Building powerful software and innovative applications, I specialize in full-stack development, cloud computing, and AI-driven solutions. I’m driven to create projects that matter and to become one of Arizona’s top software engineers.
Years of Experience
Happy Clients
Completed Projects
I’m Daniel J. Berg, a computer science student at Arizona State University passionate about building impactful software. I specialize in full-stack development, cloud computing, and AI & machine learning, and I’m focused on creating projects that make a difference. My goal is to grow as a software engineer, contribute to meaningful projects, and secure an internship that brings me closer to becoming one of Arizona’s top software engineers.
I build complete web applications from frontend interfaces to backend services, integrating databases, APIs, and responsive design with clean, maintainable code.
I develop intelligent systems that analyze data, identify patterns, and generate predictions using practical machine learning and statistical techniques.
I deploy and manage applications on cloud platforms, focusing on scalability, reliability, environment configuration, and production-ready deployment.
Building a full-stack task management platform using React, JavaScript, HTML, and CSS with a Node.js and Python based backend exposing RESTful APIs. The system supports secure user authentication, task creation and tracking, and persistent storage with MongoDB, emphasizing clean UI/UX design and responsive interactions. Integrates data visualizations to display productivity trends and task completion metrics, with planned AI/ML features for task prioritization and insights. Deployed using AWS cloud services, demonstrating experience with scalable infrastructure, security fundamentals, and modern full-stack architecture.
Developed a full-stack stock analysis and prediction web application using React, JavaScript, HTML, and CSS with a Python (Flask) REST API backend. The platform retrieves live and historical stock ticker data via yfinance, generates short-term price predictions using a trained Linear Regression model, and presents results through an intuitive, responsive UI. Implemented a sliding ticker animation displaying real-time price movement with percentage changes and directional indicators, and deployed the application on Vercel, demonstrating full ownership from data ingestion and modeling to API design and production deployment.