Day 20: Triggers

1. What is a Trigger?

A trigger is a special type of stored procedure that automatically executes in response to specific events on a table, such as INSERT, UPDATE, or DELETE.

2. Types of Triggers:

AFTER Trigger (a.k.a. FOR Trigger)

– Executes after the triggering event.

INSTEAD OF Trigger – Replaces the default action of an INSERT, UPDATE, or DELETE.

3. AFTER Trigger Example:

Triggers after a row is inserted into the Employees table.

CREATE TRIGGER trg_AfterInsert ON Employees AFTER INSERT AS BEGIN PRINT 'A new employee record has been inserted!'; END; 
Test the trigger:
INSERT INTO Employees (EmployeeID, Name, Department) VALUES (101, 'John Doe', 'IT'); 

After execution, the message "A new employee record has been inserted!" appears.

4. INSTEAD OF Trigger Example
Prevents deleting employees from the Employees table but logs the request.

CREATE TRIGGER trg_InsteadOfDelete ON Employees INSTEAD OF DELETE AS BEGIN PRINT 'Delete operation blocked. Logging attempt...'; INSERT INTO DeleteLogs (EmployeeID, DeleteTime) SELECT EmployeeID, GETDATE() FROM deleted; END; 
Test the trigger:
DELETE FROM Employees WHERE EmployeeID = 101; 
Instead of deleting, it logs the deletion attempt.

5. Viewing & Dropping Triggers

List triggers on a table:

SELECT name FROM sys.triggers WHERE parent_id = OBJECT_ID('Employees'); 
Drop a trigger:
DROP TRIGGER trg_AfterInsert; 

6. Best Practices for Triggers:

✅ Keep triggers lightweight to avoid performance issues.

✅ Use triggers only when necessary (consider stored procedures for flexibility).

✅ Avoid recursive triggers (where a trigger fires another trigger).

✅ Log actions to track unwanted modifications.

Action Steps:

Create an AFTER INSERT trigger to log new entries into an audit table.

Create an INSTEAD OF UPDATE trigger to prevent salary updates above a certain limit.

Experiment with retrieving deleted records using the deleted table inside a trigger.

🔝 SQL 30 Days Challenge

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Python Learning Plan in 2025

|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)

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Day 21: Review Week 3 Topics & Complex SQL Challenges

📌 Topics to Review from Week 3

Window Functions – (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG).

Stored Procedures – Creating, executing, and using parameters.

Triggers – AFTER and INSTEAD OF triggers.

Views – Creating, modifying, and using indexed views.

Transactions & ACID Properties – Ensuring data consistency.

📝 Complex SQL Challenges

1️⃣ Challenge: Find the Second Highest Salary (Without Using LIMIT or TOP)

You have an Employees table. Write a query to find the second highest salary.

SELECT MAX(Salary) AS SecondHighestSalary FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees); 

2️⃣ Challenge: Get Consecutive Login Streaks

Given a Logins table with UserID and LoginDate, find users who logged in for three consecutive days.

SELECT DISTINCT L1.UserID FROM Logins L1 JOIN Logins L2 ON L1.UserID = L2.UserID AND L1.LoginDate = L2.LoginDate - 1 JOIN Logins L3 ON L1.UserID = L3.UserID AND L1.LoginDate = L3.LoginDate - 2; 

3️⃣ Challenge: Rank Employees by Salary Within Each Department

SELECT EmployeeID, Name, Department, Salary, RANK() OVER (PARTITION BY Department ORDER BY Salary DESC) AS SalaryRank FROM Employees; 

✅ Action Plan for Today

Review Week 3 Topics – Revisit notes, practice stored procedures, and triggers.

Solve These Complex Challenges – Try modifying them for different cases.

Ask Yourself:

What happens if two employees have the same second-highest salary?

How would you handle ties in ranking employees?

Can you optimize these queries for better performance?

