The most popular programming languages:
1. Python
2. TypeScript
3. JavaScript
4. C#
5. HTML
6. Rust
7. C++
8. C
9. Go
10. Lua
11. Kotlin
12. Java
13. Swift
14. Jupyter Notebook
15. Shell
16. CSS
17. GDScript
18. Solidity
19. Vue
20. PHP
21. Dart
22. Ruby
23. Objective-C
24. PowerShell
25. Scala
According to the Latest GitHub Repositories
1. Python
2. TypeScript
3. JavaScript
4. C#
5. HTML
6. Rust
7. C++
8. C
9. Go
10. Lua
11. Kotlin
12. Java
13. Swift
14. Jupyter Notebook
15. Shell
16. CSS
17. GDScript
18. Solidity
19. Vue
20. PHP
21. Dart
22. Ruby
23. Objective-C
24. PowerShell
25. Scala
According to the Latest GitHub Repositories
π₯ The Information provides insights into the Stargate deal involving OpenAI:
- Concerns about Microsoft: Sam Altman was worried that Microsoft wasn't providing enough computing capacity for OpenAI to compete effectively, especially as Elon Musk launched a data center in just 3.5 months compared to Microsoft's 18 months.
- Data and Computing Expansion: Altman has been focused on enhancing OpenAI's access to data and computing power as essential for developing AGI and advancing scientific research.
- Stargate's Purpose: The Stargate initiative aims to provide OpenAI with a significant amount of affordable computing power exclusively for its use.
- Revenue Goals: OpenAI seeks to increase its revenue from $4 billion in 2024 to $12 billion in 2025, with a long-term goal of reaching $100 billion by 2029. Stargate members are considering the option to offer computing power to other companies if OpenAI's growth falters.
- Collaboration with Oracle: After Musk opted to build his own data center, Altman partnered with Oracle and Crusoe to construct a new data center in Texas for OpenAI, significantly increasing their computing resources.
- Future Expansion: Oracle has leased a 1.2 GW campus in Abilene, which will expand to 2 GW by mid-2026, with a total investment of $100 billion planned for the project.
- Oracle's Position: Oracle's CEO has close ties to both Trump and Musk, providing an incentive to support OpenAI, with Oracle's shares rising 16% following the announcement.
- Strategic Moves: Altman has maneuvered politically to secure support for OpenAI amid increasing influence from Musk, culminating in a White House announcement of the Stargate project shortly after Trump took office.
- Concerns about Microsoft: Sam Altman was worried that Microsoft wasn't providing enough computing capacity for OpenAI to compete effectively, especially as Elon Musk launched a data center in just 3.5 months compared to Microsoft's 18 months.
- Data and Computing Expansion: Altman has been focused on enhancing OpenAI's access to data and computing power as essential for developing AGI and advancing scientific research.
- Stargate's Purpose: The Stargate initiative aims to provide OpenAI with a significant amount of affordable computing power exclusively for its use.
- Revenue Goals: OpenAI seeks to increase its revenue from $4 billion in 2024 to $12 billion in 2025, with a long-term goal of reaching $100 billion by 2029. Stargate members are considering the option to offer computing power to other companies if OpenAI's growth falters.
- Collaboration with Oracle: After Musk opted to build his own data center, Altman partnered with Oracle and Crusoe to construct a new data center in Texas for OpenAI, significantly increasing their computing resources.
- Future Expansion: Oracle has leased a 1.2 GW campus in Abilene, which will expand to 2 GW by mid-2026, with a total investment of $100 billion planned for the project.
- Oracle's Position: Oracle's CEO has close ties to both Trump and Musk, providing an incentive to support OpenAI, with Oracle's shares rising 16% following the announcement.
- Strategic Moves: Altman has maneuvered politically to secure support for OpenAI amid increasing influence from Musk, culminating in a White House announcement of the Stargate project shortly after Trump took office.
π 9 must-have Python developer tools.
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
1. PyCharm IDE
2. Jupyter notebook
3. Keras
4. Pip Package
5. Python Anywhere
6. Scikit-Learn
7. Sphinx
8. Selenium
9. Sublime Text
Power BI vs Tableau: Which BI Tool is Right for You?
Ease of Use β Power BI is more beginner-friendly, especially for those familiar with Excel, while Tableau requires a steeper learning curve but offers advanced customization.
Pricing β Power BI is more affordable, making it ideal for small businesses, whereas Tableau is costlier but provides robust enterprise-level features.
Data Connectivity β Both tools support various data sources, but Tableau offers broader and deeper connectivity options for large-scale data analytics.
