5 Essential Portfolio Projects for data analysts 😄👇

1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.

Free websites to find datasets: https://news.1rj.ru/str/DataPortfolio/8

2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.

3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.

4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.

5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.

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Day 17: Indexes

1. What are Indexes?

Indexes are database objects that speed up the retrieval of data from tables. They function like a book’s index, allowing quick access to specific rows.

2. Types of Indexes

Clustered Index:

Alters the physical order of table data.
Each table can have only one clustered index.
Example: Index on a primary key.

Non-Clustered Index:

Does not change the physical order of data but creates a separate structure for quick lookups.
A table can have multiple non-clustered indexes.

Unique Index:

Ensures that values in a column or group of columns are unique.

Composite Index:

An index on multiple columns for queries involving those columns.

3. Why Use Indexes?

Improve query performance for large datasets.

Speed up searches, joins, and filtering.

Enforce uniqueness with unique indexes.

4. Syntax for Creating Indexes
a) Clustered Index

Automatically created when a primary key is defined:

CREATE CLUSTERED INDEX IndexName ON TableName (ColumnName);

Example:

CREATE CLUSTERED INDEX IDX_EmployeeID ON Employees (EmployeeID);

b) Non-Clustered Index

CREATE NONCLUSTERED INDEX IndexName ON TableName (ColumnName);

Example:

CREATE NONCLUSTERED INDEX IDX_Salary ON Employees (Salary);

c) Unique Index

CREATE UNIQUE INDEX IndexName ON TableName (ColumnName);

Example:

CREATE UNIQUE INDEX IDX_UniqueEmail ON Employees (Email);

5. Viewing Indexes

To list all indexes on a table:
EXEC sp_helpindex 'TableName';

6. Dropping Indexes

To remove an index:
DROP INDEX IndexName ON TableName;

Example:

DROP INDEX IDX_Salary ON Employees;

7. Limitations of Indexes

Slows down INSERT, UPDATE, and DELETE operations due to maintenance.

Requires additional storage.
Too many indexes can degrade performance.

8. Best Practices:

Use indexes for frequently queried columns.

Avoid indexing small tables or columns with low selectivity.

Regularly monitor and optimize index usage.

Action Steps:

Create clustered and non-clustered indexes for common queries in your database.
Check the performance difference using indexed vs non-indexed columns.
Drop unused or redundant indexes.

🔝 SQL 30 Days Challenge

Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst

Like this post if you want me to continue this SQL series 👍♥️

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If you're new to data analytics, start with SQL and Excel. Focus on one at a time, master it, and then move on to the next. Don’t juggle multiple things at once; finish what you start before taking up something new.

SQL Topics:
SELECT statements
WHERE clause
GROUP BY and HAVING
JOINS (INNER, LEFT, RIGHT, FULL)
Aggregation functions (SUM, COUNT, AVG, etc.)
Common Table Expressions (CTEs)
Subqueries

Excel Topics:
Basic formulas (SUM, IF, VLOOKUP, etc.)
Data cleaning techniques
Pivot tables
Conditional formatting
Charts and graphs
Data validation
Advanced features like Power Query and Macros

SQL Learning Series: https://news.1rj.ru/str/sqlspecialist/567

Excel Learning Series: https://news.1rj.ru/str/sqlspecialist/664

Hope it helps :)

Day 18: Transactions and ACID Properties

1. What are Transactions?

A transaction is a sequence of operations performed as a single unit of work. It ensures data consistency, even in cases of failure.

2. Key Characteristics of Transactions:

Atomicity: Ensures all operations within the transaction are completed or none at all.
Consistency: Guarantees the database remains in a valid state before and after the transaction.
Isolation: Transactions are independent and do not interfere with each other.
Durability: Once a transaction is committed, the changes are permanent.

3. Syntax for Transactions:

Start a Transaction
BEGIN TRANSACTION;

Commit a Transaction
Saves the changes made during the transaction:
COMMIT;

Rollback a Transaction
Reverts the changes made during the transaction:
ROLLBACK;

4. Example

Without Transactions

If an error occurs, only part of the data may be saved, causing inconsistencies:

UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2;

With Transactions

Ensures both operations are completed or none:

BEGIN TRANSACTION; UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2; IF @@ERROR <> 0 ROLLBACK; ELSE COMMIT;

If either UPDATE fails, the entire transaction is rolled back.

5. Savepoints

Savepoints allow partial rollback within a transaction:

BEGIN TRANSACTION; UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; SAVE TRANSACTION SavePoint1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2; ROLLBACK TRANSACTION SavePoint1; -- Reverts the second update only COMMIT;

6. Isolation Levels:

Control how transactions interact with each other:

Read Uncommitted: Allows dirty reads (data not yet committed).

Read Committed: Prevents dirty reads (default in most databases).

