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When I created this telegram channel for the first time, I just thought to share Free Resources with people like me who can't afford paid courses without thinking much of what am I doing.
Saw lots of ups & downs with telegram. My previous channel @free4unow was blocked. But I never lost the hope and created this backup channel @free4unow_backup
Happy to share that we are a community of 20k+ now.
You can find diverse kind of free courses in this channel starting from web development & ethical hacking to data science & machine learning, just search π for your interested field in the channel.
I won't target 50k+ or 100k+ subscribers in 2024. Rather I will target to share great resources with maximum benefits to help you guys as much as I can π
Happy new year ππ₯³
Share this link with your loved ones: https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Saw lots of ups & downs with telegram. My previous channel @free4unow was blocked. But I never lost the hope and created this backup channel @free4unow_backup
Happy to share that we are a community of 20k+ now.
You can find diverse kind of free courses in this channel starting from web development & ethical hacking to data science & machine learning, just search π for your interested field in the channel.
I won't target 50k+ or 100k+ subscribers in 2024. Rather I will target to share great resources with maximum benefits to help you guys as much as I can π
Happy new year ππ₯³
Share this link with your loved ones: https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Complete roadmap to learn data science in 2024 ππ
1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.
2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).
3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.
4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.
5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.
6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.
7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.
8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.
9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.
10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.
11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.
12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.
Best Resources to learn Data Science
Intro to Data Analytics by Udacity
Machine Learning course by Google
Machine Learning with Python
Data Science Interview Questions
Data Science Project ideas
Data Science: Linear Regression Course by Harvard
Machine Learning Interview Questions
Free Datasets for Projects
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.
2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).
3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.
4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.
5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.
6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.
7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.
8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.
9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.
10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.
11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.
12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.
Best Resources to learn Data Science
Intro to Data Analytics by Udacity
Machine Learning course by Google
Machine Learning with Python
Data Science Interview Questions
Data Science Project ideas
Data Science: Linear Regression Course by Harvard
Machine Learning Interview Questions
Free Datasets for Projects
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Best way to prepare for a SQL interviews ππ
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 π
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://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
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 π
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://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Best way to prepare for Python interviews ππ
1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.
2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.
3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.
4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.
5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.
6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.
7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.
8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.
9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.
10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python Course with FREE Certificate
Python for Data Analysis and Visualization
Python course for beginners by Microsoft
Python course by Google
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
1. Fundamentals: Strengthen your understanding of Python basics, including data types, control structures, functions, and object-oriented programming concepts.
2. Data Structures and Algorithms: Familiarize yourself with common data structures (lists, dictionaries, sets, etc.) and algorithms. Practice solving coding problems on platforms like LeetCode or HackerRank.
3. Problem Solving: Develop problem-solving skills by working on real-world scenarios. Understand how to approach and solve problems efficiently using Python.
4. Libraries and Frameworks: Be well-versed in popular Python libraries and frameworks relevant to the job, such as NumPy, Pandas, Flask, or Django. Demonstrate your ability to apply these tools in practical situations.
5. Web Development (if applicable): If the position involves web development, understand web frameworks like Flask or Django. Be ready to discuss your experience in building web applications using Python.
6. Database Knowledge: Have a solid understanding of working with databases in Python. Know how to interact with databases using SQLAlchemy or Django ORM.
7. Testing and Debugging: Showcase your proficiency in writing unit tests and debugging code. Understand testing frameworks like pytest and debugging tools available in Python.
8. Version Control: Familiarize yourself with version control systems, particularly Git, and demonstrate your ability to collaborate on projects using Git.
9. Projects: Showcase relevant projects in your portfolio. Discuss the challenges you faced, solutions you implemented, and the impact of your work.
10. Soft Skills: Highlight your communication and collaboration skills. Be ready to explain your thought process and decision-making during technical discussions.
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python Course with FREE Certificate
Python for Data Analysis and Visualization
Python course for beginners by Microsoft
Python course by Google
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Best ways to prepare for your next web development interview ππ
π 1. Review your technical skills: Make sure you know the programming languages and tools that are relevant to the job you're applying for. Brush up on HTML, CSS, JavaScript, and any other programming languages you plan to use.
π 2. Familiarize yourself with the company: Do research on the company's products, services, and work culture. Visit their website and social media pages to learn about their projects and clients.
π 3. Prepare for common interview questions: Be ready to answer questions about your experience, problem-solving skills, and technical knowledge. You can also expect to talk about how you work in teams and how you handle difficult situations.
π 4. Build a portfolio: Create a portfolio website or collect examples of your previous work that demonstrate your skills and expertise. This can showcase your past experience and demonstrate that you are serious about your work.
