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Data Analytics

🚀 How to Land a Data Analyst Job Without Experience?

Many people asked me this question, so I thought to answer it here to help everyone. Here is the step-by-step approach i would recommend:

✅ Step 1: Master the Essential Skills

You need to build a strong foundation in:

🔹 SQL – Learn how to extract and manipulate data
🔹 Excel – Master formulas, Pivot Tables, and dashboards
🔹 Python – Focus on Pandas, NumPy, and Matplotlib for data analysis
🔹 Power BI/Tableau – Learn to create interactive dashboards
🔹 Statistics & Business Acumen – Understand data trends and insights

Where to learn?
📌 Google Data Analytics Course
📌 SQL – Mode Analytics (Free)
📌 Python – Kaggle or DataCamp

✅ Step 2: Work on Real-World Projects

Employers care more about what you can do rather than just your degree. Build 3-4 projects to showcase your skills.

🔹 Project Ideas:

✅ Analyze sales data to find profitable products
✅ Clean messy datasets using SQL or Python
✅ Build an interactive Power BI dashboard
✅ Predict customer churn using machine learning (optional)

Use Kaggle, Data.gov, or Google Dataset Search to find free datasets!

✅ Step 3: Build an Impressive Portfolio

Once you have projects, showcase them! Create:
📌 A GitHub repository to store your SQL/Python code
📌 A Tableau or Power BI Public Profile for dashboards
📌 A Medium or LinkedIn post explaining your projects

A strong portfolio = More job opportunities! 💡

✅ Step 4: Get Hands-On Experience

If you don’t have experience, create your own!
📌 Do freelance projects on Upwork/Fiverr
📌 Join an internship or volunteer for NGOs
📌 Participate in Kaggle competitions
📌 Contribute to open-source projects

Real-world practice > Theoretical knowledge!

✅ Step 5: Optimize Your Resume & LinkedIn Profile

Your resume should highlight:
✔️ Skills (SQL, Python, Power BI, etc.)
✔️ Projects (Brief denoscriptions with links)
✔️ Certifications (Google Data Analytics, Coursera, etc.)

Bonus Tip:
🔹 Write "Data Analyst in Training" on LinkedIn
🔹 Start posting insights from your learning journey
🔹 Engage with recruiters & join LinkedIn groups

✅ Step 6: Start Applying for Jobs

Don’t wait for the perfect job—start applying!
📌 Apply on LinkedIn, Indeed, and company websites
📌 Network with professionals in the industry
📌 Be ready for SQL & Excel assessments

Pro Tip: Even if you don’t meet 100% of the job requirements, apply anyway! Many companies are open to hiring self-taught analysts.

You don’t need a fancy degree to become a Data Analyst. Skills + Projects + Networking = Your job offer!

🔥 Your Challenge: Start your first project today and track your progress!

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Hope it helps :)

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9.35K viewsedited 09:45

Data Analytics

𝟓 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐀𝐩𝐩𝐥𝐲 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐉𝐨𝐛𝐬

🔸𝐔𝐬𝐞 𝐉𝐨𝐛 𝐏𝐨𝐫𝐭𝐚𝐥𝐬
Job boards like LinkedIn & Naukari are great portals to find jobs.

Set up job alerts using keywords like “Data Analyst” so you’ll get notified as soon as something new comes up.

🔸𝐓𝐚𝐢𝐥𝐨𝐫 𝐘𝐨𝐮𝐫 𝐑𝐞𝐬𝐮𝐦𝐞
Don’t send the same resume to every job.

Take time to highlight the skills and tools that the job denoscription asks for, like SQL, Power BI, or Excel. It helps your resume get noticed by software that scans for keywords (ATS).

🔸𝐔𝐬𝐞 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧
Connect with recruiters and employees from your target companies. Ask for referrals when any jib opening is poster

Engage with data-related content and share your own work (like project insights or dashboards).

🔸𝐂𝐡𝐞𝐜𝐤 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐖𝐞𝐛𝐬𝐢𝐭𝐞𝐬 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲
Most big companies post jobs directly on their websites first.

Create a list of companies you’re interested in and keep checking their careers page. It’s a good way to find openings early before they post on job portals.

