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𝗧𝗵𝗼𝘀𝗲 𝘄𝗵𝗼 𝘄𝗮𝗻𝘁 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹𝘀 𝗮𝗻𝗱 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗳𝗿𝗼𝗺 𝗧𝗼𝗽 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗕𝗮𝘀𝗲𝗱, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗕𝗮𝘀𝗲𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗿𝘁 𝘂𝗽  𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗹𝗶𝗸𝗲 𝗔𝗺𝗮𝘇𝗼𝗻, 𝗚𝗼𝗼𝗴𝗹𝗲,  𝗔𝗽𝗽𝗹𝗲, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗜𝗕𝗠, 𝗧𝗖𝗦, 𝗖𝗼𝗴𝗻𝗶𝘇𝗮𝗻𝘁, 𝗪𝗶𝗽𝗿𝗼, 𝗖𝗧𝗦, 𝗚𝗼𝗹𝗱𝗺𝗮𝗻 𝗦𝗮𝗰𝗵𝘀, 𝗢𝗹𝗮, 𝗨𝗯𝗲𝗿, 𝗭𝗼𝗺𝗮𝘁𝗼, 𝗦𝘄𝗶𝗴𝗴𝘆, 𝘂𝗽𝗚𝗿𝗮𝗱, 𝗖𝘂𝗿𝗲 𝗙𝗶𝘁, 𝗛𝗮𝗰𝗸𝗲𝗿𝗿𝗮𝗻𝗸, 𝗚𝗲𝗲𝗸𝘀𝗳𝗼𝗿𝗴𝗲𝗲𝗸𝘀 𝗮𝗻𝗱 𝗺𝗮𝗻𝘆 𝗺𝗼𝗿𝗲, 𝗰𝗮𝗻 𝗷𝗼𝗶𝗻 𝘁𝗵𝗲 𝗯𝗲𝗹𝗼𝘄 network.

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𝗟𝗮𝘁𝗲𝘀𝘁 𝗝𝗼𝗯𝘀 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝟮𝟬𝟭𝟳, 𝟮𝟬𝟭𝟴, 𝟮𝟬𝟭𝟵, 𝟮𝟬𝟮𝟬, 𝟮𝟬𝟮𝟭, 𝟮𝟬𝟮𝟮, 𝟮𝟬𝟮𝟯, 𝟮𝟬𝟮𝟰, 𝟮𝟬𝟮𝟱, 𝟮𝟬𝟮𝟲, 𝟮𝟬𝟮𝟳, 𝟮𝟬𝟮𝟴 𝗮𝗻𝗱 𝟮𝟬𝟮𝟵 𝗕𝗮𝘁𝗰𝗵.

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🚀 SQL Project Series

Food & Grocery Delivery Analytics 🛒

Analyze customers, stores, products, orders, deliveries, and payments to understand sales performance, customer behavior, delivery efficiency, and operational costs.

🎯 Business Objectives

✅ Analyze order and revenue trends

✅ Identify top-selling products

✅ Measure customer retention

✅ Analyze store performance

✅ Track delivery efficiency

✅ Identify peak ordering periods

✅ Monitor cancellations and refunds

✅ Optimize product and store performance

📂 Database Setup

CREATE DATABASE grocery_delivery_db;
USE grocery_delivery_db;

Tables Created:

customers → customer_id, customer_name, city, signup_date

stores → store_id, store_name, city, store_type

products → product_id, product_name, category, price

orders → order_id, customer_id, store_id, order_date, order_status, delivery_time_minutes, delivery_fee

order_items → order_item_id, order_id, product_id, quantity, unit_price

Sample data for 5 customers, 4 stores, 5 products, 5 orders already included.

🧠 SQL Concepts You'll Practice

✔ INNER JOIN, LEFT JOIN

✔ Aggregate Functions, GROUP BY, HAVING

✔ CASE WHEN, CTEs, Subqueries

✔ Window Functions

✔ Date & Time Functions

✔ Conditional Aggregation

📊 Business KPIs You Can Build

📈 Total Orders, Completed Orders, Cancelled Orders, Cancellation Rate

📈 Total Revenue, AOV, Average Basket Size, Items Sold

📈 Revenue by Category, Store, City

📈 Top-Selling / Low-Selling Products

📈 Customer Lifetime Value, Repeat Purchase Rate, Retention Rate

📈 Average Delivery Time, On-Time Delivery Rate

📈 Peak Ordering Hour, Peak Ordering Day, Monthly Revenue Growth

📈 Delivery Fee Revenue, Customer Acquisition Trend

📈 Executive Grocery Delivery Dashboard

💡 Example Queries

1. Total Revenue

SELECT SUM(oi.quantity * oi.unit_price) AS total_revenue
FROM orders o
JOIN order_items oi ON o.order_id = oi.order_id
WHERE o.order_status = 'Delivered';

