Here are some essential SQL tips for beginners 👇👇

◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates 
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.

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Guys, Big Announcement!

We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!

I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.

This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.

Here’s what you’ll learn over the next 30 days:

Week 1: Python Fundamentals

- Variables & Data Types (Build your own bio/profile noscript)

- Operators (Mini calculator to sharpen math skills)

- Strings & String Methods (Word counter & palindrome checker)

- Lists & Tuples (Manage a grocery list like a pro)

- Dictionaries & Sets (Create your own contact book)

- Conditionals (Make a guess-the-number game)

- Loops (Multiplication tables & pattern printing)

Week 2: Functions & Logic — Make Your Code Smarter

- Functions (Prime number checker)

- Function Arguments (Tip calculator with custom tips)

- Recursion Basics (Factorials & Fibonacci series)

- Lambda, map & filter (Process lists efficiently)

- List Comprehensions (Filter odd/even numbers easily)

- Error Handling (Build a safe input reader)

- Review + Mini Project (Command-line to-do list)


Week 3: Files, Modules & OOP

- Reading & Writing Files (Save and load notes)

- Custom Modules (Create your own utility math module)

- Classes & Objects (Student grade tracker)

- Inheritance & OOP (RPG character system)

- Dunder Methods (Build a custom string class)

- OOP Mini Project (Simple bank account system)

- Review & Practice (Quiz app using OOP concepts)


Week 4: Real-World Python & APIs — Build Cool Apps

- JSON & APIs (Fetch weather data)

- Web Scraping (Extract noscripts from HTML)

- Regular Expressions (Find emails & phone numbers)

- Tkinter GUI (Create a simple counter app)

- CLI Tools (Command-line calculator with argparse)

- Automation (File organizer noscript)

- Final Project (Choose, build, and polish your app!)

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The best doesn't come from working more.

It comes from working smarter.

The most common mistakes people make,
With practical tips to avoid each:

1) Working late every night.

• Prioritize quality time with loved ones.

Understand that long hours won't be remembered as fondly as time spent with family and friends.

2) Believing more hours mean more productivity.

• Focus on efficiency.

Complete tasks in less time to free up hours for personal activities and rest.

3) Ignoring the need for breaks.

• Take regular breaks to rejuvenate your mind.

Creativity and productivity suffer without proper rest.

4) Sacrificing personal well-being.

• Maintain a healthy work-life balance.

Ensure you don't compromise your health or relationships for work.

5) Feeling pressured to constantly produce.

• Quality over quantity.

6) Neglecting hobbies and interests.

• Engage in activities you love outside of work.

This helps to keep your mind fresh and inspired.

7) Failing to set boundaries.

• Set clear work hours and stick to them.

This helps to prevent overworking and ensures you have time for yourself.

8) Not delegating tasks.

• Delegate when possible.

Sharing the workload can enhance productivity and give you more free time.

9) Overlooking the importance of sleep.

• Prioritize sleep for better performance.

A well-rested mind is more creative and effective.

10) Underestimating the impact of overworking.

• Recognize the long-term effects.

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How do analysts use SQL in a company?

SQL is every data analyst’s superpower! Here's how they use it in the real world:

Extract Data

Pull data from multiple tables to answer business questions.

Example:

SELECT name, revenue FROM sales WHERE region = 'North America';

(P.S. Avoid SELECT *—your future self (and the database) will thank you!)


Clean & Transform

Use SQL functions to clean raw data.

Think TRIM(), COALESCE(), CAST()—like giving data a fresh haircut.


Summarize & Analyze

Group and aggregate to spot trends and patterns.

GROUP BY, SUM(), AVG() – your best friends for quick insights.


Build Dashboards

Feed SQL queries into Power BI, Tableau, or Excel to create visual stories that make data talk.

Run A/B Tests

Evaluate product changes and campaigns by comparing user groups.

SQL makes sure your decisions are backed by data, not just gut feeling.


Use Views & CTEs

Simplify complex queries with Views and Common Table Expressions.

Clean, reusable, and boss-approved.


Drive Decisions

SQL powers decisions across Marketing, Product, Sales, and Finance.

When someone asks “What’s working?”—you’ve got the answers.


And remember: write smart queries, not lazy ones. Say no to SELECT * unless you really mean it!

Hit ♥️ if you want me to share more real-world examples to make data analytics easier to understand!

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📊 Data Science Essentials: What Every Data Enthusiast Should Know!

1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.

2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.

