Most Asked SQL Interview Questions at MAANG Companies๐ฅ๐ฅ
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;
Here you can find essential SQL Interview Resources๐
https://t.me/mysqldata
Like this post if you need more ๐โค๏ธ
Hope it helps :)
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;
Here you can find essential SQL Interview Resources๐
https://t.me/mysqldata
Like this post if you need more ๐โค๏ธ
Hope it helps :)
โค15๐2
๐ Essential Python/ Pandas snippets to explore data:
1. .head() - Review top rows
2. .tail() - Review bottom rows
3. .info() - Summary of DataFrame
4. .shape - Shape of DataFrame
5. .describe() - Descriptive stats
6. .isnull().sum() - Check missing values
7. .dtypes - Data types of columns
8. .unique() - Unique values in a column
9. .nunique() - Count unique values
10. .value_counts() - Value counts in a column
11. .corr() - Correlation matrix
1. .head() - Review top rows
2. .tail() - Review bottom rows
3. .info() - Summary of DataFrame
4. .shape - Shape of DataFrame
5. .describe() - Descriptive stats
6. .isnull().sum() - Check missing values
7. .dtypes - Data types of columns
8. .unique() - Unique values in a column
9. .nunique() - Count unique values
10. .value_counts() - Value counts in a column
11. .corr() - Correlation matrix
โค14๐1
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๐ Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
https://t.me/sqlspecialist/379
ENJOY LEARNING ๐๐
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
https://t.me/sqlspecialist/379
ENJOY LEARNING ๐๐
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โ | AI Assistants
โ | Multi-Agent Systems
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Join now โคต๏ธ
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Data Analytics Projects Listโจ! ๐ผ๐
Beginner-Level Projects ๐
(Focus: Excel, SQL, data cleaning)
1๏ธโฃ Sales performance dashboard in Excel
2๏ธโฃ Customer feedback summary using text data
3๏ธโฃ Clean and analyze a CSV file with missing data
4๏ธโฃ Product inventory analysis with pivot tables
5๏ธโฃ Use SQL to query and visualize a retail dataset
6๏ธโฃ Create a revenue tracker by month and category
7๏ธโฃ Analyze demographic data from a survey
8๏ธโฃ Market share analysis across product lines
9๏ธโฃ Simple cohort analysis using Excel
๐ User signup trends using SQL GROUP BY and DATE
Intermediate-Level Projects ๐
(Focus: Python, data visualization, EDA)
1๏ธโฃ Churn analysis from telco dataset using Python
2๏ธโฃ Power BI sales dashboard with filters & slicers
3๏ธโฃ E-commerce data segmentation with clustering
4๏ธโฃ Forecast site traffic using moving averages
5๏ธโฃ Analyze Netflix/Bollywood IMDB datasets
6๏ธโฃ A/B test results evaluation for marketing campaign
7๏ธโฃ Customer lifetime value prediction
8๏ธโฃ Explore correlations in vaccination or health datasets
9๏ธโฃ Predict loan approval using logistic regression
๐ Create a Tableau dashboard highlighting HR insights
Advanced-Level Projects ๐ฅ
(Focus: Machine learning, big data, real-world scenarios)
1๏ธโฃ Fraud detection using anomaly detection on banking data
2๏ธโฃ Real-time dashboard using streaming data (Power BI + API)
3๏ธโฃ Predictive model for sales forecasting with ML
4๏ธโฃ NLP sentiment analysis of product reviews or tweets
5๏ธโฃ Recommender system for e-commerce products
6๏ธโฃ Build ETL pipeline (Python + SQL + cloud storage)
7๏ธโฃ Analyze and visualize stock market trends
8๏ธโฃ Big data analysis using Spark on a large dataset
9๏ธโฃ Create a data compliance audit dashboard
๐ Geospatial heatmap of business locations vs revenue
๐ Pro Tip: Host these on GitHub, add visuals, and explain your processโgreat for impressing recruiters! ๐
๐ฌ React โฅ๏ธ for more
Beginner-Level Projects ๐
(Focus: Excel, SQL, data cleaning)
1๏ธโฃ Sales performance dashboard in Excel
2๏ธโฃ Customer feedback summary using text data
3๏ธโฃ Clean and analyze a CSV file with missing data
4๏ธโฃ Product inventory analysis with pivot tables
5๏ธโฃ Use SQL to query and visualize a retail dataset
6๏ธโฃ Create a revenue tracker by month and category
7๏ธโฃ Analyze demographic data from a survey
8๏ธโฃ Market share analysis across product lines
9๏ธโฃ Simple cohort analysis using Excel
๐ User signup trends using SQL GROUP BY and DATE
Intermediate-Level Projects ๐