π 5. Practice coding exercises: Many web development interviews include coding exercises to test your skills. Practice different coding challenges and exercises to become more comfortable with them.
π 6. Ask questions: Prepare a few thoughtful questions to ask the interviewer about the company, their projects and culture. This shows that you are genuinely interested in the company and the job.
π 7. Be confident and stay positive: Show enthusiasm and confidence during the interview. Even if you don't know the answer to a particular question, stay positive and try to work through the problem with the interviewer.
Best Resource to learn Web Development ππ
Freecodecamp Course with Certificate
Web Development Free Bootcamp
Javascript Free course with Certificate
Projects in HTML, CSS & Javascript
GitHub Repositories for Web Developer
Python Flask For Beginners
PHP Tutorial for Beginners
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
π 1. Review your technical skills: Make sure you know the programming languages and tools that are relevant to the job you're applying for. Brush up on HTML, CSS, JavaScript, and any other programming languages you plan to use.
π 2. Familiarize yourself with the company: Do research on the company's products, services, and work culture. Visit their website and social media pages to learn about their projects and clients.
π 3. Prepare for common interview questions: Be ready to answer questions about your experience, problem-solving skills, and technical knowledge. You can also expect to talk about how you work in teams and how you handle difficult situations.
π 4. Build a portfolio: Create a portfolio website or collect examples of your previous work that demonstrate your skills and expertise. This can showcase your past experience and demonstrate that you are serious about your work.
π 5. Practice coding exercises: Many web development interviews include coding exercises to test your skills. Practice different coding challenges and exercises to become more comfortable with them.
π 6. Ask questions: Prepare a few thoughtful questions to ask the interviewer about the company, their projects and culture. This shows that you are genuinely interested in the company and the job.
π 7. Be confident and stay positive: Show enthusiasm and confidence during the interview. Even if you don't know the answer to a particular question, stay positive and try to work through the problem with the interviewer.
Best Resource to learn Web Development ππ
Freecodecamp Course with Certificate
Web Development Free Bootcamp
Javascript Free course with Certificate
Projects in HTML, CSS & Javascript
GitHub Repositories for Web Developer
Python Flask For Beginners
PHP Tutorial for Beginners
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Complete Roadmap to learn Generative AI in 2 months ππ
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ππ
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNINGππ
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ππ
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNINGππ
Top 7 FREE Courses By Udacity ππ
Introduction to Python Programming
Intro to Java: Functional Programming
SQL for Data Analysis
Intro to Data Analysis
Developing Android Apps with Kotlin
Intro to JavaScript
Intro to Machine Learning
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Introduction to Python Programming
Intro to Java: Functional Programming
SQL for Data Analysis
Intro to Data Analysis
Developing Android Apps with Kotlin
Intro to JavaScript
Intro to Machine Learning
Please give us credits while sharing: -> https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Very few people in the channel may remember my old Instagram page where I used to post Free Resources and Giveaways. But it was hacked by someone 2 years ago and I don't know how to restore that.
The attached screenshot is from two years ago, and it appeared in my Google Photos feed today π₯Ή
So today I thought to recreate insta page again to host free giveaways and useful resources.
Need your support and love again β€οΈπ
https://www.instagram.com/educated_guy?igsh=NDd6and4MmNkbmZu
Will come up with Free Giveaways soon
ENJOY LEARNING ππ
The attached screenshot is from two years ago, and it appeared in my Google Photos feed today π₯Ή
So today I thought to recreate insta page again to host free giveaways and useful resources.
Need your support and love again β€οΈπ
https://www.instagram.com/educated_guy?igsh=NDd6and4MmNkbmZu
Will come up with Free Giveaways soon
ENJOY LEARNING ππ
Best Resource to Learn Artificial Intelligence (AI) For Free
ππ
https://imp.i115008.net/qn27PL
https://i.am.ai/roadmap
https://bit.ly/3h97QpE
https://www.tg-me.com/datasciencefun/1375
http://microsoft.github.io/AI-For-Beginners
https://ai.google/education/
Share with credits: https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
ππ
https://imp.i115008.net/qn27PL
https://i.am.ai/roadmap
https://bit.ly/3h97QpE
https://www.tg-me.com/datasciencefun/1375
http://microsoft.github.io/AI-For-Beginners
https://ai.google/education/
Share with credits: https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
This message is for all those people looking for new opportunities or learning new skills thinking if they'll earn more, sustain in this life or not.
AI will take the job.
Will there be new opportunities in 2024.
How many days will it take to learn this skill.
Why I am still not successful.
I am sharing some bit of experience with you all based on whatever I observed in this world.
