Forwarded from GROUP FOR PROGRAMMERS๐ฅ
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Looking to build a successful career in technology? Now is the perfect time to gain industry-recognized skills and certifications that employers value.
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Forwarded from Jobs and Internships Updates
Data Eminence is hiring Full Stack Engineer
For 2024, 2025 grads
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Forwarded from Jobs and Internships Updates
Accenture is hiring Web Developer Associate
For 2023, 2024, 2025 grads
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Forwarded from Jobs and Internships India
Infosys hiring .Net Dev
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Forwarded from Jobs and Internships India
Siemens Energy
Position: AI/Machine Learning Engineer
Qualifications: Bachelorโs Degree
Experience: Freshers
Location: Gurgaon, India
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Position: AI/Machine Learning Engineer
Qualifications: Bachelorโs Degree
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Forwarded from GROUP FOR PROGRAMMERS๐ฅ
MakeMyTrip is hiring for Project Based Internship (Marketing)
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Forwarded from GROUP FOR PROGRAMMERS๐ฅ
JB Poindexter (India) Private Limited is hiring for Jr Pricing Analyst
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Forwarded from Jobs and Internships Updates
American Express is hiring for Software Engineer I
Experience: 0-2 years
Salary: 15-25 LPA
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Experience: 0-2 years
Salary: 15-25 LPA
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Forwarded from Jobs and Internships Updates
Gainwell Technologies
Position: C Unix Developer
Qualification: Bachelorโs Degree
Experienc๏ปฟe: Experienced
Location: Remote
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Position: C Unix Developer
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Forwarded from Jobs and Internships India
๐๐ ๐๐๐๐ฅ๐ญ๐ก๐๐๐ซ๐
Position: Graduate Engineer Trainee
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Position: Graduate Engineer Trainee
Qualifications: Bachelorโs Degree
Experience: Freshers
Location: Pune, India
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Forwarded from Jobs and Internships India
Platform Science is hiring for Data Engineer
Batch: 2023, 2024, 2025
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Octave is hiring for Software Engineer
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American Express is hiring for Software Engineer I
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ACI Worldwide is hiring for Software Engineer
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fmg is hiring Software Engineer Intern (PHP & Java)
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carelon care solutions is hiring Associate Software Engineer
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BJAK is hiring Software Engineer (Backend)
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โ
SQL Interview Questions with Answers
1. What is a window function?
A window function computes results over a group ("window") of rows related to the current row, without collapsing them (like GROUP BY).
Examples: ROW_NUMBER(), RANK(), SUM() OVER(...) for running totals, rankings, or moving averages.
2. What is the difference between RANK() and ROW_NUMBER()?
โข ROW_NUMBER(): assigns unique sequential numbers to all rows, even if values are equal.
โข RANK(): gives same rank to tied values, then skips the next rank (e.g., 1, 1, 3).
3. How do you find the second highest salary?
SELECT salary
FROM (
SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rnk
FROM employees
) t
WHERE rnk = 2;
This avoids ties if you want exactly the secondโhighest value.
4. What is a recursive CTE?
A recursive CTE refers to itself in its WITH definition, usually in the form "anchor + UNION ALL recursive step". It is used for hierarchical data like managersโemployees, org charts, or tree structures.
5. What is the difference between correlated and non-correlated subquery?
โข Nonโcorrelated: runs once, independent of the outer query.
โข Correlated: references columns from the outer query and runs once per outer row (e.g., SELECT ... FROM t1 WHERE col > (SELECT AVG(col) FROM t2 WHERE t2.id = t1.id)).
6. How do you remove duplicates without DISTINCT?
Use window functions:
DELETE FROM (
SELECT ROW_NUMBER() OVER (PARTITION BY col1, col2 ORDER BY id) as rn
FROM table
) t
WHERE rn > 1;
Or use GROUP BY and keep one row per group.
7. What is an INDEX and when do you use it?
An index speeds up data retrieval on specified columns (used in WHERE, JOIN, ORDER BY). Use it on columns that are frequently filtered or joined; avoid on very small tables or columns updated often.
8. Explain self-join with example.
A selfโjoin joins a table to itself using aliases. Example:
SELECT e1.name as employee, e2.name as manager
FROM employees e1
LEFT JOIN employees e2 ON e1.manager_id = e2.id;
Useful for parentโchild relationships.
9. What is the difference between DELETE, DROP, and TRUNCATE?
โข DELETE: removes rows (can be filtered by WHERE), can be rolled back.
โข TRUNCATE: removes all rows quickly, resets storage; often not logged per row.
โข DROP: removes entire table (structure + data); cannot be rolled back.
10. How do you pivot/unpivot data in SQL?
โข Pivot: turns rows into columns (e.g., sales per month as columns) using PIVOT or conditional aggregation (MAX(CASE WHEN ... END)).
