Most Asked SQL Interview Questions at MAANG Companies🔥🔥

Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;

2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.

3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;

4. What is the difference between WHERE & HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;

5. How do you calculate average, sum, minimum & maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;

Hope it helps :)

✅ Data Science Learning Checklist 🧠🔬

📚 Foundations
⦁ What is Data Science & its workflow
⦁ Python/R programming basics
⦁ Statistics & Probability fundamentals
⦁ Data wrangling and cleaning

📊 Data Manipulation & Analysis
⦁ NumPy & Pandas
⦁ Handling missing data & outliers
⦁ Data aggregation & grouping
⦁ Exploratory Data Analysis (EDA)

📈 Data Visualization
⦁ Matplotlib & Seaborn basics
⦁ Interactive viz with Plotly or Tableau
⦁ Dashboard creation
⦁ Storytelling with data

🤖 Machine Learning
⦁ Supervised vs Unsupervised learning
⦁ Regression & classification algorithms
⦁ Model evaluation & validation (cross-validation, metrics)
⦁ Feature engineering & selection

⚙️ Advanced Topics
⦁ Natural Language Processing (NLP) basics
⦁ Time Series analysis
⦁ Deep Learning fundamentals
⦁ Model deployment basics

🛠️ Tools & Platforms
⦁ Jupyter Notebook / Google Colab
⦁ scikit-learn, TensorFlow, PyTorch
⦁ SQL for data querying
⦁ Git & GitHub

📁 Projects to Build
⦁ Customer Segmentation
⦁ Sales Forecasting
⦁ Sentiment Analysis
⦁ Fraud Detection

💡 Practice Platforms:
⦁ Kaggle
⦁ DataCamp
⦁ Datasimplifier

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✅ Data Scientists in Your 20s – Avoid This Trap 🚫🧠

🎯 The Trap? → Passive Learning 
Feels like you’re learning but not truly growing.

🔍 Example:
⦁ Watching endless ML tutorial videos
⦁ Saving notebooks without running or understanding
⦁ Joining courses but not coding models
⦁ Reading research papers without experimenting

End result? 
❌ No models built from scratch 
❌ No real data cleaning done 
❌ No insights or reports delivered

This is passive learning — absorbing without applying. It builds false confidence and slows progress.

🛠️ How to Fix It: 
1️⃣ Learn by doing: Grab real datasets (Kaggle, UCI, public APIs) 
2️⃣ Build projects: Classification, regression, clustering tasks 
3️⃣ Document findings: Share explanations like you’re presenting to stakeholders 
4️⃣ Get feedback: Post code & reports on GitHub, Kaggle, or LinkedIn 
5️⃣ Fail fast: Debug models, tune hyperparameters, iterate frequently

📌 In your 20s, build practical data intuition — not just theory or certificates.

Stop passive watching. 
Start real modeling. 
Start storytelling with data.

That’s how data scientists grow fast in the real world! 🚀

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AI vs ML vs Deep Learning 🤖

You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not.

AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning.

ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed.

Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc.

Think of it this way:
AI = Science
ML = A chapter in the science
Deep Learning = A paragraph in that chapter.

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If I Were to Start My Data Science Career from Scratch, Here's What I Would Do 👇

1️⃣ Master Advanced SQL

Foundations: Learn database structures, tables, and relationships.

Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.

Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.

JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.

Advanced Concepts: CTEs, window functions, and query optimization.

Metric Development: Build and report metrics effectively.


2️⃣ Study Statistics & A/B Testing

Denoscriptive Statistics: Know your mean, median, mode, and standard deviation.

Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.

Probability: Understand basic probability and Bayes' theorem.

Intro to ML: Start with linear regression, decision trees, and K-means clustering.

Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.

A/B Testing: Design experiments—hypothesis formation, sample size calculation, and sample biases.


3️⃣ Learn Python for Data

Data Manipulation: Use pandas for data cleaning and manipulation.

Data Visualization: Explore matplotlib and seaborn for creating visualizations.

