unnecessary increase runtime. In fact some databases don't even allow you to do that.
9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.
โค2
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Choose the Right Career Path in 2026
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๐ฏ Join this FREE Career Guidance Session & find:
โ The right tech career for YOU
โ Skills companies are hiring for
โ Step-by-step roadmap to get a job
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SQL Interview Questions for 0-1 year of Experience (Asked in Top Product-Based Companies).
Sharpen your SQL skills with these real interview questions!
Q1. Customer Purchase Patterns -
You have two tables, Customers and Purchases: CREATE TABLE Customers ( customer_id INT PRIMARY KEY, customer_name VARCHAR(255) ); CREATE TABLE Purchases ( purchase_id INT PRIMARY KEY, customer_id INT, product_id INT, purchase_date DATE );
Assume necessary INSERT statements are already executed.
Write an SQL query to find the names of customers who have purchased more than 5 different products within the last month. Order the result by customer_name.
Q2. Call Log Analysis -
Suppose you have a CallLogs table: CREATE TABLE CallLogs ( log_id INT PRIMARY KEY, caller_id INT, receiver_id INT, call_start_time TIMESTAMP, call_end_time TIMESTAMP );
Assume necessary INSERT statements are already executed.
Write a query to find the average call duration per user. Include only users who have made more than 10 calls in total. Order the result by average duration descending.
Q3. Employee Project Allocation - Consider two tables, Employees and Projects:
CREATE TABLE Employees ( employee_id INT PRIMARY KEY, employee_name VARCHAR(255), department VARCHAR(255) ); CREATE TABLE Projects ( project_id INT PRIMARY KEY, lead_employee_id INT, project_name VARCHAR(255), start_date DATE, end_date DATE );
Assume necessary INSERT statements are already executed.
The goal is to write an SQL query to find the names of employees who have led more than 3 projects in the last year. The result should be ordered by the number of projects led.
Sharpen your SQL skills with these real interview questions!
Q1. Customer Purchase Patterns -
You have two tables, Customers and Purchases: CREATE TABLE Customers ( customer_id INT PRIMARY KEY, customer_name VARCHAR(255) ); CREATE TABLE Purchases ( purchase_id INT PRIMARY KEY, customer_id INT, product_id INT, purchase_date DATE );
Assume necessary INSERT statements are already executed.
Write an SQL query to find the names of customers who have purchased more than 5 different products within the last month. Order the result by customer_name.
Q2. Call Log Analysis -
Suppose you have a CallLogs table: CREATE TABLE CallLogs ( log_id INT PRIMARY KEY, caller_id INT, receiver_id INT, call_start_time TIMESTAMP, call_end_time TIMESTAMP );
Assume necessary INSERT statements are already executed.
Write a query to find the average call duration per user. Include only users who have made more than 10 calls in total. Order the result by average duration descending.
Q3. Employee Project Allocation - Consider two tables, Employees and Projects:
CREATE TABLE Employees ( employee_id INT PRIMARY KEY, employee_name VARCHAR(255), department VARCHAR(255) ); CREATE TABLE Projects ( project_id INT PRIMARY KEY, lead_employee_id INT, project_name VARCHAR(255), start_date DATE, end_date DATE );
Assume necessary INSERT statements are already executed.
The goal is to write an SQL query to find the names of employees who have led more than 3 projects in the last year. The result should be ordered by the number of projects led.
โค2๐1
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โ
Essential Programming Acronyms You Should Know ๐ป๐ง
API โ Application Programming Interface
Set of rules allowing software apps to communicate and exchange data seamlessly.
IDE โ Integrated Development Environment
Software suite combining tools like editor, debugger, and compiler for efficient coding.
OOP โ Object-Oriented Programming
Paradigm organizing code around objects and classes for reusability and modularity.
HTML โ HyperText Markup Language
Standard markup language for structuring web pages and content.