โข Unpivot: turns columns into rows (e.g., multiple month columns โ one month column) using UNPIVOT or UNION ALL/VALUES.
11. What is LAG() and LEAD()?
โข LAG(col, n): value of col from n rows before current row.
โข LEAD(col, n): value from n rows after. Used for timeโseries analysis (MoM change, prior/next values).
12. How do you handle NULL in aggregates?
Most aggregates (SUM, AVG, MAX, MIN) ignore NULL.
โข COUNT(col) ignores NULL; COUNT(*) counts all rows.
โข Use COALESCE() or ISNULL() to replace NULL before aggregating.
13. What is the difference between VIEW and MATERIALIZED VIEW?
โข VIEW: virtual table; query runs every time you select.
โข MATERIALIZED VIEW: stores result physically and refreshes periodically; faster reads, slower updates.
14. Explain ACID properties.
โข Atomicity: transaction is "all or nothing".
โข Consistency: valid state before and after.
โข Isolation: concurrent transactions don't interfere.
โข Durability: committed changes survive crashes.
indexes on WHERE, JOIN, ORDER BY columns.
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1. What is a window function?
A window function computes results over a group ("window") of rows related to the current row, without collapsing them (like GROUP BY).
Examples: ROW_NUMBER(), RANK(), SUM() OVER(...) for running totals, rankings, or moving averages.
2. What is the difference between RANK() and ROW_NUMBER()?
โข ROW_NUMBER(): assigns unique sequential numbers to all rows, even if values are equal.
โข RANK(): gives same rank to tied values, then skips the next rank (e.g., 1, 1, 3).
3. How do you find the second highest salary?
SELECT salary
FROM (
SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rnk
FROM employees
) t
WHERE rnk = 2;
This avoids ties if you want exactly the secondโhighest value.
4. What is a recursive CTE?
A recursive CTE refers to itself in its WITH definition, usually in the form "anchor + UNION ALL recursive step". It is used for hierarchical data like managersโemployees, org charts, or tree structures.
5. What is the difference between correlated and non-correlated subquery?
โข Nonโcorrelated: runs once, independent of the outer query.
โข Correlated: references columns from the outer query and runs once per outer row (e.g., SELECT ... FROM t1 WHERE col > (SELECT AVG(col) FROM t2 WHERE t2.id = t1.id)).
6. How do you remove duplicates without DISTINCT?
Use window functions:
DELETE FROM (
SELECT ROW_NUMBER() OVER (PARTITION BY col1, col2 ORDER BY id) as rn
FROM table
) t
WHERE rn > 1;
Or use GROUP BY and keep one row per group.
7. What is an INDEX and when do you use it?
An index speeds up data retrieval on specified columns (used in WHERE, JOIN, ORDER BY). Use it on columns that are frequently filtered or joined; avoid on very small tables or columns updated often.
8. Explain self-join with example.
A selfโjoin joins a table to itself using aliases. Example:
SELECT e1.name as employee, e2.name as manager
FROM employees e1
LEFT JOIN employees e2 ON e1.manager_id = e2.id;
Useful for parentโchild relationships.
9. What is the difference between DELETE, DROP, and TRUNCATE?
โข DELETE: removes rows (can be filtered by WHERE), can be rolled back.
โข TRUNCATE: removes all rows quickly, resets storage; often not logged per row.
โข DROP: removes entire table (structure + data); cannot be rolled back.
10. How do you pivot/unpivot data in SQL?
โข Pivot: turns rows into columns (e.g., sales per month as columns) using PIVOT or conditional aggregation (MAX(CASE WHEN ... END)).
โข Unpivot: turns columns into rows (e.g., multiple month columns โ one month column) using UNPIVOT or UNION ALL/VALUES.
11. What is LAG() and LEAD()?
โข LAG(col, n): value of col from n rows before current row.
โข LEAD(col, n): value from n rows after. Used for timeโseries analysis (MoM change, prior/next values).
12. How do you handle NULL in aggregates?
Most aggregates (SUM, AVG, MAX, MIN) ignore NULL.
โข COUNT(col) ignores NULL; COUNT(*) counts all rows.
โข Use COALESCE() or ISNULL() to replace NULL before aggregating.
13. What is the difference between VIEW and MATERIALIZED VIEW?
โข VIEW: virtual table; query runs every time you select.
โข MATERIALIZED VIEW: stores result physically and refreshes periodically; faster reads, slower updates.
14. Explain ACID properties.
โข Atomicity: transaction is "all or nothing".
โข Consistency: valid state before and after.
โข Isolation: concurrent transactions don't interfere.
โข Durability: committed changes survive crashes.
indexes on WHERE, JOIN, ORDER BY columns.
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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
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๐ Channel Link:
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### 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
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