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Data Analyst Interview Part-1

1. What is the difference between a primary key and a foreign key in SQL?

Answer:

A primary key uniquely identifies each record in a table. It must be unique and cannot contain NULL values.

A foreign key is a field in one table that refers to the primary key in another table, establishing a relationship between the two tables.

Example:

CREATE TABLE Customers ( CustomerID INT PRIMARY KEY, Name VARCHAR(50) ); CREATE TABLE Orders ( OrderID INT PRIMARY KEY, CustomerID INT, FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID) );

2. What are the different types of JOINs in SQL?

Answer:

SQL supports different types of JOINs to combine data from multiple tables:

INNER JOIN: Returns only matching records in both tables.

LEFT JOIN: Returns all records from the left table and matching records from the right table.

RIGHT JOIN: Returns all records from the right table and matching records from the left table.

FULL OUTER JOIN: Returns all records from both tables, with NULLs where there is no match.

SELF JOIN: A table is joined with itself.

CROSS JOIN: Produces a Cartesian product of both tables.

Example:

SELECT Customers.Name, Orders.OrderID FROM Customers INNER JOIN Orders ON Customers.CustomerID = Orders.CustomerID; 

3. What are Pivot Tables in Excel, and why are they used?

Answer:

A Pivot Table in Excel allows users to summarize, analyze, explore, and present data dynamically. It helps in:
Summarizing large datasets quickly.
Performing calculations like sum, count, average, etc.
Creating reports without using complex formulas.

Example Use Case:

If you have sales data with columns for region, product, and revenue, a pivot table can show total revenue by region and product category.

Steps to create a Pivot Table:

Select your dataset.
Go to Insert → Pivot Table.
Choose where to place the Pivot Table.
Drag fields into Rows, Columns, Values, and Filters.

4. Explain the difference between COUNT(), COUNT(*), and COUNT(column_name) in SQL.

Answer:

COUNT(): Returns the number of rows where a column is NOT NULL.
COUNT(*): Returns the total number of rows in a table, including NULL values.
COUNT(column_name): Counts non-NULL values in a specific column.

Example:

SELECT COUNT(*) FROM Orders; -- Counts all rows SELECT COUNT(CustomerID) FROM Orders; -- Counts only non-NULL CustomerIDs 

5. How do you handle missing values in Python using Pandas?

Answer:

Missing values can be handled using Pandas functions:
Drop missing values: df.dropna()
Fill missing values: df.fillna(value)
Replace missing values: df.replace(to_replace, value)
Check for missing values: df.isnull().sum()

Example:

import pandas as pd df = pd.DataFrame({'A': [1, 2, None, 4], 'B': [None, 2, 3, 4]}) df.fillna(0, inplace=True) # Replace NaN with 0

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Day 22: Database Design & Normalization

1. What is Database Design?

Database design is the process of structuring data efficiently to avoid redundancy, improve consistency, and optimize performance.

2. What is Normalization?

Normalization is a technique that helps organize data by eliminating duplicates and ensuring data integrity. It consists of different normal forms (NF) to structure the database properly.

3. Normal Forms (1NF, 2NF, 3NF) Explained

✅ First Normal Form (1NF)

Each column should have atomic (indivisible) values

No repeating groups or arrays
Each row should be unique with a primary key

Example (Not 1NF)

A single column contains multiple values like "Shoes, Watch" in one row.

Fix: Split the data so that each value is stored separately in its own row.

✅ Second Normal Form (2NF)

The table must already be in 1NF.

Remove partial dependencies (where a column depends on only part of a composite key).

Example (Not 2NF)

A product category is stored in an orders table, even though it's related to the product, not the order.

Fix: Split the table into separate entities (e.g., an "Orders" table and a "Products" table).

✅ Third Normal Form (3NF)

The table must already be in 2NF.

Remove transitive dependencies (where a non-key column depends on another non-key column).

Example (Not 3NF)
A department name and department head are stored in a students table.