Don't think too much. Everything takes some time.
Rather just focus on your goal and do something which keep you closer to that. Stay consistent & work on something that your future self will be proud of.
There will be some days when you'll find yourself doing nothing. But just ignore it and learn from the failures without thinking anything negative.
In case I can be of any help to you, feel free to reach out to me either through Instagram or Telegram.
Never stop learning β€οΈ
Learning can be anything - new skill or habit. So just enjoy the process even if it takes time.
ENJOY LEARNING ππ
AI will take the job.
Will there be new opportunities in 2024.
How many days will it take to learn this skill.
Why I am still not successful.
I am sharing some bit of experience with you all based on whatever I observed in this world.
Don't think too much. Everything takes some time.
Rather just focus on your goal and do something which keep you closer to that. Stay consistent & work on something that your future self will be proud of.
There will be some days when you'll find yourself doing nothing. But just ignore it and learn from the failures without thinking anything negative.
In case I can be of any help to you, feel free to reach out to me either through Instagram or Telegram.
Never stop learning β€οΈ
Learning can be anything - new skill or habit. So just enjoy the process even if it takes time.
ENJOY LEARNING ππ
Complete Roadmap to learn SQL in 2024 ππ
1. Understand the Basics:
- Learn about databases, tables, and relationships.
- Understand the basic structure of SQL queries (SELECT, FROM, WHERE, etc.).
- Practice writing simple queries to retrieve data from a single table.
2. Learn Data Manipulation:
- Study how to insert, update, and delete data in SQL tables.
- Practice writing queries to modify data in tables.
3. Master Querying Data:
- Learn advanced SQL querying techniques such as JOINs, subqueries, and aggregations.
- Practice writing complex queries that involve multiple tables and conditions.
4. Dive into Data Analysis:
- Learn how to use SQL for data analysis tasks like filtering, grouping, and sorting data.
- Practice writing queries to analyze and summarize data.
5. Understand Data Modeling:
- Study database design principles and normalization.
- Learn how to create and manage database schemas.
6. Explore Advanced Topics:
- Learn about advanced SQL concepts like stored procedures, functions, and triggers.
- Study performance optimization techniques for SQL queries.
7. Practice, Practice, Practice:
- Work on real-world projects or use online platforms to practice SQL queries.
- Challenge yourself with different types of SQL problems to improve your skills.
8. Stay Updated:
- Keep up with the latest trends and updates in the SQL world.
- Follow SQL blogs, forums, and online communities to stay connected with other SQL learners and professionals.
By following this roadmap and dedicating time to practice and learn, you can gradually build your SQL skills and become proficient in querying and manipulating data using SQL.
Top 5 RESOURCES TO LEARN & PRACTICE SQL
Mode
Stratascratch
Udacity
SQL Notes
Udemy
Share with credits: https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
1. Understand the Basics:
- Learn about databases, tables, and relationships.
- Understand the basic structure of SQL queries (SELECT, FROM, WHERE, etc.).
- Practice writing simple queries to retrieve data from a single table.
2. Learn Data Manipulation:
- Study how to insert, update, and delete data in SQL tables.
- Practice writing queries to modify data in tables.
3. Master Querying Data:
- Learn advanced SQL querying techniques such as JOINs, subqueries, and aggregations.
- Practice writing complex queries that involve multiple tables and conditions.
4. Dive into Data Analysis:
- Learn how to use SQL for data analysis tasks like filtering, grouping, and sorting data.
- Practice writing queries to analyze and summarize data.
5. Understand Data Modeling:
- Study database design principles and normalization.
- Learn how to create and manage database schemas.
6. Explore Advanced Topics:
- Learn about advanced SQL concepts like stored procedures, functions, and triggers.
- Study performance optimization techniques for SQL queries.
7. Practice, Practice, Practice:
- Work on real-world projects or use online platforms to practice SQL queries.
- Challenge yourself with different types of SQL problems to improve your skills.
8. Stay Updated:
- Keep up with the latest trends and updates in the SQL world.
- Follow SQL blogs, forums, and online communities to stay connected with other SQL learners and professionals.
By following this roadmap and dedicating time to practice and learn, you can gradually build your SQL skills and become proficient in querying and manipulating data using SQL.
