Data Analytics & AI | SQL Interviews | Power BI Resources
25.2K subscribers
305 photos
2 videos
151 files
318 links
๐Ÿ”“Explore the fascinating world of Data Analytics & Artificial Intelligence

๐Ÿ’ป Best AI tools, free resources, and expert advice to land your dream tech job.

Admin: @coderfun
Download Telegram
๐—–๐—ถ๐˜€๐—ฐ๐—ผ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿ˜

Upgrade Your Tech Skills in 2025โ€”For FREE!

๐Ÿ”น Introduction to Cybersecurity
๐Ÿ”น Networking Essentials
๐Ÿ”น Introduction to Modern AI
๐Ÿ”น Discovering Entrepreneurship
๐Ÿ”น Python for Beginners

๐‹๐ข๐ง๐ค ๐Ÿ‘‡:-

https://pdlink.in/4chn8Us

Enroll For FREE & Get Certified ๐ŸŽ“
๐Ÿ‘4
๐—›๐—ผ๐˜„ ๐˜๐—ผ ๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ ๐—™๐—ถ๐—ป๐—ฎ๐—ป๐—ฐ๐—ถ๐—ฎ๐—น ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜

Want to break into Financial Data Analytics but donโ€™t know where to start?

Hereโ€™s your ultimate step-by-step roadmap to landing a job in this high-demand field.

๐‹๐ข๐ง๐ค๐Ÿ‘‡:-

https://pdlink.in/42aGUwb

๐ŸŽฏ ๐Ÿš€ Ready to Start?
โค5
๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜ 

Learn AI for FREE with these incredible courses by Google!

Whether youโ€™re a beginner or looking to sharpen your skills, these resources will help you stay ahead in the tech game.

๐‹๐ข๐ง๐ค ๐Ÿ‘‡:- 

https://pdlink.in/3FYbfGR

Enroll For FREE & Get Certified๐ŸŽ“
๐Ÿ‘1
Essential Python Libraries for Data Analytics ๐Ÿ˜„๐Ÿ‘‡

Python Free Resources: https://t.iss.one/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://t.iss.one/sqlspecialist

Hope it helps :)
๐Ÿ‘2
๐Ÿฐ ๐—™๐—ฅ๐—˜๐—˜ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜ 

These free, Microsoft-backed courses are a game-changer!

With these resources, youโ€™ll gain the skills and confidence needed to shine in the data analytics worldโ€”all without spending a penny.

๐‹๐ข๐ง๐ค ๐Ÿ‘‡:- 

https://pdlink.in/4jpmI0I

Enroll For FREE & Get Certified๐ŸŽ“
โค1๐Ÿ‘1
Ai revolution and learning path ๐Ÿ“š

The current AI revolution is exhilarating ๐Ÿš€, pushing the boundaries of what's possible across different sectors. Yet, it's essential to anchor oneself in the foundational elements that enable these advancements:

- Neural Networks: Grasp the basics and variations, understanding how they process information and learning about key types like CNNs and RNNs ๐Ÿง .

- Loss Functions and Optimization: Familiarize yourself with how loss functions measure model performance and the role of optimization techniques like gradient descent in improving accuracy ๐Ÿ”.

- Activation Functions: Learn about the significance of activation functions such as ReLU and Sigmoid in capturing non-linear patterns ๐Ÿ”‘.

- Training and Evaluation: Master the nuanced art of model training, from preventing overfitting with regularization to fine-tuning hyperparameters for optimal performance ๐ŸŽฏ.

- Data Handling: Recognize the importance of data preprocessing and augmentation in enhancing model robustness. ๐Ÿ’พ

- Stay Updated: Keep an eye on emerging trends, like transformers and GANs, and understand the ethical considerations in AI application. ๐ŸŒ

Immersing yourself in these core areas not only prepares you for the ongoing AI wave but sets a solid foundation for navigating future advancements. Balancing a strong grasp of fundamental concepts with an awareness of new technologies is key to thriving in the AI domain.
๐Ÿ‘1