Fix: Move department-related information to a separate "Departments" table.

📌 Why is Normalization Important?

Removes duplicate data → Saves storage.

Improves data integrity → Prevents inconsistencies.

Enhances query performance → Makes retrieval faster.

✅ Action Plan for Today

1️⃣ Understand how 1NF, 2NF, and 3NF work.

2️⃣ Look at any existing dataset and try to normalize it.

3️⃣ Ask yourself:
Is any column storing multiple values?
Does any column depend only on part of a primary key?
Are there any transitive dependencies?

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Data analyst interview Part-2

6. What is the difference between a WHERE and HAVING clause in SQL?

Answer:

WHERE is used to filter records before aggregation (used with SELECT, UPDATE, DELETE).

HAVING is used to filter records after aggregation (used with GROUP BY).

Example:

-- WHERE filters before aggregation SELECT * FROM Sales WHERE Region = 'North'; -- HAVING filters after aggregation SELECT Region, SUM(Sales) AS TotalSales FROM Sales GROUP BY Region HAVING SUM(Sales) > 50000; 

7. What are the different types of filters in Power BI?

Answer:

Power BI provides different types of filters for refining data:

Visual-level filters – Apply to a single visual.
Page-level filters – Apply to all visuals on a report page.
Report-level filters – Apply to the entire report.
Drillthrough filters – Allow users to focus on specific data in another page.
Top N filters – Show only the top N records based on a measure.

Example: In Power BI Desktop, you can apply filters using the Filters Pane.

8. How do you remove duplicate values in Excel?

Answer:

You can remove duplicates in Excel using:

Remove Duplicates feature:

Select your data.

Go to Data → Remove Duplicates and choose columns to check for duplicates.

Using a formula (COUNTIF method): =IF(COUNTIF(A:A, A2)>1, "Duplicate", "Unique")

Using Power Query:
Load data into Power Query Editor → Remove Duplicates option.

9. What is the difference between a Bar Chart and a Histogram?

Answer:

Bar Chart represents categorical data with discrete bars.
Histogram represents continuous data and shows frequency distribution.
Example:
A Bar Chart can show sales by product category.
A Histogram can show age distribution in a population dataset.

10. How do you handle missing values in Python using Pandas?
Answer:

You can handle missing values using:

Drop missing values:
df.dropna() # Removes rows with missing values

Fill missing values:
df.fillna(0) # Replaces NaN with 0

Fill with column mean/median/mode:
df['Column'].fillna(df['Column'].mean(), inplace=True)

Interpolate missing values:
df.interpolate(method='linear')

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Day 23: Constraints in SQL

SQL constraints are rules applied to columns in a table to ensure data integrity and accuracy.

1. Types of SQL Constraints

✅ PRIMARY KEY
Ensures each row is unique and not NULL.
A table can have only one primary key.

✅ FOREIGN KEY
Enforces a relationship between tables.
Ensures data exists in the referenced table.

✅ UNIQUE
Ensures all values in a column are distinct.
Unlike PRIMARY KEY, a table can have multiple UNIQUE constraints.

✅ CHECK
Sets a condition that each row must satisfy.
Example: Age must be greater than 18.

✅ DEFAULT
Assigns a default value if no value is provided.

✅ NOT NULL
Ensures a column cannot have NULL values.

2. Why Are Constraints Important?

✔ Prevent invalid data entry (e.g., age cannot be negative).
✔ Ensure referential integrity (foreign keys link valid records).
✔ Improve query performance by enforcing structure.

3. Common Use Cases

📌 PRIMARY KEY → Used for unique identification of records.
📌 FOREIGN KEY → Used for linking tables in a database.
📌 CHECK → Used for validation (e.g., salary must be positive).
📌 UNIQUE → Used to avoid duplicates in specific columns.
📌 NOT NULL → Used when a column must always have a value.