🔸𝐅𝐨𝐥𝐥𝐨𝐰 𝐔𝐩 𝐀𝐟𝐭𝐞𝐫 𝐀𝐩𝐩𝐥𝐲𝐢𝐧𝐠
After applying to a job, it helps to follow up with a quick message on LinkedIn. You can send a polite note to recruiter and aks for the update on your candidature.

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9.14K viewsedited 06:42

Data Analytics

I see so many people jump into data analytics, excited by its popularity, only to feel lost or uninterested soon after. I get it, data isn’t for everyone, and that’s okay.

Data analytics requires a certain spark or say curiosity. You need that drive to dig deeper, to understand why things happen, to explore how data pieces connect to reveal a bigger picture. Without that spark, it’s easy to feel overwhelmed or even bored.

Before diving in, ask yourself, Do I really enjoy solving puzzles? Am I genuinely excited about numbers, patterns, and insights? If you’re curious and love learning, data can be incredibly rewarding. But if it’s just about following a trend, it might not be a fulfilling path for you.

Be honest with yourself. Find your passion, whether it’s in data or somewhere else and invest in something that truly excites you.

Hope this helps you 😊

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8.93K views18:13

Data Analytics

As a data analytics enthusiast, the end goal is not just to learn SQL, Power BI, Python, Excel, etc. but to get a job as a Data Analyst👨💻

Back then, when I was trying to switch my career into data analytics, I used to keep aside 1:00-1:30 hours of my day aside so that I can utilize those hours to search for job openings related to Data analytics and Business Intelligence.

Before going to bed, I used to utilize the first 30 minutes by going through various job portals such as naukri, LinkedIn, etc to find relevant openings and next 1 hour by collecting the keywords from the job denoscription to curate the resume accordingly and searching for profile of people who can refer me for the role.

📍 I will advise every aspiring data analyst to have a dedicated timing for searching and applying for the jobs.

📍To get into data analytics, applying for jobs is as important as learning and upskilling.

If you are not applying for the jobs, you are simply delaying your success to get into data analytics👨💻📊

Data Analytics Resources
👇👇
https://news.1rj.ru/str/DataSimplifier

Hope this helps you 😊

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9.03K viewsedited 09:11

Data Analytics

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Data Analytics

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Data Analytics

Hi guys,

Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.

For those of you who are new to this channel, here are some quick links to navigate this channel easily.

Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752

Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749

Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745

SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738

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

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

Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768

Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615

Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667

Free Data Analytics Resources 👇
https://news.1rj.ru/str/datasimplifier

You can find more resources on Medium & Linkedin

Like for more ❤️

Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.

Hope it helps :)

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8.21K viewsedited 10:28

Data Analytics

Complete SQL Topics for Data Analysts 😄👇

1. Introduction to SQL:
- Basic syntax and structure
- Understanding databases and tables

2. Querying Data:
- SELECT statement
- Filtering data using WHERE clause
- Sorting data with ORDER BY

3. Joins:
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Combining data from multiple tables

4. Aggregation Functions:
- GROUP BY
- Aggregate functions like COUNT, SUM, AVG, MAX, MIN

5. Subqueries:
- Using subqueries in SELECT, WHERE, and HAVING clauses

6. Data Modification:
- INSERT, UPDATE, DELETE statements
- Transactions and Rollback

7. Data Types and Constraints:
- Understanding various data types (e.g., INT, VARCHAR)
- Using constraints (e.g., PRIMARY KEY, FOREIGN KEY)

8. Indexes:
- Creating and managing indexes for performance optimization

9. Views:
- Creating and using views for simplified querying

10. Stored Procedures and Functions:
- Writing and executing stored procedures
- Creating and using functions

11. Normalization:
- Understanding database normalization concepts

12. Data Import and Export:
- Importing and exporting data using SQL

13. Window Functions:
- ROW_NUMBER(), RANK(), DENSE_RANK(), and others

14. Advanced Filtering:
- Using CASE statements for conditional logic

15. Advanced Join Techniques:
- Self-joins and other advanced join scenarios

16. Analytical Functions:
- LAG(), LEAD(), OVER() for advanced analytics

17. Working with Dates and Times:
- Date and time functions and formatting

18. Performance Tuning:
- Query optimization strategies

19. Security:
- Understanding SQL injection and best practices for security

20. Handling NULL Values:
- Dealing with NULL values in queries

Ensure hands-on practice on these topics to strengthen your SQL skills.

Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series 👍♥️

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7.98K views14:21

Data Analytics

📊 Excel Hack of the Week

Did you know you can use Flash Fill in Excel to automatically clean and format data without writing formulas?

📝 How to Use Flash Fill?

1️⃣ Type the first correct value manually in the adjacent column.
2️⃣ Press Ctrl + E (or go to Data > Flash Fill).
3️⃣ Excel will recognize the pattern and fill in the rest automatically!

🔍 Example:
✅ Extract first names from "John Doe" → Type "John" → Press Ctrl + E → Done!
✅ Format phone numbers from "1234567890" to "(123) 456-7890" in seconds!
✅ Convert dates from "01-02-2024" to "February 1, 2024" instantly!

📌 Bonus: Try using Flash Fill for splitting names, fixing email formats, or even extracting numbers from text.

You can join @excel_data for free Excel Resources.

Like this post for more data analytics tricks 👍♥️

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7.48K views02:02

Data Analytics

Python for Data Analysis: Must-Know Libraries 👇👇

Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.

🔥 Essential Python Libraries for Data Analysis:

✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.

📌 Example: Loading a CSV file and displaying the first 5 rows:

import pandas as pd df = pd.read_csv('data.csv') print(df.head())

✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.

📌 Example: Creating an array and performing basic operations:

import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average

✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.

📌 Example: Creating a basic bar chart:

import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show()

✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.

✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.

💡 Challenge for You!
Try writing a Python noscript that:
1️⃣ Reads a CSV file
2️⃣ Cleans missing data
3️⃣ Creates a simple visualization

React with ♥️ if you want me to post the noscript for above challenge! ⬇️

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7.87K viewsedited 06:07

Data Analytics

🔍 Real-World Data Analyst Tasks & How to Solve Them

As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro!

📌 Task 1: Cleaning Messy Data

Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats.

✅ Solution (Using Pandas in Python):

import pandas as pd  
df = pd.read_csv('sales_data.csv')  
df.drop_duplicates(inplace=True)  # Remove duplicate rows  
df.fillna(0, inplace=True)  # Fill missing values with 0  
print(df.head())

💡 Tip: Always check for inconsistent spellings and incorrect date formats!

📌 Task 2: Analyzing Sales Trends

A company wants to know which months have the highest sales.

✅ Solution (Using SQL):

SELECT MONTH(SaleDate) AS Month, SUM(Quantity * Price) AS Total_Revenue  
FROM Sales  
GROUP BY MONTH(SaleDate)  
ORDER BY Total_Revenue DESC;

💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends!

📌 Task 3: Creating a Business Dashboard

Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth.

✅ Solution (Using Power BI / Tableau):

👉 Add KPI Cards to show total sales & profit

👉 Use a Line Chart for monthly trends

👉 Create a Bar Chart for top-selling products

👉 Use Filters/Slicers for better interactivity

💡 Tip: Keep your dashboards clean, interactive, and easy to interpret!

Like this post for more content like this ♥️

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9.37K views13:18

Data Analytics

📊 Power BI / Tableau Dashboard Inspiration

🚀 Want to Build Stunning Dashboards? Try This!

Creating an interactive and insightful dashboard is a key skill for any Data Analyst. Here’s a simple Power BI / Tableau dashboard idea to practice!

📝 Project Idea: Sales Performance Dashboard

📌 Dataset: Use free datasets from Kaggle or Sample Superstore (Tableau)

📌 Key Visuals to Include:
✅ Total Sales, Profit, and Orders (KPI Cards)
✅ Sales Trend Over Time (Line Chart)
✅ Top 5 Best-Selling Products (Bar Chart)
✅ Sales by Region & Category (Map & Pie Chart)
✅ Customer Segmentation (Filters & Slicers)

💡 Pro Tips:
🔹 Use conditional formatting to highlight trends 📊
🔹 Add slicers to make the dashboard interactive 🔍
🔹 Keep colors consistent for better readability 🎨

📌 Bonus Challenge: Can you create a drill-through feature to view details by region?

Join @dataportfolio to find free data analytics projects

Like this post for more content like this ♥️

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9.6K views18:04

Data Analytics

🎯 Top 20 SQL Interview Questions You Must Know

SQL is one of the most in-demand skills for Data Analysts.

