If you’re a Data Analyst, chances are you use 𝐒𝐐𝐋 every single day. And if you’re preparing for interviews, you’ve probably realized that it's not just about writing queries it's about writing smart, efficient, and scalable ones.

1. 𝐁𝐫𝐞𝐚𝐤 𝐈𝐭 𝐃𝐨𝐰𝐧 𝐰𝐢𝐭𝐡 𝐂𝐓𝐄𝐬 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬)

Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views — great for simplifying logic and improving collaboration across your team.

2. 𝐔𝐬𝐞 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬

Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals — all within the same query. Total

3. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬)

Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.

4. 𝐈𝐧𝐝𝐞𝐱𝐞𝐬 & 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧

Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.

5. 𝐉𝐨𝐢𝐧𝐬 𝐯𝐬. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬

Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.

6. 𝐂𝐀𝐒𝐄 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬:

Want to categorize or bucket data without creating a separate table? Use CASE. It’s ideal for conditional logic, custom labels, and grouping in a single query.

7. 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘

Most analytics questions start with "how many", "what’s the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.

8. 𝐃𝐚𝐭𝐞𝐬 𝐀𝐫𝐞 𝐀𝐥𝐰𝐚𝐲𝐬 𝐓𝐫𝐢𝐜𝐤𝐲

Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.

9. 𝐒𝐞𝐥𝐟-𝐉𝐨𝐢𝐧𝐬 & 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝐟𝐨𝐫 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐞𝐬

Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.


You don’t need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.

Complete Roadmap to learn SQL in 2025 👇👇

1. Basic Concepts
- Understand databases and SQL.
- Learn data types (INT, VARCHAR, DATE, etc.).

2. Basic Queries
- SELECT: Retrieve data.
- WHERE: Filter results.
- ORDER BY: Sort results.
- LIMIT: Restrict results.

3. Aggregate Functions
- COUNT, SUM, AVG, MAX, MIN.
- Use GROUP BY to group results.

4. Joins
- INNER JOIN: Combine rows from two tables based on a condition.
- LEFT JOIN: Include all rows from the left table.
- RIGHT JOIN: Include all rows from the right table.
- FULL OUTER JOIN: Include all rows from both tables.

5. Subqueries
- Use nested queries for complex data retrieval.

6. Data Manipulation
- INSERT: Add new records.
- UPDATE: Modify existing records.
- DELETE: Remove records.

7. Schema Management
- CREATE TABLE: Define new tables.
- ALTER TABLE: Modify existing tables.
- DROP TABLE: Remove tables.

8. Indexes
- Understand how to create and use indexes to optimize queries.

9. Views
- Create and manage views for simplified data access.

10. Transactions
- Learn about COMMIT and ROLLBACK for data integrity.

11. Advanced Topics
- Stored Procedures: Automate complex tasks.
- Triggers: Execute actions automatically based on events.
- Normalization: Understand database design principles.

12. Practice
- Use platforms like LeetCode, HackerRank, or learnsql for hands-on practice.

Here are some free resources to learn  & practice SQL 👇👇

SQL For Data Analysis: https://news.1rj.ru/str/sqlanalyst

For Practice- https://stratascratch.com/?via=free

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

Top 10 SQL Projects with Datasets: https://news.1rj.ru/str/DataPortfolio/16

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ENJOY LEARNING 👍👍

Some practical interview questions for an entry-level data analyst role in Power BI:

•  Data Import Scenario: Describe how you would import data from various sources (Excel,SQL Server, CSV) into Power BI.

•  Data Cleaning Exercise: In Power BI, how would you handle a dataset with missing values and inconsistent formats to prepare it for analysis?

•  Handling Large Datasets: If you're working with a very large dataset in Power BI that is causing performance issues, what strategies would you use to optimize the data processing?

•  Calculated Columns and Measures: Explain how you would use calculated columns and measures in Power BI to analyze year-over-year growth.

•  Data Modeling Case: You have sales data in one table and customer data in another. How would you create a data model in Power BI to analyze customer purchase behavior?

•  Visualizations Task: Describe your approach to visualizing sales data in Power BI to highlight trends over time across different product categories.

•  Dashboard Optimization: A Power BI dashboard is loading slowly. What steps would you take to diagnose and improve its performance?

•  Data Refresh Scheduling: How would you set up and manage automatic data refreshes for a weekly sales report in Power BI?

•  Row-Level Security: How would you implement user-level security in Power BI for a report that needs different access levels for various users?

•  Troubleshooting a DAX Calculation: If a DAX formula in Power BI is not returning the expected results, how would you go about troubleshooting it?

•  Integration with Other Tools: Describe a scenario where you integrated Power BI with another tool or service (like Excel, Azure, or a web API).