Hypothesis Testing: Dive into scipy for statistical testing.

Basic Modeling: Practice building models with scikit-learn.


4️⃣ Develop Product Sense

Product Management Basics: Manage projects and understand the product life cycle.

Data-Driven Strategy: Leverage data to inform decisions and measure success.

Metrics in Business: Define and evaluate metrics that matter to the business.


5️⃣ Hone Soft Skills

Communication: Clearly explain data findings to technical and non-technical audiences.

Collaboration: Work effectively in teams.

Time Management: Prioritize and manage projects efficiently.

Self-Reflection: Regularly assess and improve your skills.


6️⃣ Bonus: Basic Data Engineering

Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.

ETL: Set up extraction jobs, manage dependencies, clean and validate data.

Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.

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The key to starting your data science career:

❌It's not your education
❌It's not your experience

It's how you apply these principles:

1. Learn by working on real datasets
2. Build a portfolio of projects
3. Share your work and insights publicly

No one starts a data scientist, but everyone can become one.

If you're looking for a career in data science, start by:

⟶ Watching tutorials and courses
⟶ Reading expert blogs and papers
⟶ Doing internships or Kaggle competitions
⟶ Building end-to-end projects
⟶ Learning from mentors and peers

You'll be amazed at how quickly you’ll gain confidence and start solving real-world problems.

So, start today and let your data science journey begin!

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✅ Machine Learning A-Z: From Algorithm to Zenith! 🤖🧠

A: Algorithm - A step-by-step procedure used by a machine learning model to learn patterns from data.

B: Bias - A systematic error in a model's predictions, often stemming from flawed assumptions in the training data or the model itself.

C: Classification - A type of supervised learning where the goal is to assign data points to predefined categories.

D: Deep Learning - A subfield of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to analyze data.

E: Ensemble Learning - A technique that combines multiple machine learning models to improve overall predictive performance.

F: Feature Engineering - The process of selecting, transforming, and creating relevant features from raw data to improve model performance.

G: Gradient Descent - An optimization algorithm used to find the minimum of a function (e.g., the error function of a machine learning model) by iteratively adjusting parameters.

H: Hyperparameter Tuning - The process of finding the optimal set of hyperparameters for a machine learning model to maximize its performance.

I: Imputation - The process of filling in missing values in a dataset with estimated values.

J: Jaccard Index - A measure of similarity between two sets, often used in clustering and recommendation systems.

K: K-Fold Cross-Validation - A technique for evaluating model performance by partitioning the data into k subsets and training/testing the model k times, each time using a different subset as the test set.

L: Loss Function - A function that quantifies the error between the predicted and actual values, guiding the model's learning process.

M: Model - A mathematical representation of a real-world process or phenomenon, learned from data.

N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.

O: Overfitting - A phenomenon where a model learns the training data too well, resulting in poor performance on unseen data.

P: Precision - A metric that measures the proportion of correctly predicted positive instances out of all instances predicted as positive.

Q: Q-Learning - A reinforcement learning algorithm used to learn an optimal policy by estimating the expected reward for each action in a given state.

R: Regression - A type of supervised learning where the goal is to predict a continuous numerical value.

S: Supervised Learning - A machine learning approach where an algorithm learns from labeled training data.

T: Training Data - The dataset used to train a machine learning model.

U: Unsupervised Learning - A machine learning approach where an algorithm learns from unlabeled data by identifying patterns and relationships.

V: Validation Set - A subset of the training data used to tune hyperparameters and monitor model performance during training.

W: Weights - Parameters within a machine learning model that are adjusted during training to minimize the loss function.

X: XGBoost (Extreme Gradient Boosting) - A highly optimized and scalable gradient boosting algorithm widely used in machine learning competitions and real-world applications.

Y: Y-Variable - The dependent variable or target variable that a machine learning model is trying to predict.

Z: Zero-Shot Learning - A type of machine learning where a model can recognize or classify objects it has never seen during training.

Tap ❤️ for more!

