🔰 How to become a data scientist in 2025?
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
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🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
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🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
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🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
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🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
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🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
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🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
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🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data
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Don't forget to check these 10 SQL projects with corresponding datasets that you could use to practice your SQL skills:
1. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
2. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
3. Social Media Analytics:
(https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels)
4. Financial Data Analysis:
(https://www.kaggle.com/datasets/nitindatta/finance-data)
5. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
6. Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data)
7. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
8. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
9. Supply Chain Management:
(https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis)
10. Inventory Management:
(https://www.kaggle.com/datasets?search=inventory+management)
Share this channel with your friends 🤝🤩
Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
ENJOY LEARNING 👍👍
1. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
2. HR Analytics:
(https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset)
3. Social Media Analytics:
(https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels)
4. Financial Data Analysis:
(https://www.kaggle.com/datasets/nitindatta/finance-data)
5. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
6. Customer Relationship Management:
(https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data)
7. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
8. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
9. Supply Chain Management:
(https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis)
10. Inventory Management:
(https://www.kaggle.com/datasets?search=inventory+management)
Share this channel with your friends 🤝🤩
Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
ENJOY LEARNING 👍👍
❤4
Website Development Roadmap – 2025
🔹 Stage 1: HTML – Learn the basics of web page structure.
🔹 Stage 2: CSS – Style and enhance web pages (Flexbox, Grid, Animations).
🔹 Stage 3: JavaScript (ES6+) – Add interactivity and dynamic features.
🔹 Stage 4: Git & GitHub – Manage code versions and collaborate.
🔹 Stage 5: Responsive Design – Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS).
🔹 Stage 6: UI/UX Basics – Understand user experience and design principles.
🔹 Stage 7: JavaScript Frameworks – Learn React.js, Vue.js, or Angular for interactive UIs.
🔹 Stage 8: Backend Development – Use Node.js, PHP, Python, or Ruby to
build server-side logic.
🔹 Stage 9: Databases – Work with MySQL, PostgreSQL, or MongoDB for data storage.
🔹 Stage 10: RESTful APIs & GraphQL – Create APIs for data communication.
🔹 Stage 11: Authentication & Security – Implement JWT, OAuth, and HTTPS best practices.
🔹 Stage 12: Full Stack Project – Build a fully functional website with both frontend and backend.
🔹 Stage 13: Testing & Debugging – Use Jest, Cypress, or other testing tools.
🔹 Stage 14: Deployment – Host websites using Netlify, Vercel, or cloud services.
🔹 Stage 15: Performance Optimization – Improve website speed (Lazy Loading, CDN, Caching).
📂 Web Development Resources
ENJOY LEARNING 👍👍
🔹 Stage 1: HTML – Learn the basics of web page structure.
🔹 Stage 2: CSS – Style and enhance web pages (Flexbox, Grid, Animations).
🔹 Stage 3: JavaScript (ES6+) – Add interactivity and dynamic features.
🔹 Stage 4: Git & GitHub – Manage code versions and collaborate.
🔹 Stage 5: Responsive Design – Make websites mobile-friendly (Media Queries, Bootstrap, Tailwind CSS).
🔹 Stage 6: UI/UX Basics – Understand user experience and design principles.
🔹 Stage 7: JavaScript Frameworks – Learn React.js, Vue.js, or Angular for interactive UIs.
🔹 Stage 8: Backend Development – Use Node.js, PHP, Python, or Ruby to
build server-side logic.
🔹 Stage 9: Databases – Work with MySQL, PostgreSQL, or MongoDB for data storage.
🔹 Stage 10: RESTful APIs & GraphQL – Create APIs for data communication.
🔹 Stage 11: Authentication & Security – Implement JWT, OAuth, and HTTPS best practices.
🔹 Stage 12: Full Stack Project – Build a fully functional website with both frontend and backend.
🔹 Stage 13: Testing & Debugging – Use Jest, Cypress, or other testing tools.
🔹 Stage 14: Deployment – Host websites using Netlify, Vercel, or cloud services.
🔹 Stage 15: Performance Optimization – Improve website speed (Lazy Loading, CDN, Caching).