✅ Action Plan for Today:

1️⃣ Understand when to use each constraint.
2️⃣ Try adding constraints to an existing table definition.
3️⃣ Practice writing SQL queries using PRIMARY KEY, FOREIGN KEY, and CHECK constraints.

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Day 24: Creating and Managing Indexes in SQL

1. What is an Index in SQL?

An index is a database object that improves the speed of data retrieval from a table. It's like an index in a book—it helps you find information quickly without scanning the entire table.

2. Types of Indexes

✅ Primary Index (Clustered Index):
Automatically created on the PRIMARY KEY.
Physically organizes data in a sorted order.
A table can have only one clustered index.

✅ Secondary Index (Non-Clustered Index):
Created manually using the CREATE INDEX command.
Stores a pointer to the actual data (does not change physical order).
A table can have multiple non-clustered indexes.

✅ Unique Index:
Ensures that all values in a column are distinct (same as UNIQUE constraint).

✅ Full-Text Index:
Used for fast text searches in large text-based columns.

✅ Composite Index:
Created on multiple columns to optimize queries using those columns together.

3. How to Create and Drop Indexes

📌 Create an Index
CREATE INDEX idx_customer_name ON customers (customer_name);

📌 Create a Composite Index
CREATE INDEX idx_order ON orders (customer_id, order_date);

📌 Drop an Index
DROP INDEX idx_customer_name;

4. When to Use Indexes?

✔ Use indexes on frequently searched columns.
✔ Use indexes on JOIN and WHERE clause columns.
✔ Avoid indexing small tables (full table scans are faster).
✔ Avoid too many indexes (they slow down INSERT, UPDATE, DELETE).

✅ Action Plan for Today:

1️⃣ Identify queries in your database that could benefit from an index.
2️⃣ Create a non-clustered index on a table and check the performance.
3️⃣ Drop unnecessary indexes and observe the difference.

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Day 25: Backup and Restore Strategies in SQL (Good to know concept)

1. Why Are Backups Important?

Backups protect your database from accidental deletions, hardware failures, or cyberattacks. A good backup strategy ensures minimal downtime and data recovery when needed.

2. Types of Backups:

✅ Full Backup
Backs up the entire database (all tables, indexes, and transactions).
Used for disaster recovery.

Example:

BACKUP DATABASE mydb TO DISK = 'C:\backups\mydb_full.bak';

✅ Differential Backup
Backs up only changes made since the last full backup.
Faster than a full backup.

Example:

BACKUP DATABASE mydb TO DISK = 'C:\backups\mydb_diff.bak' WITH DIFFERENTIAL; 

✅ Transaction Log Backup

Backs up the transaction logs, allowing point-in-time recovery.
Example: BACKUP LOG mydb TO DISK = 'C:\backups\mydb_log.trn';

3. How to Restore a Database

📌 Restore a Full Backup

RESTORE DATABASE mydb FROM DISK = 'C:\backups\mydb_full.bak';

📌 Restore with Differential Backup

RESTORE DATABASE mydb FROM DISK = 'C:\backups\mydb_full.bak' WITH NORECOVERY; RESTORE DATABASE mydb FROM DISK = 'C:\backups\mydb_diff.bak' WITH RECOVERY; 

4. Best Practices for Database Backups

✔ Schedule backups regularly (daily or weekly).
✔ Store backups in multiple locations (local + cloud).
✔ Automate backups to avoid manual errors.
✔ Test your backups by restoring to a test database.

✅ Action Plan for Today:

1️⃣ Identify the best backup strategy for your database.

2️⃣ Set up an automated full backup.

3️⃣ Try restoring a backup to check if it works correctly.

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Data analytics offers excellent job prospects in 2025, with numerous opportunities across various industries.

Job Market Overview
Data analyst jobs are experiencing rapid growth, with an expected expansion in multiple sectors.