CSS โ Cascading Style Sheets
Stylesheet language defining presentation and layout of HTML documents.
SQL โ Structured Query Language
Language for managing and manipulating relational databases.
JSON โ JavaScript Object Notation
Lightweight data-interchange format easy for humans and machines to parse.
DOM โ Document Object Model
Tree-like representation of a web page's structure for dynamic manipulation.
CRUD โ Create, Read, Update, Delete
Core database operations for managing data persistence.
SDK โ Software Development Kit
Collection of tools, libraries, and docs for building on a platform.
UI โ User Interface
Point of interaction between user and software application.
UX โ User Experience
Overall feel of the interaction with a product or service.
CLI โ Command Line Interface
Text-based interface for issuing commands to software.
HTTP โ HyperText Transfer Protocol
Foundation protocol for data communication on the web.
REST โ Representational State Transfer
Architectural style for designing scalable web APIs using standard HTTP methods.
๐ฌ Tap โค๏ธ for more!
API โ Application Programming Interface
Set of rules allowing software apps to communicate and exchange data seamlessly.
IDE โ Integrated Development Environment
Software suite combining tools like editor, debugger, and compiler for efficient coding.
OOP โ Object-Oriented Programming
Paradigm organizing code around objects and classes for reusability and modularity.
HTML โ HyperText Markup Language
Standard markup language for structuring web pages and content.
CSS โ Cascading Style Sheets
Stylesheet language defining presentation and layout of HTML documents.
SQL โ Structured Query Language
Language for managing and manipulating relational databases.
JSON โ JavaScript Object Notation
Lightweight data-interchange format easy for humans and machines to parse.
DOM โ Document Object Model
Tree-like representation of a web page's structure for dynamic manipulation.
CRUD โ Create, Read, Update, Delete
Core database operations for managing data persistence.
SDK โ Software Development Kit
Collection of tools, libraries, and docs for building on a platform.
UI โ User Interface
Point of interaction between user and software application.
UX โ User Experience
Overall feel of the interaction with a product or service.
CLI โ Command Line Interface
Text-based interface for issuing commands to software.
HTTP โ HyperText Transfer Protocol
Foundation protocol for data communication on the web.
REST โ Representational State Transfer
Architectural style for designing scalable web APIs using standard HTTP methods.
๐ฌ Tap โค๏ธ for more!
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๐ฏ ๐ป Coding Interview Questions (With Answers)
๐ง 1๏ธโฃ Tell me about yourself
โ Sample Answer:
"I have 4+ years as a software engineer specializing in full-stack development and algorithms. I've built scalable systems handling 1M+ daily users at a fintech startup using MERN stack and microservices. Expert in JavaScript/Python, system design, and competitive programming (LeetCode 2000+/2800). I love writing clean, testable code and optimizing for performance under scale."
๐ 2๏ธโฃ What is the difference between a stack and a queue?
โ Answer:
A stack follows LIFO (Last In, First Out) principle with operations push (add to top) and pop (remove from top). Use cases: function call stack, undo/redo features.
A queue follows FIFO (First In, First Out) with enqueue (add to rear) and dequeue (remove from front). Use cases: breadth-first search, task scheduling, printers.
Both O(1) operations with arrays/linked lists.
๐ 3๏ธโฃ What is the difference between time complexity and space complexity?
โ Answer:
Time complexity measures how runtime grows with input size n (e.g., O(nยฒ) quadratic loops).
Space complexity measures memory usage growth (e.g., O(n) array stores all elements).
Tradeoffs exist: recursion uses stack space O(n), iteration uses O(1). Always analyze both.
๐ง 4๏ธโฃ How do you find duplicates in an array?
โ Answer:
Optimal: Hash Set O(n) time/space
๐ 5๏ธโฃ What is binary search and when would you use it?