📊 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!

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✅ Data Science Portfolio Tips 🚀

A Data Science portfolio is your proof of skill — it shows recruiters that you don’t just “know” concepts, but you can apply them to solve real problems. Here’s how to build an impressive one:

🔹 What to Include in Your Portfolio
• 3–5 Real Projects (end-to-end): e.g., data cleaning, EDA, ML modeling, evaluation, and conclusion
• ReadMe Files: Clearly explain each project — objectives, steps, and results
• Visuals: Add graphs, dashboards, or screenshots
• Code + Output: Well-commented Python code + output samples (charts/tables)
• Domain Variety: Include projects from healthcare, finance, e-commerce, etc.

🔹 Where to Host Your Portfolio
• GitHub: Ideal for code, Jupyter Notebooks, version control
→ Use pinned repo section
→ Keep repos clean and organized
→ Add a main README linking to your best work

• Notion: Great as a personal portfolio site
→ Link GitHub repos
→ Write project case studies
→ Embed visualizations or dashboards

• PDF Portfolio: Best when applying for jobs
→ 1–2 page summary of best projects
→ Add clickable links to GitHub/Notion/LinkedIn
→ Use as a “visual resume”

🔹 Tips for Impact
• Use real-world datasets (Kaggle, UCI, etc.)
• Don’t just copy tutorial projects
• Write short blogs explaining your approach
• Show your thought process, not just code

✅ Goal: When a recruiter opens your profile, they should instantly see your value as a practical data scientist.

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🚀 Top 10 Tools Data Scientists Love! 🧠

In the ever-evolving world of data science, staying updated with the right tools is crucial to solving complex problems and deriving meaningful insights.

🔍 Here’s a quick breakdown of the most popular tools:

1. Python 🐍: The go-to language for data science, favored for its versatility and powerful libraries.
2. SQL 🛠️: Essential for querying databases and manipulating data.
3. Jupyter Notebooks 📓: An interactive environment that makes data analysis and visualization a breeze.
4. TensorFlow/PyTorch 🤖: Leading frameworks for deep learning and neural networks.
5. Tableau 📊: A user-friendly tool for creating stunning visualizations and dashboards.
6. Git & GitHub 💻: Version control systems that every data scientist should master.
7. Hadoop & Spark 🔥: Big data frameworks that help process massive datasets efficiently.
8. Scikit-learn 🧬: A powerful library for machine learning in Python.
9. R 📈: A statistical programming language that is still a favorite among many analysts.
10. Docker 🐋: A must-have for containerization and deploying applications.

🐍 Complete Python Syllabus Roadmap (Beginner to Expert) 🚀

🔰 Beginner Level:
1. Intro to Python – Installation, IDEs, first program (print("Hello World"))
2. Variables & Data Types – int, float, string, bool, type casting
3. Operators – Arithmetic, comparison, logical, assignment
4. Control Flow – if-else, nested if, loops (for, while)
5. Functions – def, parameters, return values, lambda functions
6. Data Structures – Lists, Tuples, Sets, Dictionaries
7. Basic Projects – Calculator, number guess game, to-do app

⚙️ Intermediate Level:
1. String Handling – Slicing, formatting, string methods
2. File Handling – Reading/writing .txt, .csv, and JSON files
3. Exception Handling – try-except, finally, custom exceptions
4. Modules & Packages – import, built-in & third-party modules (random, math)
5. OOP in Python – Classes, objects, inheritance, polymorphism
6. Working with Dates & Time – datetime, time module
7. Virtual Environments – venv, pip, requirements.txt

🏆 Expert Level:
1. NumPy & Pandas – Arrays, DataFrames, data manipulation
2. Matplotlib & Seaborn – Data visualization basics
3. Web Scraping – requests, BeautifulSoup, Selenium
4. APIs & JSON – Using REST APIs, parsing data
5. Python for Automation – File automation, emails, web automation
6. Testing – unittest, pytest, writing test cases
7. Python Projects – Blog scraper, weather app, data dashboard

💡 Bonus: Learn Git, Jupyter Notebook, Streamlit, and Flask for real-world projects.