Top 5 RESOURCES TO LEARN & PRACTICE SQL
Mode
Stratascratch
Udacity
SQL Notes
Udemy
Share with credits: https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
Essential Python Libraries for Data Analytics ππ
Python Free Resources: https://www.tg-me.com/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://www.tg-me.com/sqlspecialist
Hope it helps :)
Python Free Resources: https://www.tg-me.com/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://www.tg-me.com/sqlspecialist
Hope it helps :)
5 Free Web Development Courses by Udacity ππ
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Object-Oriented JavaScript
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Intro to HTML and CSS
Intro to Backend
Networking for Web Developers
Intro to JavaScript
Object-Oriented JavaScript
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Essential programming language for Android app development ππ
1. Java: Java has been the traditional and most widely used programming language for Android app development. It is the official language for Android development and provides a robust set of tools and libraries for building Android apps.
2. Kotlin: Kotlin is a modern, concise, and expressive programming language that has gained popularity among Android developers. It is fully interoperable with Java and offers many features that make Android app development more efficient and less error-prone.
3. C++: While not as commonly used as Java or Kotlin, C++ can be used for developing performance-critical parts of an Android app, such as game engines or graphics-intensive applications.
4. Python: Although not typically used for building full-fledged Android apps, Python can be used for scripting, automation, and data processing tasks in Android development.
5. JavaScript: JavaScript can be used in combination with frameworks like React Native or NativeScript to build cross-platform mobile apps that run on both Android and iOS devices.
Overall, Java and Kotlin are the most essential programming languages for Android app development, with Kotlin gaining popularity as a more modern and efficient alternative to Java.
Free Resources to learn App Development ππ
Developing Android Apps with Kotlin
Udemy
Android Basics in Kotlin
Advanced Android with Kotlin
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1. Java: Java has been the traditional and most widely used programming language for Android app development. It is the official language for Android development and provides a robust set of tools and libraries for building Android apps.
2. Kotlin: Kotlin is a modern, concise, and expressive programming language that has gained popularity among Android developers. It is fully interoperable with Java and offers many features that make Android app development more efficient and less error-prone.
3. C++: While not as commonly used as Java or Kotlin, C++ can be used for developing performance-critical parts of an Android app, such as game engines or graphics-intensive applications.
4. Python: Although not typically used for building full-fledged Android apps, Python can be used for scripting, automation, and data processing tasks in Android development.
5. JavaScript: JavaScript can be used in combination with frameworks like React Native or NativeScript to build cross-platform mobile apps that run on both Android and iOS devices.
Overall, Java and Kotlin are the most essential programming languages for Android app development, with Kotlin gaining popularity as a more modern and efficient alternative to Java.
Free Resources to learn App Development ππ
Developing Android Apps with Kotlin
Udemy
Android Basics in Kotlin
Advanced Android with Kotlin
Join @free4unow_backup for more free resources.
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Essential Programming Languages to Learn Data Science ππ
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts ππ
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
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1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts ππ
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
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Learning graphic design in 2024 can be an exciting and rewarding journey. Here are some steps you can follow to learn graphic designing:
1. Understand the Basics: Start by understanding the basic principles of graphic design, such as typography, color theory, layout, and composition. You can find online resources, books, and courses that cover these fundamentals.
2. Learn Design Software: Familiarize yourself with popular graphic design software such as Adobe Photoshop, Illustrator, and InDesign. These tools are widely used in the industry and mastering them will be beneficial for your career.
3. Take Online Courses: Enroll in online courses or tutorials that focus on graphic design. Websites like Coursera, Udemy, Skillshare, and Lynda offer a wide range of courses on graphic design for beginners to advanced learners.
4. Practice Regularly: Practice is essential for improving your graphic design skills. Create your own projects, experiment with different styles and techniques, and seek feedback from peers or professionals to enhance your work.
5. Build a Portfolio: As you gain experience and create designs, start building a portfolio to showcase your best work. A strong portfolio is crucial when applying for graphic design jobs or freelance opportunities.
6. Stay Updated: Graphic design trends and tools are constantly evolving. Stay updated with the latest industry trends, attend workshops, webinars, and conferences to expand your knowledge and skills.
7. Join Design Communities: Join online design communities, forums, and social media groups to connect with other designers, share your work, and learn from others in the field.
8. Seek Feedback: Don't be afraid to seek feedback on your designs. Constructive criticism can help you improve and grow as a designer.
Free Resources to learn Graphic Designing ππ
Product Design Course by Udacity
Video Editing Free Courses by Udemy
Free Graphic Design Courses and Tutorials
Learn Video Editing & Photoshop
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1. Understand the Basics: Start by understanding the basic principles of graphic design, such as typography, color theory, layout, and composition. You can find online resources, books, and courses that cover these fundamentals.
2. Learn Design Software: Familiarize yourself with popular graphic design software such as Adobe Photoshop, Illustrator, and InDesign. These tools are widely used in the industry and mastering them will be beneficial for your career.