โ Answer:
Binary search finds target in sorted array in O(log n) by repeatedly dividing search interval in half:
mid = (left + right) / 2
If arr[mid] == target return mid
If arr[mid] < target search right half
Else search left half
Use when: Data naturally sorted or sorting cost acceptable. Iterative version avoids recursion stack overflow.
๐ 6๏ธโฃ How do you reverse a linked list?
โ Answer:
Iterative O(n) solution flipping next pointers:
๐ 7๏ธโฃ What is recursion and why is the base case important?
โ Answer:
Recursion is a function calling itself with modified arguments until base case stops it. Without base case โ stack overflow.
Example Fibonacci:
๐ 8๏ธโฃ How do you merge two sorted arrays?
โ Answer:
Two-pointer technique O(n+m):
๐ง 9๏ธโฃ How do you detect a cycle in a linked list?
โ Answer:
Floyd's Tortoise & Hare: Slow moves 1 step, fast moves 2. If they meet โ cycle.
To find start: Reset slow to head, move both 1 step until meet.
Double Tap โค๏ธ For More
๐ง 1๏ธโฃ Tell me about yourself
โ Sample Answer:
"I have 4+ years as a software engineer specializing in full-stack development and algorithms. I've built scalable systems handling 1M+ daily users at a fintech startup using MERN stack and microservices. Expert in JavaScript/Python, system design, and competitive programming (LeetCode 2000+/2800). I love writing clean, testable code and optimizing for performance under scale."
๐ 2๏ธโฃ What is the difference between a stack and a queue?
โ Answer:
A stack follows LIFO (Last In, First Out) principle with operations push (add to top) and pop (remove from top). Use cases: function call stack, undo/redo features.
A queue follows FIFO (First In, First Out) with enqueue (add to rear) and dequeue (remove from front). Use cases: breadth-first search, task scheduling, printers.
Both O(1) operations with arrays/linked lists.
๐ 3๏ธโฃ What is the difference between time complexity and space complexity?
โ Answer:
Time complexity measures how runtime grows with input size n (e.g., O(nยฒ) quadratic loops).
Space complexity measures memory usage growth (e.g., O(n) array stores all elements).
Tradeoffs exist: recursion uses stack space O(n), iteration uses O(1). Always analyze both.
๐ง 4๏ธโฃ How do you find duplicates in an array?
โ Answer:
Optimal: Hash Set O(n) time/space
function findDuplicates(arr) {
const seen = new Set();
const dups = new Set();
for (let num of arr) {
if (seen.has(num)) dups.add(num);
else seen.add(num);
}
return Array.from(dups);
}
Space optimized: Sort O(n log n) then scan adjacent equals.๐ 5๏ธโฃ What is binary search and when would you use it?
โ Answer:
Binary search finds target in sorted array in O(log n) by repeatedly dividing search interval in half:
mid = (left + right) / 2
If arr[mid] == target return mid
If arr[mid] < target search right half
Else search left half
Use when: Data naturally sorted or sorting cost acceptable. Iterative version avoids recursion stack overflow.
๐ 6๏ธโฃ How do you reverse a linked list?
โ Answer:
Iterative O(n) solution flipping next pointers:
function reverseList(head) {
let prev = null, curr = head;
while (curr) {
let nextTemp = curr.next;
curr.next = prev;
prev = curr;
curr = nextTemp;
}
return prev;
}
Recursive: reverseList(curr.next).then(curr.next.prev = curr, curr.next = null).๐ 7๏ธโฃ What is recursion and why is the base case important?
โ Answer:
Recursion is a function calling itself with modified arguments until base case stops it. Without base case โ stack overflow.
Example Fibonacci:
function fib(n) {
if (n <= 1) return n; // Base case
return fib(n-1) + fib(n-2);
}
Memoization optimizes overlapping subproblems.๐ 8๏ธโฃ How do you merge two sorted arrays?