- High Demand Roles:
- Data Scientist
- Business Intelligence Analyst
- Financial Analyst
- Marketing Analyst
- Healthcare Data Analyst

Skills Required
Top skills for success in data analytics include:

- Technical Skills:
- Python and R programming
- SQL database management
- Data manipulation and cleaning
- Statistical analysis
- Power BI or Tableau
- Machine learning basics

Salary Expectations
Average salaries vary by role:
- Data Scientist: ~$122,738 per year
- Data Analyst: Around INR 6L per annum
- Entry-level Data Analyst: ~$83,011 annually[2]

Job Search Strategies

- Utilize job portals like LinkedIn, Indeed & telegram
- Attend industry conferences and webinars
- Network with professionals
- Check company career pages
- Consider recruitment agencies specializing in tech roles

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Data Analyst Interview Series Part-3

11. What is the difference between UNION and UNION ALL in SQL?

Answer:
UNION combines results from two queries and removes duplicates.
UNION ALL combines results but keeps duplicates for better performance.

Example:

SELECT CustomerID FROM Orders_A
UNION 
SELECT CustomerID FROM Orders_B; -- Removes duplicates 

SELECT CustomerID FROM Orders_A 
UNION ALL 
SELECT CustomerID FROM Orders_B; -- Keeps duplicates 

12. What are common DAX functions in Power BI?

Answer:

DAX (Data Analysis Expressions) is used in Power BI for calculations.

Common DAX functions include:

SUM() – Adds up values in a column.
AVERAGE() – Finds the mean value.
COUNT() – Counts the number of rows.
CALCULATE() – Modifies a measure based on conditions.
FILTER() – Returns a subset of data.

Example:

TotalSales = SUM(Sales[Amount]) FilteredSales = CALCULATE(SUM(Sales[Amount]), Sales[Region] = "North") 

13. How do you use VLOOKUP in Excel?

Answer:

VLOOKUP searches for a value in the first column of a range and returns a value from another column.

Syntax:

=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])

Example:
To find the price of a product in a table:

=VLOOKUP("ProductA", A2:C10, 2, FALSE)

14. What is the difference between a Heatmap and a Scatter Plot?

Answer:

Heatmap: Uses color intensity to represent values across a matrix. Used for correlation analysis.

Scatter Plot: Shows relationships between two continuous variables using dots. Used for trend analysis.

Example:
A Heatmap can show sales performance by region and product category.
A Scatter Plot can show sales vs. profit for different stores.

15. How do you read a CSV file into Pandas in Python?

Answer:
You can read a CSV file using pandas.read_csv():

import pandas as pd df = pd.read_csv("data.csv") print(df.head()) # Displays first 5 rows 

To handle missing values:

df = pd.read_csv("data.csv", na_values=["NA", "Missing"]) 

To select specific columns:

df = pd.read_csv("data.csv", usecols=["Name", "Sales"])

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Top companies currently hiring data analysts

Based on the current job market in 2025, here are the top companies hiring data analysts:

## Top Tech Companies

- Meta: Investing heavily in AI with significant GPU investments
- Amazon: Offers diverse data analyst roles with complex responsibilities
- Google (Alphabet): Leverages massive data ecosystems
- JP Morgan Chase & Co.: Strong focus on data-driven banking transformation

## Specialized Data Analytics Firms

- Tiger Analytics: Specializes in AI/ML solutions
- SG Analytics: Provides data-driven insights
- Monte Carlo Data: Focuses on data observability
- CB Insights: Excels in market intelligence

## Emerging Opportunities

Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually.

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Day 26: Pivoting and Unpivoting Data in SQL

1. What is Pivoting in SQL?

Pivoting converts row-based data into columns to create a structured report. It's commonly used in reporting and summarization.

2. How to Pivot Data?

Example: You have a sales table with columns Month, Product, and Sales.

If you want to convert product names into columns and show total sales per month, use:

SELECT Month, SUM(CASE WHEN Product = 'Shoes' THEN Sales ELSE 0 END) AS Shoes, SUM(CASE WHEN Product = 'Shirts' THEN Sales ELSE 0 END) AS Shirts FROM Sales GROUP BY Month; 

3. What is Unpivoting in SQL?

Unpivoting converts columns back into rows, which is useful for normalizing data.