Here are 20 SQL interview questions that frequently appear in job interviews.

📌 Basic SQL Questions

1️⃣ What is the difference between INNER JOIN and LEFT JOIN?
2️⃣ How does GROUP BY work, and why do we use it?
3️⃣ What is the difference between HAVING and WHERE?
4️⃣ How do you remove duplicate rows from a table?
5️⃣ What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()?

📌 Intermediate SQL Questions

6️⃣ How do you find the second highest salary from an Employee table?
7️⃣ What is a Common Table Expression (CTE), and when should you use it?
8️⃣ How do you identify missing values in a dataset using SQL?
9️⃣ What is the difference between UNION and UNION ALL?
🔟 How do you calculate a running total in SQL?

📌 Advanced SQL Questions

1️⃣1️⃣ How does a self-join work? Give an example.
1️⃣2️⃣ What is a window function, and how is it different from GROUP BY?
1️⃣3️⃣ How do you detect and remove duplicate records in SQL?
1️⃣4️⃣ Explain the difference between EXISTS and IN.
1️⃣5️⃣ What is the purpose of COALESCE()?

📌 Real-World SQL Scenarios

1️⃣6️⃣ How do you optimize a slow SQL query?
1️⃣7️⃣ What is indexing in SQL, and how does it improve performance?
1️⃣8️⃣ Write an SQL query to find customers who have placed more than 3 orders.
1️⃣9️⃣ How do you calculate the percentage of total sales for each category?
2️⃣0️⃣ What is the use of CASE statements in SQL?

React with ♥️ if you want me to post the correct answers in next posts! ⬇️

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11.3K viewsedited 06:35

Data Analytics

🎯 Top 20 SQL Interview Questions You Must Know SQL is one of the most in-demand skills for Data Analysts. Here are 20 SQL interview questions that frequently appear in job interviews. 📌 Basic SQL Questions 1️⃣ What is the difference between INNER JOIN…

SQL Interview Questions with detailed answers:

1️⃣ What is the difference between INNER JOIN and LEFT JOIN?

INNER JOIN: It returns only the rows where there is a match between both tables.

Example:

SELECT * FROM employees INNER JOIN departments ON employees.department_id = departments.department_id;

This will only return rows where an employee has a department.

LEFT JOIN: It returns all the rows from the left table, along with matching rows from the right table. If there is no match, NULL values will be returned for the right table.

Example:

SELECT * FROM employees LEFT JOIN departments ON employees.department_id = departments.department_id;

This will return all employees, even if they don't belong to any department (NULL will be returned for department-related columns).

Like this post if you want me to continue posting all the answers 👍♥️

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9.45K viewsedited 16:39

Data Analytics

SQL Interview Questions with detailed answers:

2️⃣ How does GROUP BY work, and why do we use it?

GROUP BY is used to arrange identical data into groups, often for performing aggregation functions (like COUNT, SUM, AVG, etc.) on each group. It's typically used with aggregate functions to summarize data.

Example:
Consider a sales table:

SELECT department_id, SUM(salary) AS total_salary FROM employees GROUP BY department_id;

Explanation:
GROUP BY department_id: This groups all rows in the employees table by their department.
SUM(salary): This calculates the total salary for each department.

The result will show the department_id along with the corresponding total salary.

Why use GROUP BY?
It allows you to analyze data at different levels of granularity (e.g., department, region) by summarizing data in a meaningful way.

Like this post if you want me to continue this SQL Interview Series♥️

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8.66K viewsedited 06:37

Data Analytics

🧠 Case Study: How to Analyze a Business Problem Like a Pro

🚀 Want to solve real-world business problems? Here's how to approach it!

Data analysis isn’t just about writing queries or generating charts—it’s about solving business problems that drive key decisions.

Here’s a step-by-step guide to help you analyze business problems effectively:

📌 Step 1: Understand the Business Problem

First, understand the context. Speak with the stakeholders or team to clarify:

What is the business goal?

What data do you need to solve the problem?

What actions or decisions will the analysis lead to?

🔍 Example: A retail company wants to increase sales in a particular region. Your job is to identify the key factors affecting sales and come up with recommendations.

📌 Step 2: Gather the Right Data

After understanding the problem, ensure you have access to reliable data. This could include:

Sales data (transactions, customers, regions)

Marketing data (advertising campaigns, promotions)

External factors (economic conditions, competition)

🧠 Tip: Ensure data is clean and complete before analysis to avoid skewed results.