โ Answer:
Two-pointer technique O(n+m):
function mergeSorted(a1, a2) {
let i=0, j=0, result = [];
while (i < a1.length && j < a2.length) {
if (a1[i] < a2[j]) result.push(a1[i++]);
else result.push(a2[j++]);
}
return result.concat(a1.slice(i)).concat(a2.slice(j));
}
Handles unequal lengths cleanly.๐ง 9๏ธโฃ How do you detect a cycle in a linked list?
โ Answer:
Floyd's Tortoise & Hare: Slow moves 1 step, fast moves 2. If they meet โ cycle.
To find start: Reset slow to head, move both 1 step until meet.
function hasCycle(head) {
let slow = head, fast = head;
while (fast && fast.next) {
slow = slow.next;
fast = fast.next.next;
if (slow === fast) return true;
}
return false;
}
Double Tap โค๏ธ For More
โค6
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๐ฅ A-Z Backend Development Roadmap ๐ฅ๏ธ๐ง
1. Internet & HTTP Basics ๐
- How the web works (client-server model)
- HTTP methods (GET, POST, PUT, DELETE)
- Status codes
- RESTful principles
2. Programming Language (Pick One) ๐ป
- JavaScript (Node.js)
- Python (Flask/Django)
- Java (Spring Boot)
- PHP (Laravel)
- Ruby (Rails)
3. Package Managers ๐ฆ
- npm (Node.js)
- pip (Python)
- Maven/Gradle (Java)
4. Databases ๐๏ธ
- SQL: PostgreSQL, MySQL
- NoSQL: MongoDB, Redis
- CRUD operations
- Joins, Indexing, Normalization
5. ORMs (Object Relational Mapping) ๐
- Sequelize (Node.js)
- SQLAlchemy (Python)
- Hibernate (Java)
- Mongoose (MongoDB)
6. Authentication & Authorization ๐
- Session vs JWT
- OAuth 2.0
- Role-based access
- Passport.js / Firebase Auth / Auth0
7. APIs & Web Services ๐ก
- REST API design
- GraphQL basics
- API documentation (Swagger, Postman)
8. Server & Frameworks ๐
- Node.js with Express.js
- Django or Flask
- Spring Boot
- NestJS
9. File Handling & Uploads ๐
- File system basics
- Multer (Node.js), Django Media
10. Error Handling & Logging ๐
- Try/catch, middleware errors
- Winston, Morgan (Node.js)
- Sentry, LogRocket
11. Testing & Debugging ๐งช
- Unit testing (Jest, Mocha, PyTest)
- Postman for API testing
- Debuggers
12. Real-Time Communication ๐ฌ
- WebSockets
- Socket.io (Node.js)
- Pub/Sub Models
13. Caching โก
- Redis
- In-memory caching
- CDN basics
14. Queues & Background Jobs โณ
- RabbitMQ, Bull, Celery
- Asynchronous task handling
15. Security Best Practices ๐ก๏ธ
- Input validation
- Rate limiting
- HTTPS, CORS
- SQL injection prevention
16. CI/CD & DevOps Basics โ๏ธ
- GitHub Actions, GitLab CI
- Docker basics
- Environment variables
- .env and config management
17. Cloud & Deployment โ๏ธ
- Vercel, Render, Railway
- AWS (EC2, S3, RDS)
- Heroku, DigitalOcean
18. Documentation & Code Quality ๐
- Clean code practices
- Commenting & README.md
- Swagger/OpenAPI
19. Project Ideas ๐ก
- Blog backend
- RESTful API for a todo app
- Authentication system
- E-commerce backend
- File upload service
- Chat server
20. Interview Prep ๐งโ๐ป
- System design basics
- DB schema design
- REST vs GraphQL
- Real-world scenarios
๐ Top Resources to Learn Backend Development ๐
โข MDN Web Docs
โข Roadmap.sh
โข FreeCodeCamp
โข Backend Masters
โข Traversy Media โ YouTube
โข CodeWithHarry โ YouTube
๐ฌ Double Tap โฅ๏ธ For More
1. Internet & HTTP Basics ๐
- How the web works (client-server model)
