Best way to prepare for a SQL interviews ๐๐
1. Review Basic Concepts: Ensure you understand fundamental SQL concepts like SELECT statements, JOINs, GROUP BY, and WHERE clauses.
2. Practice SQL Queries: Work on writing and executing SQL queries. Practice retrieving, updating, and deleting data.
3. Understand Database Design: Learn about normalization, indexes, and relationships to comprehend how databases are structured.
4. Know Your Database: If possible, find out which database system the company uses (e.g., MySQL, PostgreSQL, SQL Server) and familiarize yourself with its specific syntax.
5. Data Types and Constraints: Understand various data types and constraints such as PRIMARY KEY, FOREIGN KEY, and UNIQUE constraints.
6. Stored Procedures and Functions: Learn about stored procedures and functions, as interviewers may inquire about these.
7. Data Manipulation Language (DML): Be familiar with INSERT, UPDATE, and DELETE statements.
8. Data Definition Language (DDL): Understand statements like CREATE, ALTER, and DROP for database and table management.
9. Normalization and Optimization: Brush up on database normalization and optimization techniques to demonstrate your understanding of efficient database design.
10. Troubleshooting Skills: Be prepared to troubleshoot queries, identify errors, and optimize poorly performing queries.
11. Scenario-Based Questions: Practice answering scenario-based questions. Understand how to approach problems and design solutions.
12. Latest Trends: Stay updated on the latest trends in database technologies and SQL best practices.
13. Review Resume Projects: If you have projects involving SQL on your resume, be ready to discuss them in detail.
14. Mock Interviews: Conduct mock interviews with a friend or use online platforms to simulate real interview scenarios.
15. Ask Questions: Prepare questions to ask the interviewer about the company's use of databases and SQL.
Best Resources to learn SQL ๐
SQL Topics for Data Analysts
SQL Udacity Course
Download SQL Cheatsheet
SQL Interview Questions
Learn & Practice SQL
Also try to apply what you learn through hands-on projects or challenges.
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
1. Review Basic Concepts: Ensure you understand fundamental SQL concepts like SELECT statements, JOINs, GROUP BY, and WHERE clauses.
2. Practice SQL Queries: Work on writing and executing SQL queries. Practice retrieving, updating, and deleting data.
3. Understand Database Design: Learn about normalization, indexes, and relationships to comprehend how databases are structured.
4. Know Your Database: If possible, find out which database system the company uses (e.g., MySQL, PostgreSQL, SQL Server) and familiarize yourself with its specific syntax.
5. Data Types and Constraints: Understand various data types and constraints such as PRIMARY KEY, FOREIGN KEY, and UNIQUE constraints.
6. Stored Procedures and Functions: Learn about stored procedures and functions, as interviewers may inquire about these.
7. Data Manipulation Language (DML): Be familiar with INSERT, UPDATE, and DELETE statements.
8. Data Definition Language (DDL): Understand statements like CREATE, ALTER, and DROP for database and table management.
9. Normalization and Optimization: Brush up on database normalization and optimization techniques to demonstrate your understanding of efficient database design.
10. Troubleshooting Skills: Be prepared to troubleshoot queries, identify errors, and optimize poorly performing queries.
11. Scenario-Based Questions: Practice answering scenario-based questions. Understand how to approach problems and design solutions.
12. Latest Trends: Stay updated on the latest trends in database technologies and SQL best practices.
13. Review Resume Projects: If you have projects involving SQL on your resume, be ready to discuss them in detail.
14. Mock Interviews: Conduct mock interviews with a friend or use online platforms to simulate real interview scenarios.
15. Ask Questions: Prepare questions to ask the interviewer about the company's use of databases and SQL.
Best Resources to learn SQL ๐
SQL Topics for Data Analysts
SQL Udacity Course
Download SQL Cheatsheet
SQL Interview Questions
Learn & Practice SQL
Also try to apply what you learn through hands-on projects or challenges.