📌 Step 3: Analyze the Data

Now, dive into the data and perform the following tasks:

1. Data Exploration: Look for patterns, trends, and anomalies.

2. Hypothesis Testing: Identify possible causes of the problem (e.g., "Are promotions leading to an increase in sales?").

3. Segmentation Analysis: Break down the data by regions, products, customer types, etc. to identify key insights.

🧠 Example:
Use SQL to extract sales data by region and calculate monthly growth:

SELECT Region, SUM(Sales) AS Total_Sales, AVG(Sales) AS Avg_Sales
FROM Sales
GROUP BY Region;

📌 Step 4: Visualize the Insights

Once you've analyzed the data, create visualizations to make the insights clear and actionable:

💹 Use line charts for trends over time.

📊 Use bar charts to compare different segments (regions, products, etc.).

🗺 Use heatmaps for geographical analysis.

💡 Tip: Keep your visualizations simple and focused on the key insights.

📌 Step 5: Provide Recommendations

Finally, based on your analysis, provide actionable recommendations to the business.

For example: “Focus promotions on Region X, where sales are consistently lower than other regions.”

“Increase marketing spend for the high-performing products.”

Free Resources for business analysts
👇👇
https://news.1rj.ru/str/analystcommunity

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8.24K viewsedited 13:35

Data Analytics

SQL Interview Questions with detailed answers: 2️⃣ How does GROUP BY work, and why do we use it? GROUP BY is used to arrange identical data into groups, often for performing aggregation functions (like COUNT, SUM, AVG, etc.) on each group. It's typically…

SQL Interview Questions with detailed answers:

3️⃣ What is the difference between HAVING and WHERE?

WHERE: It is used to filter records before any grouping occurs. It operates on individual rows in the table.

HAVING: It is used to filter records after the grouping operation. It works on aggregated data (e.g., data created using GROUP BY).

Example:

-- Using WHERE to filter rows before grouping 
SELECT department_id, AVG(salary) AS avg_salary FROM employees WHERE salary > 50000 GROUP BY department_id; 

-- Using HAVING to filter groups after aggregation 
SELECT department_id, AVG(salary) AS avg_salary FROM employees GROUP BY department_id HAVING AVG(salary) > 60000;

Explanation:

WHERE filters rows where the salary is greater than 50,000 before grouping by department.
HAVING filters departments where the average salary is greater than 60,000 after grouping.

Key difference:
WHERE filters individual rows.
HAVING filters groups after aggregation.

Like this post if you want me to continue this SQL Interview Series♥️

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8.47K views05:02

Data Analytics

Which of the following is not a Python Library?

Anonymous Quiz

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1.74K voters10.9K views06:24

Data Analytics

Which of the following is SQL Command is used to sort results?

Anonymous Quiz

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1.56K voters11.2K views06:25

Data Analytics

Which of the following is SQL Command is used to sort results?

Guys, please check out my SQL tutorial if you're getting this wrong! 👇
https://news.1rj.ru/str/sqlspecialist/567

For the next few days, I'll be posting basic data analytics questions to ensure all my subscribers understand the essential concepts. Once I see 80%+ correct answers, we'll move on to more advanced polls and quizzes!

Hope you all succeed one day :)

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Data Analytics

SQL Interview Questions with detailed answers: 3️⃣ What is the difference between HAVING and WHERE? WHERE: It is used to filter records before any grouping occurs. It operates on individual rows in the table. HAVING: It is used to filter records after…

SQL Interview Questions with detailed answers:

4️⃣ How do you remove duplicate rows from a table?

To remove duplicate rows, you can use the DISTINCT keyword in a SELECT query.

Example:

SELECT DISTINCT column_name FROM table_name;

Explanation:
DISTINCT will return only unique rows for the specified column(s). It compares all columns in the query and removes duplicates.

For example, if you have a table of employees and some rows are repeated, using DISTINCT will only return unique employees.

Example with multiple columns:

SELECT DISTINCT first_name, last_name FROM employees;

This will return only unique combinations of first and last names.

Top 20 SQL Interview Questions

Like this post if you want me to continue this SQL Interview Series♥️

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9.51K viewsedited 10:47

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