2. Top-Selling Products

SELECT p.product_name, SUM(oi.quantity) AS units_sold
FROM products p
JOIN order_items oi ON p.product_id = oi.product_id
JOIN orders o ON oi.order_id = o.order_id
WHERE o.order_status = 'Delivered'
GROUP BY p.product_name
ORDER BY units_sold DESC
LIMIT 10;

3. Average Order Value

WITH order_values AS (
    SELECT o.order_id, SUM(oi.quantity * oi.unit_price) AS order_value
    FROM orders o
    JOIN order_items oi ON o.order_id = oi.order_id
    WHERE o.order_status = 'Delivered'
    GROUP BY o.order_id
)
SELECT ROUND(AVG(order_value), 2) AS average_order_value FROM order_values;

4. Repeat Customers

SELECT customer_id, COUNT(order_id) AS total_orders
FROM orders
WHERE order_status = 'Delivered'
GROUP BY customer_id
HAVING COUNT(order_id) > 1;

5. Revenue by Store

SELECT s.store_name, SUM(oi.quantity * oi.unit_price) AS revenue
FROM stores s
JOIN orders o ON s.store_id = o.store_id
JOIN order_items oi ON o.order_id = oi.order_id
WHERE o.order_status = 'Delivered'
GROUP BY s.store_name
ORDER BY revenue DESC;

6. Cancellation Rate

SELECT ROUND(100.0 * SUM(CASE WHEN order_status = 'Cancelled' THEN 1 ELSE 0 END) / COUNT(*), 2) AS cancellation_rate
FROM orders;

7. Peak Ordering Hours

SELECT EXTRACT(HOUR FROM order_date) AS order_hour, COUNT(*) AS total_orders
FROM orders
WHERE order_status = 'Delivered'
GROUP BY EXTRACT(HOUR FROM order_date)
ORDER BY total_orders DESC;

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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
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s 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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✅ 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()? 
• ROW_NUMBER(): assigns unique sequential numbers to all rows, even if values are equal.
• 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 second‑highest 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 managers‑employees, org charts, or tree structures.

5. What is the difference between correlated and non-correlated subquery? 
• Non‑correlated: runs once, independent of the outer query.
• 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 self‑join 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 parent‑child relationships.

9. What is the difference between DELETE, DROP, and TRUNCATE? 
• DELETE: removes rows (can be filtered by WHERE), can be rolled back.
• TRUNCATE: removes all rows quickly, resets storage; often not logged per row.
• DROP: removes entire table (structure + data); cannot be rolled back.

10. How do you pivot/unpivot data in SQL? 
• Pivot: turns rows into columns (e.g., sales per month as columns) using PIVOT or conditional aggregation (MAX(CASE WHEN ... END)).
• Unpivot: turns columns into rows (e.g., multiple month columns → one month column) using UNPIVOT or UNION ALL/VALUES.

11. What is LAG() and LEAD()? 
• LAG(col, n): value of col from n rows before current row.
• LEAD(col, n): value from n rows after. Used for time‑series analysis (MoM change, prior/next values).

12. How do you handle NULL in aggregates? 
Most aggregates (SUM, AVG, MAX, MIN) ignore NULL. 
• COUNT(col) ignores NULL; COUNT(*) counts all rows.
• Use COALESCE() or ISNULL() to replace NULL before aggregating.

13. What is the difference between VIEW and MATERIALIZED VIEW? 
• VIEW: virtual table; query runs every time you select.
• MATERIALIZED VIEW: stores result physically and refreshes periodically; faster reads, slower updates.

14. Explain ACID properties. 
• Atomicity: transaction is "all or nothing".
• Consistency: valid state before and after.
• Isolation: concurrent transactions don't interfere.
• Durability: committed changes survive crashes.

indexes on WHERE, JOIN, ORDER BY columns.


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BJAK is hiring Software Engineer (Backend)
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carelon care solutions is hiring Associate Software Engineer
For 2024, 2025 grads
Location: Multiple

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For 2026, 2027 grads
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ACI Worldwide is hiring for Software Engineer
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American Express is hiring for Software Engineer I
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Octave is hiring for Software Engineer
Batch: 2021, 2022, 2023, 2024

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Artpark is hiring for Data quality check executive (Freelance)
Batch: 2023, 2024, 2025, 2026

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Priceline is hiring for Associate Software Engineer
Batch: 2024, 2025

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Platform Science is hiring for Data Engineer
Batch: 2023, 2024, 2025

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