👍 Tap ❤️ for more!

✅ Data Scientist Resume Checklist (2025) 🚀📝

1️⃣ Professional Summary
• 2-3 lines summarizing experience, skills, and career goals.
✔️ Example: "Data Scientist with 5+ years of experience developing and deploying machine learning models to solve complex business problems. Proficient in Python, TensorFlow, and cloud platforms."

2️⃣ Technical Skills
• Programming Languages: Python, R (list proficiency)
• Machine Learning: Regression, Classification, Clustering, Deep Learning, NLP
• Deep Learning Frameworks: TensorFlow, PyTorch, Keras
• Data Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn
• Big Data Technologies: Spark, Hadoop (if applicable)
• Databases: SQL, NoSQL
• Cloud Technologies: AWS, Azure, GCP
• Statistical Analysis: Hypothesis Testing, Time Series Analysis, Experimental Design
• Version Control: Git

3️⃣ Projects Section
• 2-4 data science projects showcasing your skills. Include:
- Project name & brief denoscription
- Problem addressed
- Technologies & algorithms used
- Key results & impact
- Link to GitHub repo/live demo (essential!)
✔️ Quantify your achievements: "Improved model accuracy by 15%..."

4️⃣ Work Experience (if any)
• Company name, role, and duration.
• Responsibilities and accomplishments, quantifying impact.
✔️ Example: "Developed a fraud detection model that reduced fraudulent transactions by 20%."

5️⃣ Education
• Degree, University/Institute, Graduation Year.
✔️ Highlight relevant coursework (statistics, ML, AI).
✔️ List any relevant certifications (e.g., AWS Certified Machine Learning).

6️⃣ Publications/Presentations (Optional)
• If you have any publications or conference presentations, include them.

7️⃣ Soft Skills
• Communication, problem-solving, critical thinking, collaboration, creativity

8️⃣ Clean & Professional Formatting
• Use a readable font and layout.
• Keep it concise (ideally 1-2 pages).
• Save as a PDF.

💡 Customize your resume to each job denoscription. Focus on the skills and experiences that are most relevant to the specific role. Showcase your ability to communicate complex technical concepts to non-technical audiences.

👍 Tap ❤️ if you found this helpful!

✅ Step-by-step guide to create a Data Science Portfolio 🚀

✅ 1️⃣ Choose Your Tools & Skills
Decide what you want to showcase:
• Programming languages: Python, R
• Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch
• Data visualization: Matplotlib, Seaborn, Plotly, Tableau
• Big data tools (optional): Spark, Hadoop

✅ 2️⃣ Plan Your Portfolio Structure
Your portfolio should have:
• Home Page – Brief intro and your data science focus
• About Me – Skills, education, tools, and experience
• Projects – Detailed case studies with code and results
• Blog or Articles (optional) – Explain concepts or your learnings
• Contact – Email, LinkedIn, GitHub links

✅ 3️⃣ Build or Use Platforms to Showcase
Options:
• Create your own website using HTML/CSS/React
• Use GitHub Pages, Kaggle Profile, or Medium for blogs
• Platforms like LinkedIn or personal blogs also work

✅ 4️⃣ Add 4–6 Strong Projects
Include a mix of projects:
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Machine Learning models (regression, classification, clustering)
• Deep Learning projects (optional)
• Data visualization dashboards or reports
• Real-world datasets from Kaggle, UCI, or your own collection

For each project, include:
• Problem statement and goal
• Dataset denoscription
• Tools and techniques used
• Code repository link (GitHub)
• Key findings and visualizations
• Challenges and how you solved them

✅ 5️⃣ Write Clear Documentation
• Explain your thought process step-by-step
• Use Markdown files or Jupyter Notebooks for code explanations
• Add visuals like charts and graphs to support your findings

✅ 6️⃣ Deploy & Share Your Portfolio
• Host your website on GitHub Pages, Netlify, or Vercel
• Share your GitHub repo links
• Publish notebooks on Kaggle or Google Colab

✅ 7️⃣ Keep Improving & Updating
• Add new projects regularly
• Refine old projects based on feedback
• Share insights on social media or blogs

💡 Pro Tips
• Focus on storytelling with data — explain why and how
• Highlight your problem-solving and technical skills
• Show end-to-end project workflow from data to insights
• Include a downloadable resume and your contact info

🎯 Goal: Visitors should quickly see your skills, understand your approach to data problems, and know how to connect with you!

