Welcome to your journey into Data Science! This repository contains hands-on projects designed for beginners learning data science with Python in 2025.
- Basic Python knowledge (variables, functions, loops)
- High school level mathematics
- Curiosity and willingness to learn!
- Data manipulation with Pandas
- Data visualization with Matplotlib & Seaborn
- Machine Learning with Scikit-learn
- Natural Language Processing with NLTK & TextBlob
- API integration and real-world data collection
- Statistical analysis and interpretation
Difficulty: Beginner
Technologies: Python, Scikit-learn, Pandas, Matplotlib, Seaborn
What you'll learn:
- Decision Tree classification algorithm
- Feature importance analysis
- Data visualization and exploration
- Model training and prediction
- Interactive prediction system
Skills gained:
- Basic machine learning concepts
- Classification vs regression understanding
- Feature engineering basics
- Model evaluation techniques
- Data visualization best practices
Difficulty: Beginner
Technologies: Python, Tweepy, TextBlob, Pandas, Matplotlib
What you'll learn:
- API integration and data collection
- Text preprocessing and cleaning
- Sentiment analysis techniques
- Data visualization and reporting
Skills gained:
- Working with social media APIs
- Natural Language Processing basics
- Data cleaning techniques
- Statistical analysis and visualization
Difficulty: Beginner
Technologies: Pandas, Matplotlib, Seaborn
What you'll learn:
- CSV data manipulation
- Exploratory Data Analysis (EDA)
- Business metrics calculation
- Dashboard creation
Difficulty: Intermediate
Technologies: Pandas, Scikit-learn, Matplotlib
What you'll learn:
- Time series analysis
- Machine learning regression
- Feature engineering
- Model evaluation
git clone https://github.com/yourusername/python-data-science-projects.git
cd python-data-science-projectspython -m venv .venv
# Windows
.\.venv\Scripts\Activate.ps1
# macOS/Linux
source .venv/bin/activatepip install pandas numpy matplotlib seaborn scikit-learn jupyter notebook tweepy textblob python-dotenv nltk- Pandas - Data manipulation and analysis
- NumPy - Numerical computing
- Matplotlib & Seaborn - Data visualization
- Scikit-learn - Machine learning
- Jupyter Notebook - Interactive development
- Start with Gender Classification ML project for ML basics
- Move to Twitter Sentiment Analysis for NLP concepts
- Practice data cleaning and visualization
- Advance to Sales Data Analysis for business insights
- Complete Stock Price Prediction for advanced ML
- Build your own project using learned concepts
- Large Language Models (LLMs) integration
- Automated Machine Learning (AutoML)
- Real-time data processing
- Ethical AI and bias detection
- Cloud-based data science platforms
- Machine learning fundamentals (Classification & Regression)
- Data collection from APIs and files
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Statistical analysis and hypothesis testing
- Model evaluation and interpretation
- Data visualization and storytelling
- Feature engineering and selection
- Problem-solving with data
- Critical thinking and analysis
- Communication of technical results
- Project management and documentation
- Data Analyst - Analyze business data for insights
- Junior Data Scientist - Build predictive models
- Business Intelligence Analyst - Create dashboards and reports
- Research Assistant - Support data-driven research
- ML Engineer - Deploy and maintain ML models
- Data Analyst: $50,000 - $75,000
- Data Scientist: $70,000 - $120,000
- ML Engineer: $80,000 - $140,000
- Choose a project that interests you
- Start with Gender Classification for ML basics
- Try Twitter Sentiment Analysis for real-world data
- Read the project README for specific instructions
- Set up your environment following the setup guide
- Start coding and experimenting!
- Document your learning in a personal journal
- Don't worry about perfection - focus on learning
- Practice regularly, even if just 30 minutes a day
- Join data science communities (Reddit, Discord, LinkedIn)
- Build a portfolio of completed projects
- Ask questions and seek help when stuck
- Always start with exploratory data analysis
- Document your code with clear comments
- Use version control (Git) for your projects
- Validate your results and assumptions
- Present findings clearly with visualizations
Want to add more projects or improve existing ones?
- Fork the repository
- Create a new branch for your project
- Add your project with proper documentation
- Submit a pull request
For questions, feedback, or collaboration, please contact:
- Email: aldobesma@gmail.com
- LinkedIn: https://www.linkedin.com/in/aldols/
This project is licensed under the MIT License - see the LICENSE file for details.
Happy Learning! 🎉
Remember: Every expert was once a beginner. Your data science journey starts here!