Repeatable Read: Prevents dirty reads and ensures no changes to data during the transaction.

Serializable: Ensures complete isolation but may reduce performance.
Set the isolation level:

SET TRANSACTION ISOLATION LEVEL SERIALIZABLE; BEGIN TRANSACTION;

7. Best Practices:

Keep transactions short to reduce locking and improve performance.

Always use COMMIT or ROLLBACK explicitly.

Test for errors within transactions to handle rollbacks.

Use appropriate isolation levels based on requirements.

Action Steps:

Write a transaction to transfer money between two accounts.

Experiment with savepoints for partial rollbacks.

Explore the effect of different isolation levels on concurrent transactions.

🔝 SQL 30 Days Challenge

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As a data analyst, your primary goal isn’t just to create dashboards, write SQL queries, build pivot tables, generate reports, or clean data. While these are important technical tasks, they are merely tools in your toolkit.

Your real focus should be on solving business problems and providing actionable insights. This means understanding the context of the problem, identifying the right data to analyze, and interpreting the results in a way that adds value to the business. Use these technical skills strategically to answer key questions, identify opportunities, and help drive informed decision-making.

Always remember, the ultimate purpose of your work is to contribute to business growth and efficiency by solving problems, not just completing tasks.

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Day 19: Stored Procedures

1. What is a Stored Procedure?

A stored procedure is a precompiled set of SQL statements that can be executed with a single call. It helps improve performance, maintainability, and security.

2. Why Use Stored Procedures?

✅ Reduce redundant code.
✅ Improve query performance.
✅ Enhance security by controlling access to direct queries.
✅ Allow parameterized queries for dynamic execution.

3. Syntax for Creating a Stored Procedure

CREATE PROCEDURE ProcedureName AS BEGIN -- SQL statements END;

Example:

A procedure to fetch all employees:

CREATE PROCEDURE GetAllEmployees AS BEGIN SELECT * FROM Employees; END; 
Execute the procedure:
EXEC GetAllEmployees;

4. Stored Procedures with Parameters

Stored procedures can take input and output parameters.

Example:

Procedure to fetch employees based on department ID:

CREATE PROCEDURE GetEmployeesByDept @DeptID INT AS BEGIN SELECT * FROM Employees WHERE DepartmentID = @DeptID; END; 
Execute with a parameter:
EXEC GetEmployeesByDept @DeptID = 2;

5. Stored Procedure with Output Parameters

Used to return values from a procedure.

Example:

A procedure to count employees in a department:

CREATE PROCEDURE GetEmployeeCountByDept @DeptID INT, @EmpCount INT OUTPUT AS BEGIN SELECT @EmpCount = COUNT(*) FROM Employees WHERE DepartmentID = @DeptID; END; 

Call the procedure and get the output value:

DECLARE @Count INT; EXEC GetEmployeeCountByDept @DeptID = 2, @EmpCount = @Count OUTPUT; PRINT @Count; 

6. Modifying and Dropping a Stored Procedure

Modify an existing procedure:

ALTER PROCEDURE ProcedureName AS BEGIN 
-- Updated SQL statements END; 
Drop a stored procedure:
DROP PROCEDURE ProcedureName; 

7. Best Practices for Stored Procedures

Use meaningful names for easy identification.

Avoid SELECT ; instead, specify required columns.

Use parameters instead of hardcoded values.

Handle errors using TRY...CATCH.

Action Steps:

Create a stored procedure to insert a new employee into the Employees table.

Write a procedure with an input parameter for filtering records.

Experiment with an output parameter to return calculated values.

🔝 SQL 30 Days Challenge

Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst

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Certificates have their own value in proving your skills, but completing a course just for the sake of a certificate won’t help you at all.

What truly matters is how well you understand and apply what you’ve learned.

Whatever course you take, focus on learning, practicing, and mastering the skill rather than just collecting certificates. The real proof of your expertise is in solving real-world problems, not in the number of certificates you have.

That's why I always recommend building data analytics project. Projects help you apply your knowledge, work with real datasets, and tackle challenges similar to what you’d face in a real job.

They also showcase your problem-solving skills, creativity, and ability to draw meaningful insights—things no certificate alone can prove.

Here, you can find free resources to build your own data portfolio
👇👇
https://news.1rj.ru/str/DataPortfolio

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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

Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst

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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?

🔝 SQL 30 Days Challenge

Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst

Like this post if you want me to continue this SQL series 👍♥️

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Thanks for the amazing response on last poll. Because of the huge request, I have decided to post important data analyst questions in the channel on daily basis 😊

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?

🔝 SQL 30 Days Challenge

Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst

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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')

I have curated best 80+ top-notch Data Analytics Resources 👇👇
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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.

🔝 SQL 30 Days Challenge

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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.

🔝 SQL 30 Days Challenge

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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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