Example: If you have sales data stored in separate columns (Shoes, Shirts), but you need a column named Product instead, use:

SELECT Month, Product, Sales FROM SalesTable UNPIVOT (Sales FOR Product IN (Shoes, Shirts)) AS unpvt; 

4. When to Use Pivot and Unpivot?

Pivot when you need structured reports with categories as columns.

Unpivot when working with dynamic columns and data normalization.

✅ Action Plan for Today:

1️⃣ Write a PIVOT query for summarizing data.

2️⃣ Use UNPIVOT to transform columns into rows.

3️⃣ Experiment with SUM, COUNT, and AVG while pivoting data.

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Day 27: Writing Stored Procedures and Functions in SQL

1. What Are Stored Procedures?

A Stored Procedure is a reusable block of SQL code that executes multiple SQL statements in a single call. It improves performance, security, and maintainability.

2. Creating a Stored Procedure

📌 Basic Syntax

CREATE PROCEDURE GetEmployeeDetails AS BEGIN SELECT * FROM Employees; END; 

📌 Executing the Procedure

EXEC GetEmployeeDetails;

📌 Stored Procedure with Parameters

CREATE PROCEDURE GetEmployeeByID (@EmpID INT) AS BEGIN SELECT * FROM Employees WHERE EmployeeID = @EmpID; END; 

📌 Executing with a Parameter

EXEC GetEmployeeByID 101; 

3. What Are SQL Functions?

Functions return a single value and are used inside queries. Unlike stored procedures, functions cannot modify the database.

📌 Creating a Function

CREATE FUNCTION GetTotalSalary() RETURNS INT AS BEGIN DECLARE @TotalSalary INT; SELECT @TotalSalary = SUM(Salary) FROM Employees; RETURN @TotalSalary; END; 

📌 Calling the Function

SELECT dbo.GetTotalSalary();

4. Differences: Stored Procedures vs Functions

✔ Stored Procedures: Perform multiple actions, support transactions, and can modify data.
✔ Functions: Return a value, are used inside queries, and cannot change database state.

✅ Action Plan for Today:

1️⃣ Create a Stored Procedure that retrieves filtered data.

2️⃣ Write a Function that calculates an aggregate value.

3️⃣ Compare performance differences between functions and stored procedures.

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Data Analyst Interview Part-4

16. What is the difference between OLTP and OLAP?

Answer:
OLTP (Online Transaction Processing): Handles real-time, transactional data (e.g., banking systems, e-commerce). Focuses on fast inserts, updates, and deletes.

OLAP (Online Analytical Processing): Used for complex queries, reporting, and business intelligence (e.g., data warehouses, dashboards). Optimized for data retrieval.

Example:
An OLTP system records a customer's purchase in an online store.
An OLAP system analyzes total sales trends for different products over time.

17. What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() in SQL?

Answer:
These functions assign ranks to rows in a result set:

RANK(): Assigns a rank but skips numbers if there are duplicates.

DENSE_RANK(): Assigns a rank without skipping numbers.

ROW_NUMBER(): Assigns a unique row number to each row, even if values are the same.

Example:

SELECT Employee, Salary, RANK() OVER (ORDER BY Salary DESC) AS Rank, DENSE_RANK() OVER (ORDER BY Salary DESC) AS DenseRank, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS RowNum FROM Employees; 

18. What are Measures and Dimensions in Tableau?

Answer:
Measures: Numeric values that can be aggregated (e.g., Sales, Profit, Quantity).
Dimensions: Categorical fields that define data granularity (e.g., Product, Region, Date).

Example:
"Sales" is a Measure (sum of sales).
"Customer Name" is a Dimension (used to group data).