📂 Web Development Resources
ENJOY LEARNING 👍👍
❤3
Learn Django Easily 🤩
Here's all you need to get started 🙌
1. Introduction to Django
- What is Django?
- Setting up the Development Environment
2. Django Basics
- Django Project Structure
- Apps in Django
- Settings and Configuration
3. Models
- Creating Models
- Migrations
- Model Relationships
4. Views
- Function-Based Views
- Class-Based Views
- Generic Views
5. Templates
- Template Syntax
- Template Inheritance
- Template Tags and Filters
6. Forms
- Creating Forms
- Form Validation
- Model Forms
7. URLs and Routing
- URLconf
- Named URL Patterns
- URL Namespaces
8. Django ORM
- Querying the Database
- QuerySets
- Aggregations
9. Authentication and Authorization
- User Authentication
- Permission and Groups
- Django's Built-in User Model
10. Static Files and Media
- Serving Static Files
- File Uploads
- Managing Media Files
11. Middleware
- Using Middleware
- Creating Custom Middleware
12. REST Framework
- Django REST Framework (DRF)
- Serializers
- ViewSets and Routers
13. Testing
- Writing Tests
- Testing Models, Views, and Forms
- Test Coverage
14. Internationalization and Localization
- Translating Strings
- Time Zones
15. Security
- Securing Django Applications
- CSRF Protection
- XSS Protection
16. Deployment
- Deploying with WSGI and ASGI
- Using Gunicorn
- Deploying to Heroku, AWS, etc.
17. Optimization
- Database Optimization
- Caching Strategies
- Profiling and Performance Monitoring
18. Best Practices
- Code Structure
- DRY Principle
- Reusable Apps
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING 👍👍
#django #webdev
Here's all you need to get started 🙌
1. Introduction to Django
- What is Django?
- Setting up the Development Environment
2. Django Basics
- Django Project Structure
- Apps in Django
- Settings and Configuration
3. Models
- Creating Models
- Migrations
- Model Relationships
4. Views
- Function-Based Views
- Class-Based Views
- Generic Views
5. Templates
- Template Syntax
- Template Inheritance
- Template Tags and Filters
6. Forms
- Creating Forms
- Form Validation
- Model Forms
7. URLs and Routing
- URLconf
- Named URL Patterns
- URL Namespaces
8. Django ORM
- Querying the Database
- QuerySets
- Aggregations
9. Authentication and Authorization
- User Authentication
- Permission and Groups
- Django's Built-in User Model
10. Static Files and Media
- Serving Static Files
- File Uploads
- Managing Media Files
11. Middleware
- Using Middleware
- Creating Custom Middleware
12. REST Framework
- Django REST Framework (DRF)
- Serializers
- ViewSets and Routers
13. Testing
- Writing Tests
- Testing Models, Views, and Forms
- Test Coverage
14. Internationalization and Localization
- Translating Strings
- Time Zones
15. Security
- Securing Django Applications
- CSRF Protection
- XSS Protection
16. Deployment
- Deploying with WSGI and ASGI
- Using Gunicorn
- Deploying to Heroku, AWS, etc.
17. Optimization
- Database Optimization
- Caching Strategies
- Profiling and Performance Monitoring
18. Best Practices
- Code Structure
- DRY Principle
- Reusable Apps
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING 👍👍
#django #webdev
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How to convert image to pdf in Python
# Python3 program to convert image to pfd
# using img2pdf library
# importing necessary libraries
import img2pdf
from PIL import Image
import os
# storing image path
img_path = "Input.png"
# storing pdf path
pdf_path = "file_pdf.pdf"
# opening image
image = Image.open(img_path)
# converting into chunks using img2pdf
pdf_bytes = img2pdf.convert(image.filename)
# opening or creating pdf file
file = open(pdf_path, "wb")
# writing pdf files with chunks
file.write(pdf_bytes)
# closing image file
image.close()
# closing pdf file
file.close()
# output
print("Successfully made pdf file")
pip3 install pillow && pip3 install img2pdf👍1