Java-Android-DataScience-Portfolio

🚀 Welcome!

Hi there! 👋 Welcome to my GitHub portfolio!

With 15 years of data analysis experience and a year of immersive coding bootcamps, I have transitioned into software development and data science, focusing on mobile applications, machine learning, and algorithm optimization.

This repository showcases my work in Java programming, Android development, and Data Science, following best practices like Clean Architecture and Google’s modern MVVM approach for scalable applications. I am enthusiastic about collaborating with other developers and data scientists to learn, build, and grow together.

🤝 Let’s Connect!

📧 Email: [email protected]
🔗 LinkedIn: www.linkedin.com/in/minervacfranco
🛠️ GitHub Issues – Open a discussion!

🚀 Let’s build, learn, and grow together! 🚀

📌 Table of Contents

1. Repository Overview
2. Technologies Used
3. Getting Started
4. Architecture Overview
5. Testing
6. Roadmap
7. Known Issues
8. FAQ
9. Contributing
10. License
11. 🙌 Acknowledgments

🔥 Repository Overview

This repository includes projects across three key areas:

📱 Android Applications

• Chat App – Real-time messaging using Firebase.
• Farkle App – A digital version of the dice game Farkle.
• NASA APOD App – Fetches NASA’s Astronomy Picture of the Day using an API.
• Notes App – A simple note-taking application with local storage.
• Space Seek App – An interactive space exploration app.

🧩 Algorithmic Challenges & Data Structures

• Complementary DNA Task – String manipulation challenge.
• Concurrent Computation Task – Multi-threading and parallel processing.
• Fizz Buzz Game – Classic coding challenge with variations.
• Greatest Common Divisor Task – Mathematical algorithm implementation.

📊 Data Science & Machine Learning

• Binary Classification – Transaction fraud detection using machine learning.
• Natural Language Processing (NLP) – Wikipedia text analysis.
• Neural Networks – Feature extraction using Convolutional Neural Networks (CNNs).
• Regression Models – Tree-based regression for Spotify data and housing price predictions.
• SQL & Data Engineering – SQLite3 database analysis using the Chinook dataset.

🛠 Technologies Used

• Java & Kotlin – Core programming languages for mobile development.
• Android SDK & Jetpack Libraries – MVVM, LiveData, ViewModel, Room Database.
• Clean Architecture – Layered separation of concerns for scalable applications.
• Python & Jupyter Notebooks – Machine learning and data science workflows.
• Scikit-learn & TensorFlow – ML model training and optimization.
• SQLite3 & Pandas – Data analysis and database management.
• Git & GitHub Actions – Version control and CI/CD workflows.

🚀 Getting Started

Prerequisites

Ensure you have the following installed:

• Java Development Kit (JDK) 11+
• Android Studio or IntelliJ IDEA
• Python 3.x & Jupyter Notebook
• Scikit-learn & TensorFlow
• Gradle
• Git

Installation

git clone https://github.com/minerva-devs/Java-Android-DataScience-Portfolio.git
cd Java-Android-DataScience-Portfolio

Open the project in IntelliJ IDEA, Android Studio, or Jupyter Notebook, depending on the project type.

⚙️ Configuration

Some projects may require additional configurations:

• API keys or environment variables should be placed in a .env file or configured within the project settings.
• Database setup instructions are included in relevant project folders.

⚙️ Architecture Overview

🏗️ Clean Architecture in Java & Android

This repository follows Clean Architecture, ensuring:

• Separation of concerns – Business logic remains independent of frameworks.
• Scalability – Easy to extend and modify without affecting core functionality.
• Testability – Decoupled layers make unit testing more effective.

📱 MVVM (Google’s Modern Approach)

For Android projects, MVVM (Model-View-ViewModel) is used to:

• Decouple UI logic from business logic using ViewModel.
• Improve data handling with LiveData and Repository patterns.
• Enhance maintainability by structuring components efficiently.

📊 Machine Learning Workflow

For data science projects, the repository follows best practices in ML development, including:

• Preprocessing & Feature Engineering – Cleaning and transforming data for model training.
• Model Selection & Optimization – Using tree-based regression, neural networks, and classification models.
• Evaluation & Deployment – Assessing model performance and integrating results into applications.

🧪 Testing

Run tests using:

./gradlew test  # For Java/Android projects

For Python & ML projects:

pytest  # Run unit tests
jupyter notebook  # Open notebooks for interactive analysis

🛣️ Roadmap

Planned improvements:

• Enhance UI for ongoing Android projects.
• Add more advanced data structures and ML models.
• Optimize algorithm implementations for better performance.
• Launch an Android app on the Google Play Store.

🐞 Known Issues

• Some Android apps have incomplete UI elements.
• Certain Java implementations may need performance optimizations.
• Some ML models require further tuning for better accuracy.

❓ FAQ

Q: Can I contribute?
A: Yes! I am actively looking for collaborators to learn from and build great projects together.

Q: Are all Android apps fully functional?
A: Some are complete, while others are still in progress.

Q: What IDE should I use?
A: IntelliJ IDEA for Java, Android Studio for mobile development, and Jupyter Notebook for data science projects.

🛠 Contributing

I am looking forward to collaborating and learning! If you’d like to contribute:

git checkout -b feature-branch
git commit -m "Add new feature"
git push origin feature-branch

Then, open a Pull Request.

📜 License

This project is licensed under the MIT License – see the LICENSE file for details.

🙌 Acknowledgments

I want to express my gratitude to the incredible instructors and support staff at CNM Ingenuity's Deep Dive Coding program for their guidance and encouragement:

• Robert Citek, Joe Olonia, Nick Bennett & Reed Searle – For their instruction and technical support throughout the program.
• Devonna James, Esteban Martinez & Sue Andres – For their invaluable contributions as program director, career success coach, and wraparound support staff.

Their mentorship has been instrumental in shaping my journey. Thank you!

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