19. How do you remove outliers from a dataset in Python?

Answer:
Outliers can be removed using statistical methods:

Using IQR (Interquartile Range) Method:

import pandas as pd import numpy as np Q1 = df["Sales"].quantile(0.25) Q3 = df["Sales"].quantile(0.75) IQR = Q3 - Q1 df_cleaned = df[(df["Sales"] >= Q1 - 1.5*IQR) & (df["Sales"] <= Q3 + 1.5*IQR)] 

Using Z-Score Method:

from scipy import stats df_cleaned = df[(np.abs(stats.zscore(df["Sales"])) < 3)] 

20. What is the difference between INNER JOIN and LEFT JOIN?

Answer:
INNER JOIN returns only matching records from both tables.

LEFT JOIN returns all records from the left table and matching records from the right table (fills NULL if no match).

Example:

-- INNER JOIN: Returns only matching Customers with Orders SELECT Customers.Name, Orders.OrderID FROM Customers INNER JOIN Orders ON Customers.CustomerID = Orders.CustomerID; -- LEFT JOIN: Returns all Customers, even if they have no Orders SELECT Customers.Name, Orders.OrderID FROM Customers LEFT JOIN Orders ON Customers.CustomerID = Orders.CustomerID; 

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Day 28: Integrating SQL with Other Tools (Python, Power BI, Tableau) & SQL in Big Data

1. Using SQL with Python

Python is widely used to interact with databases via libraries like sqlite3, SQLAlchemy, and pandas.

📌 Example: Connecting to a Database in Python

import sqlite3 # Connect to the database conn = sqlite3.connect('my_database.db') cursor = conn.cursor() # Execute a query cursor.execute("SELECT * FROM Employees") rows = cursor.fetchall() # Print results for row in rows: print(row) conn.close()

2. Using SQL with Power BI
Power BI allows direct SQL connections for data visualization.

📌 Steps to Connect SQL to Power BI:

1️⃣ Open Power BI Desktop.
2️⃣ Click on Get Data → Select SQL Server.
3️⃣ Enter Server Name & Database Name.
4️⃣ Choose DirectQuery or Import Mode.
5️⃣ Load and create visualizations using SQL queries.

3. Using SQL with Tableau

Tableau connects with SQL databases to create interactive dashboards.

📌 Steps to Connect SQL to Tableau:

1️⃣ Open Tableau → Click Connect to Data.
2️⃣ Choose Microsoft SQL Server, MySQL, or PostgreSQL.
3️⃣ Enter Database Credentials.
4️⃣ Use SQL queries to fetch data and build charts & graphs.

4. SQL in Big Data (Introduction to NoSQL)

SQL is not always suitable for big data processing. NoSQL databases like MongoDB, Cassandra, and Hadoop are used for scalable, unstructured data.

📌 SQL vs NoSQL:

✔ SQL: Structured data, strict schema, ACID compliance (e.g., MySQL, PostgreSQL).

✔ NoSQL: Flexible schema, distributed storage, better for big data (e.g., MongoDB, Cassandra).

✅ Action Plan for Today:

1️⃣ Try running a SQL query in Python.
2️⃣ Connect a SQL database to Power BI/Tableau.
3️⃣ Research the difference between SQL and NoSQL for big data.

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Data analyst interview questions Part-5:

21. What are Window Functions in SQL?

Window functions perform calculations across a set of rows related to the current row without collapsing the dataset like GROUP BY.

Common window functions:
RANK() – Assigns a rank with gaps for ties.
DENSE_RANK() – Assigns a rank without gaps.
ROW_NUMBER() – Assigns a unique row number.
LEAD() / LAG() – Access next/previous row values.

Example:

SELECT Employee, Department, Salary, RANK() OVER (PARTITION BY Department ORDER BY Salary DESC) AS Rank FROM Employees; 

22. How do you create a calculated column in Power BI?

A calculated column is a new column created using DAX formulas.