3️⃣ Use Denoscriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.

4️⃣ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.

5️⃣ Learn SQL for Efficient Data Extraction
Write optimized queries (SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases.

6️⃣ Build Strong Programming Skills
Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis.

7️⃣ Understand Machine Learning Basics
Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.

8️⃣ Learn Dashboarding & Storytelling
Power BI and Tableau help convert raw data into actionable insights for stakeholders.

🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy!

Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!

Most popular Python libraries for data visualization:

Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.

Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.

Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.

Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.

Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.

For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.

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#python

Advanced SQL Optimization Tips for Data Analysts

1. Use Proper Indexing
Create indexes on frequently queried columns to speed up data retrieval.

2. Avoid `SELECT *`
Specify only the columns you need to reduce the amount of data processed.

3. Use `WHERE` Instead of `HAVING`
Filter your data as early as possible in the query to optimize performance.

4. Limit Joins
Try to keep joins to a minimum to reduce query complexity and processing time.

5. Apply `LIMIT` or `TOP`
Retrieve only the required rows to save on resources.

6. Optimize Joins
Use INNER JOIN instead of OUTER JOIN whenever possible.

7. Use Temporary Tables
Break large, complex queries into smaller parts using temporary tables.

8. Avoid Functions on Indexed Columns
Using functions on indexed columns often prevents the index from being used.

9. Use CTEs for Readability
Common Table Expressions help simplify nested queries and improve clarity.

10. Analyze Execution Plans
Leverage execution plans to identify bottlenecks and make targeted optimizations.

Happy querying!

🔍 Best Data Analytics Roles Based on Your Graduation Background!

🚀 For Mathematics/Statistics Graduates:
🔹 Data Analyst
🔹 Statistical Analyst
🔹 Quantitative Analyst
🔹 Risk Analyst

🚀 For Computer Science/IT Graduates:
🔹 Data Scientist
🔹 Business Intelligence Developer
🔹 Data Engineer
🔹 Data Architect

🚀 For Economics/Finance Graduates:
🔹 Financial Analyst
🔹 Market Research Analyst
🔹 Economic Consultant
🔹 Data Journalist

🚀 For Business/Management Graduates:
🔹 Business Analyst
🔹 Operations Research Analyst
🔹 Marketing Analytics Manager
🔹 Supply Chain Analyst

🚀 For Engineering Graduates:
🔹 Data Scientist
🔹 Industrial Engineer
🔹 Operations Research Analyst
🔹 Quality Engineer

🚀 For Social Science Graduates:
🔹 Data Analyst
🔹 Research Assistant
🔹 Social Media Analyst
🔹 Public Health Analyst

🚀 For Biology/Healthcare Graduates:
🔹 Clinical Data Analyst
🔹 Biostatistician
🔹 Research Coordinator
🔹 Healthcare Consultant

Some of these roles may require additional certifications or upskilling in SQL, Python, Power BI, Tableau, or Machine Learning to stand out in the job market.

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Data Analyst Scenario based Question and Answers 👇👇

1. Scenario: Creating a Dynamic Sales Growth Report in Power BI
Approach:
Load Data: Import sales data and calendar tables.
Data Model: Establish a relationship between the sales and calendar tables.
Create Measures:
Current Sales: Current Sales = SUM(Sales[Amount]).
Previous Year Sales: Previous Year Sales = CALCULATE(SUM(Sales[Amount]), DATEADD(Calendar[Date], -1, YEAR)).
Sales Growth: Sales Growth = [Current Sales] - [Previous Year Sales].
Visualization:
Use Line Chart for trends.
Use Card Visual for displaying numeric growth values.
Slicers and Filters: Add slicers for selecting specific time periods.

2. Scenario: Identifying Top 5 Customers by Revenue in SQL
Approach:
Understand the Schema: Know the relevant tables and columns, e.g., Orders table with CustomerID and Revenue.
SQL Query:
SELECT TOP 5 CustomerID, SUM(Revenue) AS TotalRevenue
FROM Orders
GROUP BY CustomerID
ORDER BY TotalRevenue DESC;

3. Scenario: Creating a Monthly Sales Forecast in Power BI
Approach:
Load Historical Data: Import historical sales data.
Data Model: Ensure proper relationships.
Time Series Analysis:
Use built-in Power BI forecasting features.
Create measures for historical and forecasted sales.
Visualization:
Use a Line Chart to display historical and forecasted sales.
Adjust Forecast Parameters: Customize the forecast length and confidence intervals.