•  Interactive Reports Creation: How would you design a Power BI report that allows user interaction, such as using slicers or drill-down features?

•  Adapting to Data Source Changes: If there are structural changes in a primary data source (like addition or removal of columns), how would you update your Power BI reports and dashboards?

•  Sharing Reports: Explain how you would share a report with your team and set up access controls using Power BI Service.
•  SQL Queries in Power BI: How do you use SQL queries in Power BI for advanced data transformation or analysis?

•  Error Handling in Data Sources: How do you manage and resolve errors in data sources or calculations in Power BI?

•  Custom Visuals Usage: Have you used custom visuals in Power BI? Describe the scenario and the benefit

•  Collaboration in Power BI Projects: Discuss how you have worked with others on a Power BI project. What collaboration tools or features within Power BI did you utilize?

•  Performance Tuning: What steps do you take to ensure your Power BI reports are performing optimally when dealing with large datasets or complex calculations?

Power BI Interviews 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

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Data Analytics Roadmap

1. Fundamentals of Statistics and Mathematics
  - Understand denoscriptive statistics: mean, median, mode, variance, standard deviation.
  - Basics of probability theory.
  - Hypothesis testing and statistical inference.
  - Some linear algebra and calculus basics (optional depending on needs).

2. Learn Excel and Google Sheets
  - Master spreadsheet basics: formulas, functions, pivot tables.
  - Data visualization with charts and graphs.
  - Basic automation with macros and advanced formulas.

3. Programming for Data Analytics
  - Choose Python or R as your main analytical programming language.
  - Python libraries: pandas (data manipulation), numpy (numerical operations), matplotlib and seaborn (visualization).
  - For R: dplyr, ggplot2.
  - Use Jupyter Notebook (Python) or RStudio for coding environment.

4. Databases and SQL
  - Understand relational databases and how data is stored.
  - Learn SQL queries: SELECT, JOIN, GROUP BY, aggregation functions.
  - Practice querying real databases.

5. Data Visualization Tools
  - Learn tools like Tableau, Power BI, or Looker.
  - Build interactive dashboards and reports.
  - Understand best practices for effective visualization (color, simplicity, clarity).

6. Business Analytics Fundamentals
  - Understand business processes and workflows.
  - Define Key Performance Indicators (KPIs).
  - Translate business questions into analytical problems.

7. Data Cleaning and Preprocessing
  - Handle missing, inconsistent, and outlier data.
  - Data transformation and normalization techniques.
  - Use Python (pandas) or other tools to clean data effectively.

8. Basics of Machine Learning (Optional for Advanced Skills)
  - Understand simple models: linear regression, classification.
  - Use scikit-learn library in Python.
  - Apply models for forecasting and clustering.

9. Hands-on Practice and Projects
  - Work on real datasets from Kaggle or other platforms.
  - Build a portfolio showcasing your data analysis projects.
  - Participate in data competitions and hackathons.

10. Communication and Reporting
  - Develop skills in presenting data insights clearly.
  - Create compelling reports and presentations.
  - Learn to work with stakeholders to tailor insights.

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SQL Basics for Beginners: Must-Know Concepts

1. What is SQL?
SQL (Structured Query Language) is a standard language used to communicate with databases. It allows you to query, update, and manage relational databases by writing simple or complex queries.

2. SQL Syntax
SQL is written using statements, which consist of keywords like SELECT, FROM, WHERE, etc., to perform operations on the data.
- SQL keywords are not case-sensitive, but it's common to write them in uppercase (e.g., SELECT, FROM).

3. SQL Data Types
Databases store data in different formats. The most common data types are:
- INT (Integer): For whole numbers.
- VARCHAR(n) or TEXT: For storing text data.
- DATE: For dates.
- DECIMAL: For precise decimal values, often used in financial calculations.

4. Basic SQL Queries
Here are some fundamental SQL operations:

- SELECT Statement: Used to retrieve data from a database.

     SELECT column1, column2 FROM table_name;
     

- WHERE Clause: Filters data based on conditions.

     SELECT * FROM table_name WHERE condition;
     

- ORDER BY: Sorts data in ascending (ASC) or descending (DESC) order.

     SELECT column1, column2 FROM table_name ORDER BY column1 ASC;
     

- LIMIT: Limits the number of rows returned.

     SELECT * FROM table_name LIMIT 5;
     

5. Filtering Data with WHERE Clause
The WHERE clause helps you filter data based on a condition:

   SELECT * FROM employees WHERE salary > 50000;
   

You can use comparison operators like:
- =: Equal to
- >: Greater than
- <: Less than
- LIKE: For pattern matching

6. Aggregating Data
SQL provides functions to summarize or aggregate data:
- COUNT(): Counts the number of rows.

     SELECT COUNT(*) FROM table_name;
     

- SUM(): Adds up values in a column.