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
โค1
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ถ๐ฐ๐ธ๐๐๐ฎ๐ฟ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ฎ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ (๐ก๐ผ ๐๐
๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!)๐
Ready to Upgrade Your Skills for a Data-Driven Career in 2025?๐
Whether youโre a student, a fresher, or someone switching to tech, these free beginner-friendly courses will help you get started in data analysis, machine learning, Python, and more๐จโ๐ป๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mwOACf
Best For: Beginners ready to dive into real machine learningโ ๏ธ
Ready to Upgrade Your Skills for a Data-Driven Career in 2025?๐
Whether youโre a student, a fresher, or someone switching to tech, these free beginner-friendly courses will help you get started in data analysis, machine learning, Python, and more๐จโ๐ป๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4mwOACf
Best For: Beginners ready to dive into real machine learningโ ๏ธ
โค1
5 beginner-to-intermediate projects you can build if you're learning Programming & AI
1. AI-Powered Chatbot (Using Python)
Build a simple chatbot that can understand and respond to user inputs. You can use rule-based logic at first, and then explore NLP with libraries like NLTK or spaCy.
Skills: Python, NLP, Regex, Basic ML
Ideas to include:
- Greeting and small talk
- FAQ-based responses
- Sentiment-based replies
You can also integrate it with Telegram or Discord bot
2. Movie Recommendation System
Create a recommendation system based on movie genre, user preferences, or ratings using collaborative filtering or content-based filtering.
Skills: Python, Pandas, Scikit-learn
Ideas to include:
- Use TMDB or MovieLens datasets
- Add filtering by genre
- Include cosine similarity logic
3. AI-Powered Resume Parser
Upload a PDF or DOCX resume and let your app extract name, skills, experience, education, and output it in a structured format.
Skills: Python, NLP, Regex, Flask
Ideas to include:
- File upload option
- Named Entity Recognition (NER) with spaCy
- Save extracted info into a CSV/Database
4. To-Do App with Smart Suggestions
A regular to-do list but with an AI assistant that suggests tasks based on previous entries (e.g., you often add "buy milk" on Mondays? It suggests it.)
Skills: JavaScript/React + AI API (like OpenAI or custom model)
Ideas to include:
- CRUD functionality
- Natural Language date/time parsing
- AI suggestion module
5. Fake News Detector
Given a news headline or article, predict if itโs fake or real. A great application of classification problems.
Skills: Python, NLP, ML (Logistic Regression or TF-IDF + Naive Bayes)
Ideas to include:
- Use datasets from Kaggle
- Preprocess with stopwords, lemmatization
- Display prediction result with probability
React with โค๏ธ if you want me to share source code or free resources to build these projects
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
Software Developer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
ENJOY LEARNING ๐๐
1. AI-Powered Chatbot (Using Python)
Build a simple chatbot that can understand and respond to user inputs. You can use rule-based logic at first, and then explore NLP with libraries like NLTK or spaCy.
Skills: Python, NLP, Regex, Basic ML
Ideas to include:
- Greeting and small talk
- FAQ-based responses
- Sentiment-based replies
You can also integrate it with Telegram or Discord bot
2. Movie Recommendation System
Create a recommendation system based on movie genre, user preferences, or ratings using collaborative filtering or content-based filtering.
Skills: Python, Pandas, Scikit-learn
Ideas to include:
- Use TMDB or MovieLens datasets
- Add filtering by genre
- Include cosine similarity logic
3. AI-Powered Resume Parser
Upload a PDF or DOCX resume and let your app extract name, skills, experience, education, and output it in a structured format.
Skills: Python, NLP, Regex, Flask
Ideas to include:
- File upload option
- Named Entity Recognition (NER) with spaCy
- Save extracted info into a CSV/Database
4. To-Do App with Smart Suggestions
A regular to-do list but with an AI assistant that suggests tasks based on previous entries (e.g., you often add "buy milk" on Mondays? It suggests it.)
Skills: JavaScript/React + AI API (like OpenAI or custom model)
Ideas to include:
- CRUD functionality
- Natural Language date/time parsing
- AI suggestion module
5. Fake News Detector
Given a news headline or article, predict if itโs fake or real. A great application of classification problems.