(Focus: Python, data visualization, EDA)
1๏ธโฃ Churn analysis from telco dataset using Python
2๏ธโฃ Power BI sales dashboard with filters & slicers
3๏ธโฃ E-commerce data segmentation with clustering
4๏ธโฃ Forecast site traffic using moving averages
5๏ธโฃ Analyze Netflix/Bollywood IMDB datasets
6๏ธโฃ A/B test results evaluation for marketing campaign
7๏ธโฃ Customer lifetime value prediction
8๏ธโฃ Explore correlations in vaccination or health datasets
9๏ธโฃ Predict loan approval using logistic regression
๐ Create a Tableau dashboard highlighting HR insights
Advanced-Level Projects ๐ฅ
(Focus: Machine learning, big data, real-world scenarios)
1๏ธโฃ Fraud detection using anomaly detection on banking data
2๏ธโฃ Real-time dashboard using streaming data (Power BI + API)
3๏ธโฃ Predictive model for sales forecasting with ML
4๏ธโฃ NLP sentiment analysis of product reviews or tweets
5๏ธโฃ Recommender system for e-commerce products
6๏ธโฃ Build ETL pipeline (Python + SQL + cloud storage)
7๏ธโฃ Analyze and visualize stock market trends
8๏ธโฃ Big data analysis using Spark on a large dataset
9๏ธโฃ Create a data compliance audit dashboard
๐ Geospatial heatmap of business locations vs revenue
๐ Pro Tip: Host these on GitHub, add visuals, and explain your processโgreat for impressing recruiters! ๐
๐ฌ React โฅ๏ธ for more
โค16๐5๐ฅฐ1
๐ Essential Python/ Pandas snippets to explore data:
1. .head() - Review top rows
2. .tail() - Review bottom rows
3. .info() - Summary of DataFrame
4. .shape - Shape of DataFrame
5. .describe() - Descriptive stats
6. .isnull().sum() - Check missing values
7. .dtypes - Data types of columns
8. .unique() - Unique values in a column
9. .nunique() - Count unique values
10. .value_counts() - Value counts in a column
11. .corr() - Correlation matrix
1. .head() - Review top rows
2. .tail() - Review bottom rows
3. .info() - Summary of DataFrame
4. .shape - Shape of DataFrame
5. .describe() - Descriptive stats
6. .isnull().sum() - Check missing values
7. .dtypes - Data types of columns
8. .unique() - Unique values in a column
9. .nunique() - Count unique values
10. .value_counts() - Value counts in a column
11. .corr() - Correlation matrix
โค7๐6
๐ฅ Guys, Another Big Announcement!
Iโm launching a Python Interview Series ๐๐ผ โ your complete guide to cracking Python interviews from beginner to advanced level!
This will be a week-by-week series designed to make you interview-ready โ covering core concepts, coding questions, and real interview scenarios asked by top companies.
Hereโs whatโs coming your way ๐
๐น Week 1: Python Fundamentals (Beginner Level)
โข Data types, variables & operators
โข If-else, loops & functions
โข Input/output & basic problem-solving
๐ก *Practice:* Reverse string, Prime check, Factorial, Palindrome
๐น Week 2: Data Structures in Python
โข Lists, Tuples, Sets, Dictionaries
โข Comprehensions (list, dict, set)
โข Sorting, searching, and nested structures
๐ก *Practice:* Frequency count, remove duplicates, find max/min
๐น Week 3: Functions, Modules & File Handling
โข
โข File read/write, CSV handling
โข Modules & imports
๐ก *Practice:* Create custom functions, read data files, handle errors
๐น Week 4: Object-Oriented Programming (OOP)
โข Classes, objects, inheritance, polymorphism
โข Encapsulation & abstraction
โข Magic methods (
๐ก *Practice:* Build a simple class like BankAccount or StudentSystem
๐น Week 5: Exception Handling & Logging
โข
โข Custom exceptions
โข Logging errors & debugging best practices
๐ก *Practice:* File operations with proper error handling
๐น Week 6: Advanced Python Concepts
โข Decorators, generators, iterators
โข Closures & context managers
โข Shallow vs deep copy
๐ก *Practice:* Create your own decorator, generator examples
๐น Week 7: Pandas & NumPy for Data Analysis
โข DataFrame basics, filtering & grouping
โข Handling missing data
โข NumPy arrays, slicing, and aggregation
๐ก *Practice:* Analyze small CSV datasets
๐น Week 8: Python for Analytics & Visualization
โข Matplotlib, Seaborn basics
โข Data summarization & correlation
โข Building simple dashboards
๐ก *Practice:* Visualize sales or user data
๐น Week 9: Real Interview Questions (IntermediateโAdvanced)
โข 50+ Python interview questions with answers
โข Common logical & coding tasks
โข Real company-style questions (Infosys, TCS, Deloitte, etc.)