4. Scenario: Updating a SQL Table with New Data
Approach:
Understand the Schema: Identify the table and columns to be updated.
SQL Query:
UPDATE Employees
SET JobTitle = 'Senior Developer'
WHERE EmployeeID = 1234;

5. Scenario: Creating a Custom KPI in Power BI
Approach:
Define KPI: Identify the key performance indicators.
Create Measures:
Define the KPI measure using DAX.
Visualization:
Use KPI Visual or Card Visual.
Configure the target and actual values.
Conditional Formatting: Apply conditional formatting based on the KPI thresholds.

Data Analytics Resources
👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope it helps :)

SQL Essential Concepts for Data Analyst Interviews ✅

1. SQL Syntax: Understand the basic structure of SQL queries, which typically include SELECT, FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses. Know how to write queries to retrieve data from databases.

2. SELECT Statement: Learn how to use the SELECT statement to fetch data from one or more tables. Understand how to specify columns, use aliases, and perform simple arithmetic operations within a query.

3. WHERE Clause: Use the WHERE clause to filter records based on specific conditions. Familiarize yourself with logical operators like =, >, <, >=, <=, <>, AND, OR, and NOT.

4. JOIN Operations: Master the different types of joins—INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN—to combine rows from two or more tables based on related columns.

5. GROUP BY and HAVING Clauses: Use the GROUP BY clause to group rows that have the same values in specified columns and aggregate data with functions like COUNT(), SUM(), AVG(), MAX(), and MIN(). The HAVING clause filters groups based on aggregate conditions.

6. ORDER BY Clause: Sort the result set of a query by one or more columns using the ORDER BY clause. Understand how to sort data in ascending (ASC) or descending (DESC) order.

7. Aggregate Functions: Be familiar with aggregate functions like COUNT(), SUM(), AVG(), MIN(), and MAX() to perform calculations on sets of rows, returning a single value.

8. DISTINCT Keyword: Use the DISTINCT keyword to remove duplicate records from the result set, ensuring that only unique records are returned.

9. LIMIT/OFFSET Clauses: Understand how to limit the number of rows returned by a query using LIMIT (or TOP in some SQL dialects) and how to paginate results with OFFSET.

10. Subqueries: Learn how to write subqueries, or nested queries, which are queries within another SQL query. Subqueries can be used in SELECT, WHERE, FROM, and HAVING clauses to provide more specific filtering or selection.

11. UNION and UNION ALL: Know the difference between UNION and UNION ALL. UNION combines the results of two queries and removes duplicates, while UNION ALL combines all results including duplicates.

12. IN, BETWEEN, and LIKE Operators: Use the IN operator to match any value in a list, the BETWEEN operator to filter within a range, and the LIKE operator for pattern matching with wildcards (%, _).

13. NULL Handling: Understand how to work with NULL values in SQL, including using IS NULL, IS NOT NULL, and handling nulls in calculations and joins.

14. CASE Statements: Use the CASE statement to implement conditional logic within SQL queries, allowing you to create new fields or modify existing ones based on specific conditions.

15. Indexes: Know the basics of indexing, including how indexes can improve query performance by speeding up the retrieval of rows. Understand when to create an index and the trade-offs in terms of storage and write performance.

16. Data Types: Be familiar with common SQL data types, such as VARCHAR, CHAR, INT, FLOAT, DATE, and BOOLEAN, and understand how to choose the appropriate data type for a column.

17. String Functions: Learn key string functions like CONCAT(), SUBSTRING(), REPLACE(), LENGTH(), TRIM(), and UPPER()/LOWER() to manipulate text data within queries.

18. Date and Time Functions: Master date and time functions such as NOW(), CURDATE(), DATEDIFF(), DATEADD(), and EXTRACT() to handle and manipulate date and time data effectively.

19. INSERT, UPDATE, DELETE Statements: Understand how to use INSERT to add new records, UPDATE to modify existing records, and DELETE to remove records from a table. Be aware of the implications of these operations, particularly in maintaining data integrity.

20. Constraints: Know the role of constraints like PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, and CHECK in maintaining data integrity and ensuring valid data entry in your database.

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

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Top 5 Case Studies for Data Analytics: You Must Know Before Attending an Interview

1. Retail: Target's Predictive Analytics for Customer Behavior
Company: Target
Challenge: Target wanted to identify customers who were expecting a baby to send them personalized promotions.
Solution:
Target used predictive analytics to analyze customers' purchase history and identify patterns that indicated pregnancy.
They tracked purchases of items like unscented lotion, vitamins, and cotton balls.
Outcome:
The algorithm successfully identified pregnant customers, enabling Target to send them relevant promotions.
This personalized marketing strategy increased sales and customer loyalty.