👍 Double Tap ♥️ for more

✅ How to Apply for Data Science Jobs (Step-by-Step Guide) 📊🧠

🔹 1. Build a Solid Portfolio
- 3–5 real-world projects (EDA, ML models, dashboards, NLP, etc.)
- Host code on GitHub & showcase results with Jupyter Notebooks, Streamlit, or Tableau
- Projects ideas: Loan prediction, sentiment analysis, fraud detection, etc.

🔹 2. Create a Targeted Resume
- Highlight skills: Python, SQL, Pandas, Scikit-learn, Tableau, etc.
- Emphasize metrics: “Improved accuracy by 20% using Random Forest”
- Add GitHub, LinkedIn & portfolio links

🔹 3. Build Your LinkedIn Profile
- Title: “Aspiring Data Scientist | Python | Machine Learning”
- Post about your projects, Kaggle solutions, or learning updates
- Connect with recruiters and data professionals

🔹 4. Register on Job Portals
- General: LinkedIn, Naukri, Indeed
- Tech-focused: Hirect, Kaggle Jobs, Analytics Vidhya Jobs
- Internships: Internshala, AICTE, HelloIntern
- Freelance: Upwork, Turing, Freelancer

🔹 5. Apply Smartly
- Target entry-level or internship roles
- Customize every application (don’t mass apply)
- Keep a tracker of where you applied

🔹 6. Prepare for Interviews
- Revise: Python, Stats, Probability, SQL, ML algorithms
- Practice SQL queries, case studies, and ML model explanations
- Use platforms like HackerRank, StrataScratch, InterviewBit

💡 Bonus: Participate in Kaggle competitions & open-source data science projects to gain visibility!

👍 Tap ❤️ if you found this helpful!

✅ AI Career Paths & Skills to Master 🤖🚀💼

🔹 1️⃣ Machine Learning Engineer
🔧 Role: Build & deploy ML models
🧠 Skills: Python, TensorFlow/PyTorch, Data Structures, SQL, Cloud (AWS/GCP)

🔹 2️⃣ Data Scientist
🔧 Role: Analyze data & create predictive models
🧠 Skills: Statistics, Python/R, Pandas, NumPy, Data Viz, ML

🔹 3️⃣ NLP Engineer
🔧 Role: Chatbots, text analysis, speech recognition
🧠 Skills: spaCy, Hugging Face, Transformers, Linguistics basics

🔹 4️⃣ Computer Vision Engineer
🔧 Role: Image/video processing, facial recognition, AR/VR
🧠 Skills: OpenCV, YOLO, CNNs, Deep Learning

🔹 5️⃣ AI Product Manager
🔧 Role: Oversee AI product strategy & development
🧠 Skills: Product Mgmt, Business Strategy, Data Analysis, Basic ML

🔹 6️⃣ Robotics Engineer
🔧 Role: Design & program industrial robots
🧠 Skills: ROS, Embedded Systems, C++, Path Planning

🔹 7️⃣ AI Research Scientist
🔧 Role: Innovate new AI models & algorithms
🧠 Skills: Advanced Math, Deep Learning, RL, Research papers

🔹 8️⃣ MLOps Engineer
🔧 Role: Deploy & manage ML models at scale
🧠 Skills: Docker, Kubernetes, MLflow, CI/CD, Cloud Platforms

💡 Pro Tip: Start with Python & math, then specialize!

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