Python for Data Analysts
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Most Asked SQL Interview Questions at MAANG Companies๐Ÿ”ฅ๐Ÿ”ฅ

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 Resources๐Ÿ‘‡
https://t.me/mysqldata

Like this post if you need more ๐Ÿ‘โค๏ธ

Hope it helps :)
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๐Ÿš€ 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
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Data Structures and
Algorithms in Python


๐Ÿ“š book
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Pandas.pdf
21.3 MB
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๐Ÿ”Ÿ 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 ๐Ÿ‘๐Ÿ‘
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Data Analytics Projects Listโœจ! ๐Ÿ’ผ๐Ÿ“Š

Beginner-Level Projects ๐Ÿ
(Focus: Excel, SQL, data cleaning)

1๏ธโƒฃ Sales performance dashboard in Excel
2๏ธโƒฃ Customer feedback summary using text data
3๏ธโƒฃ Clean and analyze a CSV file with missing data
4๏ธโƒฃ Product inventory analysis with pivot tables
5๏ธโƒฃ Use SQL to query and visualize a retail dataset
6๏ธโƒฃ Create a revenue tracker by month and category
7๏ธโƒฃ Analyze demographic data from a survey
8๏ธโƒฃ Market share analysis across product lines
9๏ธโƒฃ Simple cohort analysis using Excel
๐Ÿ”Ÿ User signup trends using SQL GROUP BY and DATE

Intermediate-Level Projects ๐Ÿš€
(Focus: Python, data visualization, EDA)

1๏ธโƒฃ Churn analysis from telco dataset using Python
2๏ธโƒฃ Power BI sales dashboard with filters & slicers
3๏ธโƒฃ E-commerce data segmentation with clustering
4๏ธโƒฃ Forecast site traffic using moving averages
5๏ธโƒฃ Analyze Netflix/Bollywood IMDB datasets
6๏ธโƒฃ A/B test results evaluation for marketing campaign
7๏ธโƒฃ Customer lifetime value prediction
8๏ธโƒฃ Explore correlations in vaccination or health datasets
9๏ธโƒฃ Predict loan approval using logistic regression
๐Ÿ”Ÿ Create a Tableau dashboard highlighting HR insights

Advanced-Level Projects ๐Ÿ”ฅ
(Focus: Machine learning, big data, real-world scenarios)

1๏ธโƒฃ Fraud detection using anomaly detection on banking data
2๏ธโƒฃ Real-time dashboard using streaming data (Power BI + API)
3๏ธโƒฃ Predictive model for sales forecasting with ML
4๏ธโƒฃ NLP sentiment analysis of product reviews or tweets
5๏ธโƒฃ Recommender system for e-commerce products
6๏ธโƒฃ Build ETL pipeline (Python + SQL + cloud storage)
7๏ธโƒฃ Analyze and visualize stock market trends
8๏ธโƒฃ Big data analysis using Spark on a large dataset
9๏ธโƒฃ Create a data compliance audit dashboard
๐Ÿ”Ÿ Geospatial heatmap of business locations vs revenue

๐Ÿ“‚ Pro Tip: Host these on GitHub, add visuals, and explain your processโ€”great for impressing recruiters! ๐Ÿ™Œ

๐Ÿ’ฌ React โ™ฅ๏ธ for more
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Python Pandas ๐Ÿผ
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๐Ÿš€ 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
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๐Ÿ”ฅ Guys, Another Big Announcement!

Iโ€™m launching a Python Interview Series ๐Ÿ๐Ÿ’ผ 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.

Hereโ€™s whatโ€™s coming your way ๐Ÿ‘‡

๐Ÿ”น Week 1: Python Fundamentals (Beginner Level)
โ€ข Data types, variables & operators
โ€ข If-else, loops & functions
โ€ข Input/output & basic problem-solving
๐Ÿ’ก *Practice:* Reverse string, Prime check, Factorial, Palindrome

๐Ÿ”น Week 2: Data Structures in Python
โ€ข Lists, Tuples, Sets, Dictionaries
โ€ข Comprehensions (list, dict, set)
โ€ข Sorting, searching, and nested structures
๐Ÿ’ก *Practice:* Frequency count, remove duplicates, find max/min

๐Ÿ”น Week 3: Functions, Modules & File Handling
โ€ข *args, *kwargs, lambda, map/filter/reduce
โ€ข File read/write, CSV handling
โ€ข Modules & imports
๐Ÿ’ก *Practice:* Create custom functions, read data files, handle errors

๐Ÿ”น Week 4: Object-Oriented Programming (OOP)
โ€ข Classes, objects, inheritance, polymorphism
โ€ข Encapsulation & abstraction
โ€ข Magic methods (__init__, __str__)
๐Ÿ’ก *Practice:* Build a simple class like BankAccount or StudentSystem

๐Ÿ”น Week 5: Exception Handling & Logging
โ€ข try-except-else-finally
โ€ข Custom exceptions
โ€ข Logging errors & debugging best practices
๐Ÿ’ก *Practice:* File operations with proper error handling

๐Ÿ”น Week 6: Advanced Python Concepts
โ€ข Decorators, generators, iterators
โ€ข Closures & context managers
โ€ข Shallow vs deep copy
๐Ÿ’ก *Practice:* Create your own decorator, generator examples

๐Ÿ”น Week 7: Pandas & NumPy for Data Analysis
โ€ข DataFrame basics, filtering & grouping
โ€ข Handling missing data
โ€ข NumPy arrays, slicing, and aggregation
๐Ÿ’ก *Practice:* Analyze small CSV datasets

๐Ÿ”น Week 8: Python for Analytics & Visualization
โ€ข Matplotlib, Seaborn basics
โ€ข Data summarization & correlation
โ€ข Building simple dashboards
๐Ÿ’ก *Practice:* Visualize sales or user data

๐Ÿ”น Week 9: Real Interview Questions (Intermediateโ€“Advanced)
โ€ข 50+ Python interview questions with answers
โ€ข Common logical & coding tasks
โ€ข Real company-style questions (Infosys, TCS, Deloitte, etc.)
๐Ÿ’ก *Practice:* Solve daily problem sets

๐Ÿ”น Week 10: Final Interview Prep (Mock & Revision)
โ€ข End-to-end mock interviews
โ€ข Python project discussion tips
โ€ข Resume & GitHub portfolio guidance

๐Ÿ“Œ Each week includes:
โœ… Key Concepts & Examples
โœ… Coding Snippets & Practice Tasks
โœ… Real Interview Q&A
โœ… Mini Quiz & Discussion

๐Ÿ‘ React โค๏ธ if youโ€™re ready to master Python interviews!

๐Ÿ‘‡ You can access it from here: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/2099
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