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10 Questions asked in Every Interview:>>>

1. Tell me about yourself.
2. Why are you interested in our company?
3. Why are you interested in this position?
4. What are your strengths?
5. What are your weaknesses?
6. Why should we hire you over other qualified candidates?
7. Where do you see yourself in 5 years?
8. Tell me about a time you failed.
9. What are your salary expectations?
10. What questions do you have for us?

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Coursera

Python for Data Science, AI & Development

Offered by IBM. Kickstart your Python journey with this ... Enroll for free.

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Use these ChatGPT prompts to finish hours of work in seconds

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Apply now 👇🏼

https://linktr.ee/python4dev1

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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

All the best 👍👍

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Learn skills from Google, IBM and Meta

1. Google Data Analytics
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2. Google Project Management
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3. Google IT Support
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4. Google Digital Marketing & E-commerce
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5. Google IT Automation with Python
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6. Google Business Intelligence
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7. Google Advanced Data Analytics
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8. Google Cybersecurity
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9. IBM Data Science
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10. Machine Learning
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11. IBM Data Analyst
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12. Python for Everybody
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lnkd.in

This link will take you to a page that’s not on LinkedIn

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Tech & code

Complete Roadmap map To learn SQL in one month

Week 1: Introduction and Basics

Day 1-2: Understanding Databases and SQL Syntax

•Study relational databases and SQL’s role in managing and querying data.

•Familiarize yourself with common SQL databases (e.g., MySQL, PostgreSQL, SQLite).

•Install a SQL environment or use a cloud-based platform like SQLBolt, Mode, or Google BigQuery to practice.

•Day 3-4: Basic SQL Commands

•Learn fundamental commands such as SELECT, FROM, and WHERE.

•Practice filtering data with conditions and combining them with AND, OR, and NOT operators.

•Complete exercises on selecting specific columns, using DISTINCT to remove duplicates, and sorting data with ORDER BY.

•Day 5-7: Basic Functions and Aggregate Functions

•Learn to use basic functions like COUNT, SUM, MIN, MAX, and AVG for aggregate data analysis.

•Practice grouping data with GROUP BY and filtering grouped data with HAVING.

Week 2: Intermediate Querying Techniques

•Day 8-10: Joining Tables

•Study types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN).

•Understand primary and foreign keys and how they link tables in relational databases.

•Practice combining data from multiple tables with various joins.

• Day 11-12: Subqueries and Nested Queries

• Learn how to create subqueries within SELECT, WHERE, and FROM clauses.

• Practice writing queries that depend on other queries, focusing on readability and efficiency.

• Day 13-14: Set Operations

• Learn about set operators (UNION, INTERSECT, EXCEPT) to combine or filter query results.

• Practice combining results from different tables or queries and filtering results.

Week 3: Advanced SQL Concepts

• Day 15-16: Advanced Functions

• Study string functions (e.g., CONCAT, SUBSTRING, REPLACE), date functions (e.g., DATEADD, DATEDIFF), and mathematical functions.

• Learn about case statements and conditional logic to customize output.

• Day 17-18: Indexes and Performance Optimization

• Understand indexes, their purpose, and their effect on query performance.

• Study how to use EXPLAIN to analyze queries and optimize them for faster results.

• Day 19-21: Stored Procedures, Triggers, and Views

• Learn to create stored procedures to encapsulate SQL code for reusability.

• Study triggers to automate actions based on database events.

• Practice creating views to simplify complex queries for easier access.

Week 4: Final Project and Practical Applications

• Day 22-25: Case Study or Mini Project

• Choose a dataset (e.g., a sample sales database) and outline analysis goals.

• Write queries to extract insights, applying techniques from previous weeks.

• Ensure to include complex joins, subqueries, and aggregate analysis.

• Day 26-28: Error Handling and Best Practices

• Learn common SQL pitfalls and best practices for writing clean, efficient queries.

• Understand how to troubleshoot errors and optimize query structure.

• Day 29-30: Review and Practice

• Revisit complex topics and practice additional exercises.

• Take sample SQL assessments or timed quizzes to evaluate your progress.

By following this roadmap, you will build a solid SQL foundation within a month.

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