- HTTP methods (GET, POST, PUT, DELETE)
- Status codes
- RESTful principles
2. Programming Language (Pick One) ๐ป
- JavaScript (Node.js)
- Python (Flask/Django)
- Java (Spring Boot)
- PHP (Laravel)
- Ruby (Rails)
3. Package Managers ๐ฆ
- npm (Node.js)
- pip (Python)
- Maven/Gradle (Java)
4. Databases ๐๏ธ
- SQL: PostgreSQL, MySQL
- NoSQL: MongoDB, Redis
- CRUD operations
- Joins, Indexing, Normalization
5. ORMs (Object Relational Mapping) ๐
- Sequelize (Node.js)
- SQLAlchemy (Python)
- Hibernate (Java)
- Mongoose (MongoDB)
6. Authentication & Authorization ๐
- Session vs JWT
- OAuth 2.0
- Role-based access
- Passport.js / Firebase Auth / Auth0
7. APIs & Web Services ๐ก
- REST API design
- GraphQL basics
- API documentation (Swagger, Postman)
8. Server & Frameworks ๐
- Node.js with Express.js
- Django or Flask
- Spring Boot
- NestJS
9. File Handling & Uploads ๐
- File system basics
- Multer (Node.js), Django Media
10. Error Handling & Logging ๐
- Try/catch, middleware errors
- Winston, Morgan (Node.js)
- Sentry, LogRocket
11. Testing & Debugging ๐งช
- Unit testing (Jest, Mocha, PyTest)
- Postman for API testing
- Debuggers
12. Real-Time Communication ๐ฌ
- WebSockets
- Socket.io (Node.js)
- Pub/Sub Models
13. Caching โก
- Redis
- In-memory caching
- CDN basics
14. Queues & Background Jobs โณ
- RabbitMQ, Bull, Celery
- Asynchronous task handling
15. Security Best Practices ๐ก๏ธ
- Input validation
- Rate limiting
- HTTPS, CORS
- SQL injection prevention
16. CI/CD & DevOps Basics โ๏ธ
- GitHub Actions, GitLab CI
- Docker basics
- Environment variables
- .env and config management
17. Cloud & Deployment โ๏ธ
- Vercel, Render, Railway
- AWS (EC2, S3, RDS)
- Heroku, DigitalOcean
18. Documentation & Code Quality ๐
- Clean code practices
- Commenting & README.md
- Swagger/OpenAPI
19. Project Ideas ๐ก
- Blog backend
- RESTful API for a todo app
- Authentication system
- E-commerce backend
- File upload service
- Chat server
20. Interview Prep ๐งโ๐ป
- System design basics
- DB schema design
- REST vs GraphQL
- Real-world scenarios
๐ Top Resources to Learn Backend Development ๐
โข MDN Web Docs
โข Roadmap.sh
โข FreeCodeCamp
โข Backend Masters
โข Traversy Media โ YouTube
โข CodeWithHarry โ YouTube
๐ฌ Double Tap โฅ๏ธ For More
โค6
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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstraโs algorithm for shortest path
- Kruskalโs and Primโs algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstraโs algorithm for shortest path
- Kruskalโs and Primโs algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
Best DSA RESOURCES: https://topmate.io/coding/886874
Credits: https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
โค4
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Freshers are getting paid 10 - 15 Lakhs by learning Data Analytics WIth AI skill
๐ Learn Data Analytics from Scratch
๐ซ AI Tools & Automation
๐ Build real world Projects for job ready portfolio
๐ E&ICT IIT Roorkee Certification Program
๐ฅDeadline :- 29th March
๐๐ฝ๐ฝ๐น๐ ๐ก๐ผ๐๐ :-
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Don't Miss This Opportunity. Get Placement Assistance With 5000+ Companies
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