Power BI interview questions and answers 😄👇

1. Question: What is Power BI?

   Answer: Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their reports and dashboards.

2. Question: Differentiate between Power BI Desktop, Power BI Service, and Power BI Mobile.

   Answer: Power BI Desktop is used for creating reports, Power BI Service (or Power BI Online) is the cloud service for sharing and collaborating on reports, and Power BI Mobile allows users to access reports on mobile devices.

3. Question: Explain the role of Power Query in Power BI.

   Answer: Power Query is used for data transformation and shaping. It allows users to connect to various data sources, clean and transform data before loading it into Power BI for analysis.

4. Question: What is DAX in Power BI, and why is it important?

   Answer: DAX (Data Analysis Expressions) is a formula language used for creating custom calculations in Power BI. It is important as it enables users to create sophisticated measures and calculated columns.

5. Question: How do you create relationships between tables in Power BI?

   Answer: In Power BI Desktop, go to the "Model" view, drag and drop fields from one table to another to create relationships based on common keys.

6. Question: What is the difference between a calculated column and a measure in Power BI?

   Answer: A calculated column is a column added to a table, computed row by row, while a measure is a formula applied to a set of data, providing a dynamic calculation based on the context.

7. Question: How can you implement row-level security in Power BI?

   Answer: Row-level security in Power BI can be implemented by creating roles in Power BI Desktop and defining filters at the row level based on user roles.

8. Question: Explain the purpose of the Power BI Gateway.

   Answer: The Power BI Gateway allows for a secure connection between Power BI services and on-premises data sources. It facilitates refreshing datasets and running scheduled refreshes.

9. Question: What is a Power BI dashboard?

   Answer: A Power BI dashboard is a single-page, interactive view of your data that provides a consolidated and visualized summary of key metrics. It can include visuals, images, and live data.

10. Question: How can you share a Power BI report with others?

    Answer: Power BI reports can be shared through the Power BI service. Publish the report to the Power BI service, and then share it with specific users or distribute it widely within an organization.

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗼𝗮𝗱𝗺𝗮𝗽😍

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Data Analyst vs Data Engineer vs Data Scientist ✅

Skills required to become a Data Analyst 👇

- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.


Skills required to become a Data Engineer: 👇

- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.


Skills required to become a Data Scientist: 👇

- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.

Bonus Skills Across All Roles:

- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.

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𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍

𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇

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𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Hey guys!

I’ve been getting a lot of requests from you all asking for solid Data Analytics projects that can help you boost resume and build real skills.

So here you go —

These aren’t just “for practice,” they’re portfolio-worthy projects that show recruiters you’re ready for real-world work.

1. Sales Performance Dashboard

Tools: Excel / Power BI / Tableau
You’ll take raw sales data and turn it into a clean, interactive dashboard. Show key metrics like revenue, profit, top products, and regional trends.
Skills you build: Data cleaning, slicing & filtering, dashboard creation, business storytelling.

2. Customer Churn Analysis

Tools: Python (Pandas, Seaborn)

Work with a telecom or SaaS dataset to identify which customers are likely to leave and why.

Skills you build: Exploratory data analysis, visualization, correlation, and basic machine learning.


3. E-commerce Product Insights using SQL

Tools: SQL + Power BI

Analyze product categories, top-selling items, and revenue trends from a sample e-commerce dataset.

Skills you build: Joins, GROUP BY, aggregation, data modeling, and visual storytelling.


4. HR Analytics Dashboard

Tools: Excel / Power BI

Dive into employee data to find patterns in attrition, hiring trends, average salaries by department, etc.

Skills you build: Data summarization, calculated fields, visual formatting, DAX basics.


5. Movie Trends Analysis (Netflix or IMDb Dataset)

Tools: Python (Pandas, Matplotlib)

Explore trends across genres, ratings, and release years. Great for people who love entertainment and want to show creativity.

Skills you build: Data wrangling, time-series plots, filtering techniques.


6. Marketing Campaign Analysis

Tools: Excel / Power BI / SQL

Analyze data from a marketing campaign to measure ROI, conversion rates, and customer engagement. Identify which channels or strategies worked best and suggest improvements.