๐ก *Practice:* Solve daily problem sets
๐น Week 10: Final Interview Prep (Mock & Revision)
โข End-to-end mock interviews
โข Python project discussion tips
โข Resume & GitHub portfolio guidance
๐ Each week includes:
โ Key Concepts & Examples
โ Coding Snippets & Practice Tasks
โ Real Interview Q&A
โ Mini Quiz & Discussion
๐ React โค๏ธ if youโre ready to master Python interviews!
๐ You can access it from here: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/2099
Iโm launching a Python Interview Series ๐๐ผ โ your complete guide to cracking Python interviews from beginner to advanced level!
This will be a week-by-week series designed to make you interview-ready โ covering core concepts, coding questions, and real interview scenarios asked by top companies.
Hereโs whatโs coming your way ๐
๐น Week 1: Python Fundamentals (Beginner Level)
โข Data types, variables & operators
โข If-else, loops & functions
โข Input/output & basic problem-solving
๐ก *Practice:* Reverse string, Prime check, Factorial, Palindrome
๐น Week 2: Data Structures in Python
โข Lists, Tuples, Sets, Dictionaries
โข Comprehensions (list, dict, set)
โข Sorting, searching, and nested structures
๐ก *Practice:* Frequency count, remove duplicates, find max/min
๐น Week 3: Functions, Modules & File Handling
โข
*args
, *kwargs
, lambda
, map/filter/reduce
โข File read/write, CSV handling
โข Modules & imports
๐ก *Practice:* Create custom functions, read data files, handle errors
๐น Week 4: Object-Oriented Programming (OOP)
โข Classes, objects, inheritance, polymorphism
โข Encapsulation & abstraction
โข Magic methods (
__init__
, __str__
)๐ก *Practice:* Build a simple class like BankAccount or StudentSystem
๐น Week 5: Exception Handling & Logging
โข
try-except-else-finally
โข Custom exceptions
โข Logging errors & debugging best practices
๐ก *Practice:* File operations with proper error handling
๐น Week 6: Advanced Python Concepts
โข Decorators, generators, iterators
โข Closures & context managers
โข Shallow vs deep copy
๐ก *Practice:* Create your own decorator, generator examples
๐น Week 7: Pandas & NumPy for Data Analysis
โข DataFrame basics, filtering & grouping
โข Handling missing data
โข NumPy arrays, slicing, and aggregation
๐ก *Practice:* Analyze small CSV datasets
๐น Week 8: Python for Analytics & Visualization
โข Matplotlib, Seaborn basics
โข Data summarization & correlation
โข Building simple dashboards
๐ก *Practice:* Visualize sales or user data
๐น Week 9: Real Interview Questions (IntermediateโAdvanced)
โข 50+ Python interview questions with answers
โข Common logical & coding tasks
โข Real company-style questions (Infosys, TCS, Deloitte, etc.)
๐ก *Practice:* Solve daily problem sets
๐น Week 10: Final Interview Prep (Mock & Revision)
โข End-to-end mock interviews
โข Python project discussion tips
โข Resume & GitHub portfolio guidance
๐ Each week includes:
โ Key Concepts & Examples
โ Coding Snippets & Practice Tasks
โ Real Interview Q&A
โ Mini Quiz & Discussion
๐ React โค๏ธ if youโre ready to master Python interviews!
๐ You can access it from here: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/2099
โค12