🔰 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

➖➖➖➖➖

🔢 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

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 👍👍

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 👍👍

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

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

🔅 Barcode Generation using Python