DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI)

👉 Power BI:

Q1: Explain step-by-step how you will create a sales dashboard from scratch.

Q2: Explain how you can optimize a slow Power BI report.

Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.

👉SQL:

Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.

Q2 – Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)

Q2: Find the nth highest salary from the Employee table.

Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.

Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.

Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)

👉Behavioral:

Q1: Why do you want to become a data analyst and why did you apply to this company?

Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?

I have curated best top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

Top 6 Data Concepts

Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it!

Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI?

On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential.

On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world:
- Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare”
- Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics
- AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of trannoscriptional control at the DNA level
- Anderson Rocha (University of Campinas, Brazil) will give a presentation ennoscriptd “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”.

And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI.

The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced.

Ride the wave with AI into the future!

Tune in to the AI Journey webcast on November 19-21.

Data Science Techniques

Data Analytics Interview Questions

1. What is the difference between SQL and MySQL?

SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle or IBM DB2, that is used to manage SQL databases.


2. What is a Cross-Join?

Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables. If a WHERE clause is used in cross join then the query will work like an INNER JOIN.


3. What is a Stored Procedure?

A stored procedure is a subroutine available to applications that access a relational database management system (RDBMS). Such procedures are stored in the database data dictionary. The sole disadvantage of stored procedure is that it can be executed nowhere except in the database and occupies more memory in the database server.


4. What is Pattern Matching in SQL?

SQL pattern matching provides for pattern search in data if you have no clue as to what that word should be. This kind of SQL query uses wildcards to match a string pattern, rather than writing the exact word. The LIKE operator is used in conjunction with SQL Wildcards to fetch the required information.

✅ Useful Resources to Learn Machine Learning in 2025 🤖📘

1. YouTube Channels
⦁ StatQuest – Simple, visual ML explanations
⦁ Krish Naik – ML projects and interviews
⦁ Simplilearn – Concepts + hands-on demos
⦁ freeCodeCamp – Full ML crash courses

2. Free Courses
⦁ Andrew Ng’s ML – Coursera (audit for free)
⦁ Google’s ML Crash Course – Interactive + videos
⦁ Kaggle Learn – Short, hands-on ML tutorials
⦁ Fast.ai – Practical deep learning for coders

3. Practice Platforms
⦁ Kaggle – Real datasets, notebooks, and competitions
⦁ Google Colab – Run Python ML code in browser
⦁ DrivenData – ML competitions with impact

4. Projects to Try
⦁ House price predictor
⦁ Stock trend classifier
⦁ Sentiment analysis on tweets
⦁ MNIST handwritten digit recognition
⦁ Recommendation system

5. Key Libraries
⦁ scikit-learn – Core ML algorithms
⦁ pandas – Data manipulation
⦁ matplotlib/seaborn – Visualization
⦁ TensorFlow / PyTorch – Deep learning
⦁ XGBoost – Advanced boosting models

6. Must-Know Concepts
⦁ Supervised vs Unsupervised learning
⦁ Overfitting & underfitting
⦁ Model evaluation: Accuracy, F1, ROC
⦁ Cross-validation
⦁ Feature engineering

7. Books
⦁ “Hands-On ML with Scikit-Learn & TensorFlow” – Aurélien Géron
⦁ “Python ML” – Sebastian Raschka

💡 Build a portfolio. Learn by doing. Share projects on GitHub.

💬 Tap ❤️ for more!

🔅 Most important SQL commands

🔍 Support Vector Machines (SVM) in Machine Learning!

🚀 Support Vector Machines are a powerful class of supervised learning algorithms that can be used for both classification and regression tasks. They have gained immense popularity due to their ability to handle complex datasets and deliver accurate predictions. Let's explore some key aspects that make SVMs stand out:

1️⃣ Robustness: SVMs are highly effective in handling high-dimensional data, making them suitable for various real-world applications such as text categorization and bioinformatics. Their robustness enables them to handle noise and outliers effectively.

2️⃣ Margin Maximization: One of the core principles behind SVM is maximizing the margin between different classes. By finding an optimal hyperplane that separates data points with the maximum margin, SVMs aim to achieve better generalization on unseen data.

3️⃣ Kernel Trick: The kernel trick is a game-changer when it comes to SVMs. It allows us to transform non-linearly separable data into higher-dimensional feature spaces where they become linearly separable. This technique opens up possibilities for solving complex problems that were previously considered challenging.

4️⃣ Regularization: SVMs employ regularization techniques like L1 or L2 regularization, which help prevent overfitting by penalizing large coefficients. This ensures better generalization performance on unseen data.

5️⃣ Versatility: SVMs offer various formulations such as C-SVM (soft-margin), ν-SVM (nu-Support Vector Machine), and ε-SVM (epsilon-Support Vector Machine). These formulations provide flexibility in handling different types of datasets and trade-offs between model complexity and error tolerance.

6️⃣ Interpretability: Unlike some black-box models, SVMs provide interpretability. The support vectors, which are the data points closest to the decision boundary, play a crucial role in defining the model. This interpretability helps in understanding the underlying patterns and decision-making process.

As machine learning continues to revolutionize industries, Support Vector Machines remain a valuable tool in our arsenal. Their ability to handle complex datasets, maximize margins, and transform non-linear data make them an essential technique for tackling challenging problems.

The Difference Between Model Accuracy and Business Accuracy

A model can be 95% accurate…
yet deliver 0% business value.

Why❔
Because data science metrics ≠ business metrics.

📌 Examples:
- A fraud model catches tiny fraud but misses large ones
- A churn model predicts already obvious churners
- A recommendation model boosts clicks but reduces revenue

Always align ML metrics with business KPIs.
Otherwise, your “great model” is just a great illusion.