     SELECT SUM(salary) FROM employees;
     

- AVG(): Calculates the average value.

     SELECT AVG(salary) FROM employees;
     

- GROUP BY: Groups rows that have the same values into summary rows.

     SELECT department, AVG(salary) FROM employees GROUP BY department;
     

7. Joins in SQL
Joins combine data from two or more tables:
- INNER JOIN: Retrieves records with matching values in both tables.

     SELECT employees.name, departments.department
     FROM employees
     INNER JOIN departments
     ON employees.department_id = departments.id;
     

- LEFT JOIN: Retrieves all records from the left table and matched records from the right table.

     SELECT employees.name, departments.department
     FROM employees
     LEFT JOIN departments
     ON employees.department_id = departments.id;
     

8. Inserting Data
To add new data to a table, you use the INSERT INTO statement:

   INSERT INTO employees (name, position, salary) VALUES ('John Doe', 'Analyst', 60000);
   

9. Updating Data
You can update existing data in a table using the UPDATE statement:

   UPDATE employees SET salary = 65000 WHERE name = 'John Doe';
   

10. Deleting Data
To remove data from a table, use the DELETE statement:

    DELETE FROM employees WHERE name = 'John Doe';
    

Here you can find essential SQL Interview Resources👇
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𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗦𝗤𝗟 𝗹𝗲𝘀𝘀𝗼𝗻 𝘆𝗼𝘂’𝗹𝗹 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝘁𝗼𝗱𝗮𝘆:

Master the core SQL statements—they are the building blocks of every powerful query you'll write.

-> SELECT retrieves data efficiently and accurately. Remember, clarity starts with understanding the result set you need.

-> WHERE filters data to show only the insights that matter. Precision is key.

-> CREATE, INSERT, UPDATE, DELETE allow you to mold your database like an artist—design it, fill it, improve it, or even clean it up.

In a world where everyone wants to take, give knowledge back.

Become an alchemist of your life. Learn, share, and build solutions.

Always follow best practices in SQL to avoid mistakes like missing WHERE in an UPDATE or DELETE. These oversights can cause chaos!

Without WHERE, you risk updating or deleting entire datasets unintentionally. That's a costly mistake.

But with proper syntax and habits, your databases will be secure, efficient, and insightful.

SQL is not just a skill—it's a mindset of precision, logic, and innovation.

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

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#sql

🔥 Top SQL Projects for Data Analytics 🚀

If you're preparing for a Data Analyst role or looking to level up your SQL skills, working on real-world projects is the best way to learn!

Here are some must-do SQL projects to strengthen your portfolio. 👇

🟢 Beginner-Friendly SQL Projects (Great for Learning Basics)

✅ Employee Database Management – Build and query HR data 📊
✅ Library Book Tracking – Create a database for book loans and returns
✅ Student Grading System – Analyze student performance data
✅ Retail Point-of-Sale System – Work with sales and transactions 💰
✅ Hotel Booking System – Manage customer bookings and check-ins 🏨

🟡 Intermediate SQL Projects (For Stronger Querying & Analysis)

⚡ E-commerce Order Management – Analyze order trends & customer data 🛒
⚡ Sales Performance Analysis – Work with revenue, profit margins & KPIs 📈
⚡ Inventory Control System – Optimize stock tracking 📦
⚡ Real Estate Listings – Manage and analyze property data 🏡
⚡ Movie Rating System – Analyze user reviews & trends 🎬

🔵 Advanced SQL Projects (For Business-Level Analytics)

🔹 Social Media Analytics – Track user engagement & content trends
🔹 Insurance Claim Management – Fraud detection & risk assessment
🔹 Customer Feedback Analysis – Perform sentiment analysis on reviews ⭐
🔹 Freelance Job Platform – Match freelancers with project opportunities
🔹 Pharmacy Inventory System – Optimize stock levels & prenoscriptions

🔴 Expert-Level SQL Projects (For Data-Driven Decision Making)

🔥 Music Streaming Analysis – Study user behavior & song trends 🎶
🔥 Healthcare Prenoscription Tracking – Identify patterns in medicine usage
🔥 Employee Shift Scheduling – Optimize workforce efficiency ⏳
🔥 Warehouse Stock Control – Manage supply chain data efficiently
🔥 Online Auction System – Analyze bidding patterns & sales performance 🛍️

🔗 Pro Tip: If you're applying for Data Analyst roles, pick 3-4 projects, clean the data, and create interactive dashboards using Power BI/Tableau to showcase insights!

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