Skills you build: Data blending, KPI calculation, segmentation, and actionable insights.


7. Financial Expense Analysis & Budget Forecasting

Tools: Excel / Power BI / Python

Work on a company’s expense data to analyze spending patterns, categorize expenses, and create a forecasting model to predict future budgets.

Skills you build: Time series analysis, forecasting, budgeting, and financial storytelling.


Pick 2–3 projects. Don’t just show the final visuals — explain your process on LinkedIn or GitHub. That’s what sets you apart.

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Data Analytics Projects for Beginners 👇


Web Scraping
https://github.com/shreyaswankhede/IMDb-Web-Scraping-and-Sentiment-Analysis

Product Price Scraping and Analysis
https://github.com/CodesdaLu/Web-Scrapping

News Scraping
https://github.com/rohit-yadav/scraping-news-articles

Real Time Stock Price Scraping with Python
https://youtu.be/rONhdonaWUo?si=A3oDEVbLIAP78cCz

Zomato Analysis
https://youtu.be/fFi_TBw27is?si=E0iLd3J06YHfQkRk

IPL Analysis
https://github.com/Yashmenaria1/IPL-Data-Exploration

https://www.youtube.com/watch?v=ur-v0dv0Qtw

https://www.youtube.com/watch?v=ur-v0dv0Qtw

Football Data Analysis
https://youtu.be/yat7soj__4w?si=h5CLIvVFzzKm8IEP

Market Basket Analysis
https://youtu.be/Ne8Sbp2hJIk?si=ThEuvdOnRrpcVjOg

Customer Churn Prediction
https://github.com/Pradnya1208/Telecom-Customer-Churn-prediction

Employee’s Performance for HR Analytics
https://www.kaggle.com/code/rajatraj0502/employee-s-performance-for-hr-analytics

Food Price Prediction
https://github.com/VectorInstitute/foodprice-forecasting

𝗦𝗤𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍

SQL is the backbone of data analytics. Whether you’re cleaning data, generating reports, or exploring trends—SQL helps you turn raw information into actionable insights.

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⚡️ Stanford Released a Free Course on Language Modeling from Scratch

The university is currently teaching CS336: Language Modeling from Scratch - and uploading the full course to YouTube for everyone in real time.

Here’s why it’s a big deal:

• Anyone can learn to build their own language models from zero - completely free
• Full course: from architecture and tokenizers to RL training and scaling
• Explained step-by-step, beginner-friendly (even if you’re new to coding)
• Each lecture includes extra reading, assignments, and slides

📚 Course site: https://web.stanford.edu/class/cs336
▶️ YouTube playlist: Watch here

Sber500 is now accepting applications for its 6th batch — an international accelerator for tech startups in AI, DeepTech, FinTech, and beyond.

This fully online, 12-week program is designed for early-stage teams — whether you’ve got an MVP or a product ready to scale. Open to founders worldwide, with a special focus on BRICS countries. The participation is totally free!

🚀 What’s in it for you:
• Mentors from 17+ countries, including experts from Google, Amazon, Oracle
• Access to VCs, corporate partners, and pilot opportunities
• PR visibility in a fast-growing ecosystem
• Strategic entry into the Russian market

The top 25 teams will pitch live at Demo Day in Moscow to investors, corporates, and Sber leadership.

Yes, the application form is detailed — and that’s intentional. The more effort you put in now, the greater your chances of joining. Don’t rush it — this is your gateway to major opportunities.

📅 Deadline extended: June 9
Apply now → https://tinyurl.com/6wunzste

If you’re building something bold and ambitious — this is your moment. Join us!

𝟱 𝗠𝘂𝘀𝘁-𝗙𝗼𝗹𝗹𝗼𝘄 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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🚀 Complete Roadmap to Become a Data Scientist in 5 Months

📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.

🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.

🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.

🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.

🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.

📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.

🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.

💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.

🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.

👨‍💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.

🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.

🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.

🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.

🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!

🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥

🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍

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A-Z of Data Science Part-1