Skills: Python, NLP, ML (Logistic Regression or TF-IDF + Naive Bayes)
Ideas to include:
- Use datasets from Kaggle
- Preprocess with stopwords, lemmatization
- Display prediction result with probability
React with โค๏ธ if you want me to share source code or free resources to build these projects
Coding Projects: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
Software Developer Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
ENJOY LEARNING ๐๐
โค4๐1
๐ฏ ๐ข๐ฝ๐ฒ๐ป-๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
If youโve ever thought, โCan I actually build something useful with AI?โ โ the answer is yes, and you donโt need to be a genius to start.โจ๏ธ๐
These 3 open-source projects on GitHub are proof of what you can build with just basic coding knowledge and a passion for learning.๐งโ๐ป๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45jKiXe
Build your own AI agent that remembers conversations and gets smarter over time.โ ๏ธ
If youโve ever thought, โCan I actually build something useful with AI?โ โ the answer is yes, and you donโt need to be a genius to start.โจ๏ธ๐
These 3 open-source projects on GitHub are proof of what you can build with just basic coding knowledge and a passion for learning.๐งโ๐ป๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45jKiXe
Build your own AI agent that remembers conversations and gets smarter over time.โ ๏ธ
โค2
Forwarded from Python Projects & Resources
๐ ๐
๐ซ๐๐ ๐๐จ๐ฎ๐๐ฎ๐๐ ๐๐๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐ญ๐จ ๐๐ฎ๐ข๐ฅ๐ ๐๐ ๐๐ฎ๐ญ๐จ๐ฆ๐๐ญ๐ข๐จ๐ง๐ฌ & ๐๐ ๐๐ง๐ญ๐ฌ ๐๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐๐จ๐๐ข๐ง๐ ๐
Want to Create AI Automations & Agents Without Writing a Single Line of Code?๐งโ๐ป
These 5 free YouTube tutorials will take you from complete beginner to automation expert in record time.๐งโ๐โจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lhYwhn
Just pure, actionable automation skills โ for free.โ ๏ธ
Want to Create AI Automations & Agents Without Writing a Single Line of Code?๐งโ๐ป
These 5 free YouTube tutorials will take you from complete beginner to automation expert in record time.๐งโ๐โจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lhYwhn
Just pure, actionable automation skills โ for free.โ ๏ธ
โค1
If you want to get a job as a machine learning engineer, donโt start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
โค1
๐ฆ๐๐ฒ๐ฝ ๐๐ป๐๐ผ ๐ฎ ๐๐๐ ๐๐ป๐ฎ๐น๐๐๐โ๐ ๐ฆ๐ต๐ผ๐ฒ๐: ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฆ๐ถ๐บ๐๐น๐ฎ๐๐ถ๐ผ๐ป + ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ฒ๐
๐ผ Ever Wondered How Data Shapes Real Business Decisions at a Top Consulting Firm?๐งโ๐ปโจ๏ธ
Now you can experience it firsthand with this interactive simulation from BCG (Boston Consulting Group)๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45HWKRP
This is a powerful resume booster and a unique way to prove your analytical skillsโ ๏ธ
๐ผ Ever Wondered How Data Shapes Real Business Decisions at a Top Consulting Firm?๐งโ๐ปโจ๏ธ
Now you can experience it firsthand with this interactive simulation from BCG (Boston Consulting Group)๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/45HWKRP
This is a powerful resume booster and a unique way to prove your analytical skillsโ ๏ธ
โค2
Forwarded from Python Projects & Resources
๐๐ญ๐๐ซ๐ญ ๐๐จ๐ฎ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐๐จ๐ฎ๐ซ๐ง๐๐ฒ โ ๐๐๐% ๐
๐ซ๐๐ & ๐๐๐ ๐ข๐ง๐ง๐๐ซ-๐
๐ซ๐ข๐๐ง๐๐ฅ๐ฒ๐
Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
โค2