3. Take Online Courses: Enroll in online courses or tutorials that focus on graphic design. Websites like Coursera, Udemy, Skillshare, and Lynda offer a wide range of courses on graphic design for beginners to advanced learners.
4. Practice Regularly: Practice is essential for improving your graphic design skills. Create your own projects, experiment with different styles and techniques, and seek feedback from peers or professionals to enhance your work.
5. Build a Portfolio: As you gain experience and create designs, start building a portfolio to showcase your best work. A strong portfolio is crucial when applying for graphic design jobs or freelance opportunities.
6. Stay Updated: Graphic design trends and tools are constantly evolving. Stay updated with the latest industry trends, attend workshops, webinars, and conferences to expand your knowledge and skills.
7. Join Design Communities: Join online design communities, forums, and social media groups to connect with other designers, share your work, and learn from others in the field.
8. Seek Feedback: Don't be afraid to seek feedback on your designs. Constructive criticism can help you improve and grow as a designer.
Free Resources to learn Graphic Designing ππ
Product Design Course by Udacity
Video Editing Free Courses by Udemy
Free Graphic Design Courses and Tutorials
Learn Video Editing & Photoshop
Join @free4unow_backup for more free resources.
ENJOY LEARNINGππ
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Complete Roadmap to learn DSA in 30 days
Day 1-5: Introduction to Data Structures and Algorithms
- Understand the importance of DSA in programming
- Learn about different types of data structures (arrays, linked lists, stacks, queues, trees, graphs)
- Study basic algorithms like searching and sorting
Day 6-10: Arrays and Strings
- Dive deeper into arrays and strings
- Learn about common operations and algorithms on arrays and strings
- Practice solving problems related to arrays and strings
Day 11-15: Linked Lists
- Study linked lists and their variations (singly linked list, doubly linked list, circular linked list)
- Implement basic operations on linked lists
- Solve problems involving linked lists
Day 16-20: Stacks and Queues
- Learn about stacks and queues and their applications
- Implement stack and queue data structures
- Solve problems using stacks and queues
Day 21-25: Trees and Graphs
- Study binary trees, binary search trees, AVL trees, heaps, and graphs
- Understand traversal algorithms (inorder, preorder, postorder) for trees
- Implement basic graph algorithms (DFS, BFS)
- Solve problems related to trees and graphs
Day 26-30: Advanced Topics
- Study advanced data structures like hash tables, tries, segment trees
- Learn about dynamic programming, backtracking, and divide and conquer algorithms
- Practice solving complex problems that require a combination of data structures and algorithms
Throughout the 30 days, make sure to practice regularly by solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Additionally, review your concepts regularly and seek out resources like online tutorials, textbooks, and study groups to deepen your understanding of DSA.
5β£ Free DSA resources to crack coding interview
π GeekforGeeks
π Leetcode
π Hackerrank
π DSA Resources
π FreeCodeCamp
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Day 1-5: Introduction to Data Structures and Algorithms
- Understand the importance of DSA in programming
- Learn about different types of data structures (arrays, linked lists, stacks, queues, trees, graphs)
- Study basic algorithms like searching and sorting
Day 6-10: Arrays and Strings
- Dive deeper into arrays and strings
- Learn about common operations and algorithms on arrays and strings
- Practice solving problems related to arrays and strings
Day 11-15: Linked Lists
- Study linked lists and their variations (singly linked list, doubly linked list, circular linked list)
- Implement basic operations on linked lists
- Solve problems involving linked lists
Day 16-20: Stacks and Queues
- Learn about stacks and queues and their applications
- Implement stack and queue data structures
- Solve problems using stacks and queues
Day 21-25: Trees and Graphs
- Study binary trees, binary search trees, AVL trees, heaps, and graphs
- Understand traversal algorithms (inorder, preorder, postorder) for trees
- Implement basic graph algorithms (DFS, BFS)
- Solve problems related to trees and graphs
Day 26-30: Advanced Topics
- Study advanced data structures like hash tables, tries, segment trees
- Learn about dynamic programming, backtracking, and divide and conquer algorithms
- Practice solving complex problems that require a combination of data structures and algorithms
Throughout the 30 days, make sure to practice regularly by solving coding problems on platforms like LeetCode, HackerRank, or Codeforces. Additionally, review your concepts regularly and seek out resources like online tutorials, textbooks, and study groups to deepen your understanding of DSA.
5β£ Free DSA resources to crack coding interview
π GeekforGeeks
π Leetcode
π Hackerrank
π DSA Resources
π FreeCodeCamp
Join for more free resources: https://www.tg-me.com/free4unow_backup
ENJOY LEARNING ππ
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