Visualization & Customization β Tableau excels in advanced visualizations and design flexibility, whereas Power BI provides a simpler drag-and-drop interface with strong AI-driven insights.
Performance & Speed β Tableau handles large datasets more efficiently, whereas Power BI can slow down with very complex models unless optimized.
Integration β Power BI integrates seamlessly with Microsoft products (Excel, Azure, Power Apps), while Tableau connects well with multiple platforms, including cloud services.
Community & Support β Tableau has a strong user community for data visualization, while Power BI benefits from Microsoftβs extensive support and frequent updates.
Ease of Use β Power BI is more beginner-friendly, especially for those familiar with Excel, while Tableau requires a steeper learning curve but offers advanced customization.
Pricing β Power BI is more affordable, making it ideal for small businesses, whereas Tableau is costlier but provides robust enterprise-level features.
Data Connectivity β Both tools support various data sources, but Tableau offers broader and deeper connectivity options for large-scale data analytics.
Visualization & Customization β Tableau excels in advanced visualizations and design flexibility, whereas Power BI provides a simpler drag-and-drop interface with strong AI-driven insights.
Performance & Speed β Tableau handles large datasets more efficiently, whereas Power BI can slow down with very complex models unless optimized.
Integration β Power BI integrates seamlessly with Microsoft products (Excel, Azure, Power Apps), while Tableau connects well with multiple platforms, including cloud services.
Community & Support β Tableau has a strong user community for data visualization, while Power BI benefits from Microsoftβs extensive support and frequent updates.
How to start your career in data analysis for freshers ππ
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
1. Learn the Basics: Begin with understanding the fundamental concepts of statistics, mathematics, and programming languages like Python or R.
Free Resources: https://t.iss.one/pythonanalyst/103
2. Acquire Technical Skills: Develop proficiency in data analysis tools such as Excel, SQL, and data visualization tools like Tableau or Power BI.
Free Data Analysis Books: https://t.iss.one/learndataanalysis
3. Gain Knowledge in Statistics: A solid foundation in statistical concepts is crucial for data analysis. Learn about probability, hypothesis testing, and regression analysis.
Free course by Khan Academy will help you to enhance these skills.
4. Programming Proficiency: Enhance your programming skills, especially in languages commonly used in data analysis like Python or R. Familiarity with libraries such as Pandas and NumPy in Python is beneficial. Kaggle has amazing content to learn these skills.
5. Data Cleaning and Preprocessing: Understand the importance of cleaning and preprocessing data. Learn techniques to handle missing values, outliers, and transform data for analysis.
6. Database Knowledge: Acquire knowledge about databases and SQL for efficient data retrieval and manipulation.
SQL for data analytics: https://t.iss.one/sqlanalyst
7. Data Visualization: Master the art of presenting insights through visualizations. Learn tools like Matplotlib, Seaborn, or ggplot2 for creating meaningful charts and graphs. If you are from non-technical background, learn Tableau or Power BI.
FREE Resources to learn data visualization: https://t.iss.one/PowerBI_analyst
8. Machine Learning Basics: Familiarize yourself with basic machine learning concepts. This knowledge can be beneficial for advanced analytics tasks.
ML Basics: https://t.iss.one/datasciencefun/1476
9. Build a Portfolio: Work on projects that showcase your skills. This could be personal projects, contributions to open-source projects, or challenges from platforms like Kaggle.
Data Analytics Portfolio Projects: https://t.iss.one/DataPortfolio
10. Networking and Continuous Learning: Engage with the data science community, attend meetups, webinars, and conferences. Build your strong Linkedin profile and enhance your network.
11. Apply for Internships or Entry-Level Positions: Gain practical experience by applying for internships or entry-level positions in data analysis. Real-world projects contribute significantly to your learning.
Data Analyst Jobs & Internship opportunities: https://t.iss.one/jobs_SQL
12. Effective Communication: Develop strong communication skills. Being able to convey your findings and insights in a clear and understandable manner is crucial.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
π1
Practical Python Dat... by Ashwin Pajankar.pdf
4.8 MB
Practical Python Data Visualization
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ΠΠ²ΡΠΎΡ: Ashwin Pajankar
https_coderbooks_ruIntroduction_to_Data_Science_Data_Analysis_and.pdf
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ΠΠ²ΡΠΎΡ: Rafael A. Irizarry
ΠΠ²ΡΠΎΡ: Rafael A. Irizarry
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2.5 MB
Introduction to Algorithms &
Data Structures 1
β Free Courses with Certificate:
https://t.iss.one/free4unow_backup
All the best ππ
Data Structures 1
β Free Courses with Certificate:
https://t.iss.one/free4unow_backup
All the best ππ
π3