2. Healthcare: IBM Watson's Oncology Treatment Recommendations
Company: IBM Watson
Challenge: Oncologists needed support in identifying the best treatment options for cancer patients.
Solution:
IBM Watson analyzed vast amounts of medical data, including patient records, clinical trials, and medical literature.
It provided oncologists with evidencebased treatment recommendations tailored to individual patients.
Outcome:
Improved treatment accuracy and personalized care for cancer patients.
Reduced time for doctors to develop treatment plans, allowing them to focus more on patient care.

3. Finance: JP Morgan Chase's Fraud Detection System
Company: JP Morgan Chase
Challenge: The bank needed to detect and prevent fraudulent transactions in realtime.
Solution:
Implemented advanced machine learning algorithms to analyze transaction patterns and detect anomalies.
The system flagged suspicious transactions for further investigation.
Outcome:
Significantly reduced fraudulent activities.
Enhanced customer trust and satisfaction due to improved security measures.

4. Sports: Oakland Athletics' Use of Sabermetrics
Team: Oakland Athletics (Moneyball)
Challenge: Compete with larger teams with higher budgets by optimizing player performance and team strategy.
Solution:
Used sabermetrics, a form of advanced statistical analysis, to evaluate player performance and potential.
Focused on undervalued players with high onbase percentages and other key metrics.
Outcome:
Achieved remarkable success with a limited budget.
Revolutionized the approach to team building and player evaluation in baseball and other sports.

5. Ecommerce: Amazon's Recommendation Engine
Company: Amazon
Challenge: Enhance customer shopping experience and increase sales through personalized recommendations.
Solution:
Implemented a recommendation engine using collaborative filtering, which analyzes user behavior and purchase history.
The system suggests products based on what similar users have bought.
Outcome:
Increased average order value and customer retention.
Significantly contributed to Amazon's revenue growth through crossselling and upselling.

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SQL Interview Questions with Answers

1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.

2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.

3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.

4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY

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Essential Excel Functions for Data Analysts 🚀

1️⃣ Basic Functions

SUM() – Adds a range of numbers. =SUM(A1:A10)

AVERAGE() – Calculates the average. =AVERAGE(A1:A10)

MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10)


2️⃣ Logical Functions

IF() – Conditional logic. =IF(A1>50, "Pass", "Fail")

IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C")

AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100)


3️⃣ Text Functions

LEFT() / RIGHT() / MID() – Extract text from a string.

=LEFT(A1, 3) (First 3 characters)

=MID(A1, 3, 2) (2 characters from the 3rd position)


LEN() – Counts characters. =LEN(A1)

TRIM() – Removes extra spaces. =TRIM(A1)

UPPER() / LOWER() / PROPER() – Changes text case.


4️⃣ Lookup Functions

VLOOKUP() – Searches for a value in a column.

=VLOOKUP(1001, A2:B10, 2, FALSE)


HLOOKUP() – Searches in a row.

XLOOKUP() – Advanced lookup replacing VLOOKUP.

=XLOOKUP(1001, A2:A10, B2:B10, "Not Found")



5️⃣ Date & Time Functions

TODAY() – Returns the current date.

NOW() – Returns the current date and time.

YEAR(), MONTH(), DAY() – Extracts parts of a date.

DATEDIF() – Calculates the difference between two dates.


6️⃣ Data Cleaning Functions

REMOVE DUPLICATES – Found in the "Data" tab.

CLEAN() – Removes non-printable characters.

SUBSTITUTE() – Replaces text within a string.

=SUBSTITUTE(A1, "old", "new")



7️⃣ Advanced Functions

INDEX() & MATCH() – More flexible alternative to VLOOKUP.

TEXTJOIN() – Joins text with a delimiter.

UNIQUE() – Returns unique values from a range.

FILTER() – Filters data dynamically.

=FILTER(A2:B10, B2:B10>50)



8️⃣ Pivot Tables & Power Query

PIVOT TABLES – Summarizes data dynamically.

GETPIVOTDATA() – Extracts data from a Pivot Table.

POWER QUERY – Automates data cleaning & transformation.


You can find Free Excel Resources here: https://news.1rj.ru/str/excel_data

Hope it helps :)

#dataanalytics

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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