Example:

Profit Margin = Sales[Profit] / Sales[Revenue]

Steps in Power BI:

Open Power BI Desktop.
Go to the Modeling tab → Click New Column.
Enter the DAX formula and press Enter.

23. How do you find duplicate values in Excel?

Methods to identify duplicates:

Conditional Formatting:

Select data → Click Home > Conditional Formatting > Highlight Duplicates.

Using COUNTIF formula: =IF(COUNTIF(A:A, A2) > 1, "Duplicate", "Unique")

Using Power Query:

Load data into Power Query → Use Group By to count duplicates.

24. What is the difference between a Line Chart and an Area Chart?

Line Chart: Shows trends over time using a continuous line.
Area Chart: Similar to a Line Chart but fills the area below the line with color, emphasizing volume.

Example:
A Line Chart shows monthly stock prices over time.
An Area Chart shows cumulative sales trends over time.

25. How do you merge two DataFrames in Pandas?

You can use merge() for SQL-like joins:

import pandas as pd df1 = pd.DataFrame({"ID": [1, 2, 3], "Name": ["Alice", "Bob", "Charlie"]}) df2 = pd.DataFrame({"ID": [1, 2, 4], "Salary": [50000, 60000, 70000]}) # INNER JOIN df_inner = df1.merge(df2, on="ID", how="inner") # LEFT JOIN df_left = df1.merge(df2, on="ID", how="left") print(df_inner) 

Common merge types:
how="inner" → Returns only matching rows.
how="left" → Keeps all rows from the left DataFrame.
how="right" → Keeps all rows from the right DataFrame.
how="outer" → Returns all rows, filling missing values with NaN.

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Day 29: Query Performance Tuning – Optimize SQL Queries

1. Why Optimize SQL Queries?

Efficient queries reduce execution time, improve database performance, and minimize resource usage.

2. Key Techniques for Query Optimization

📌 1. Use Indexes to Speed Up Searches

Indexes improve query performance by reducing the number of scanned rows.

✅ Example: Creating an Index

CREATE INDEX idx_employee_name ON Employees(Name); 

✅ Check Existing Indexes

SELECT * FROM sys.indexes WHERE object_id = OBJECT_ID('Employees'); 

📌 2. Avoid SELECT * (Specify Columns Instead)

Fetching all columns increases memory usage and slows down queries.

❌ Bad Query:
SELECT * FROM Employees;

✅ Optimized Query:
SELECT Name, Salary FROM Employees;

📌 3. Use WHERE Instead of HAVING for Filtering

WHERE filters before grouping, while HAVING filters after aggregation, making WHERE more efficient.

❌ Bad Query:

SELECT Department, COUNT(*) FROM Employees GROUP BY Department HAVING COUNT(*) > 5; 

✅ Optimized Query:

SELECT Department, COUNT(*) FROM Employees WHERE Department IS NOT NULL GROUP BY Department; 

📌 4. Use EXISTS Instead of IN for Large Datasets

EXISTS stops searching after the first match, whereas IN scans the entire list.

❌ Bad Query:

SELECT * FROM Employees WHERE DepartmentID IN (SELECT DepartmentID FROM Departments); 

✅ Optimized Query:

SELECT * FROM Employees WHERE EXISTS (SELECT 1 FROM Departments WHERE Departments.DepartmentID = Employees.DepartmentID);

📌 5. Optimize JOINS by Selecting Required Columns

Avoid unnecessary columns and filters in JOIN queries.

❌ Bad Query:

SELECT * FROM Employees e JOIN Departments d ON e.DepartmentID = d.DepartmentID; 

✅ Optimized Query:

SELECT e.Name, d.DepartmentName FROM Employees e JOIN Departments d ON e.DepartmentID = d.DepartmentID; 

✅ Action Plan for Today:

1️⃣ Create an index for a frequently searched column.
2️⃣ Rewrite a query to avoid SELECT *.
3️⃣ Experiment with EXISTS vs IN for filtering data.

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