My name is Anthony Baratti and I am a Software Engineering Student at Southern New Hampshire University (2022-2025).
I enjoy working on database management and program design.
One of my favorite activities to do is read or design diagrams such as sequence, class/object, activity, and use case diagrams.
I have studied Client-Server Development, Data Structures & Algorithms, Emerging Technologies (Artifical Intelligence), Full Stack Development (MongoDB, Express, AngularJS, and Node.js) & Cloud Deployment (Docker and AWS), Secure Coding Principles,
Mobile Architecture & Application Development, System Analysis & UI/UX Design, Reverse Engineering, Testing, Computer Graphics (OpenGL), and Micro-Controller Programming.
I have used Java, Python, C/C++, JavaScript and TypeScript, and have used JSON and SQL modeling to create and manage databases in MongoDB, AWS, and SQLite3 with knowledge of tools such as Postman and MongoDB compass.
I have used Eclipse IDE, Pycharm, Juptyer Notebook, Spyder, Microsoft Visual Studio, Visual Studio Code, Android Studio, among many others. I have developed cloud services using AWS platform, using S3, Lambda Functions, and API Gateways.
This project showcases artifacts from courses at SNHU's Software Engineering Program.
There are three enhancements on 2 artifacts. The first artifact is a Binary Search Tree the sorts a csv file of courses into
a search tree data structure. The first enhancement was to create more user functionality, offering an add custom course method
with a menu option, and a delete course method with a menu option.
These two additional functions run the risk of degrading the Binary Search Tree search time complexity from O(log n) to O(n). To prevent
this breakdown, enhancement two adds self-balancing algorithms into the insert and delete methods that will rotate the nodes of the tree should
it become unbalanced (See read me files for full explanations).
The last enhancement is done on artifact two, which is a graphical user interface (GUI) deployed on a webpage for filtering a database with animals.
The original artifact is scripted in Python using MongoDB and JSON formatted for database reads. The enhancement performed was converting from an online
database to an SQLite3 local database. This requires minimal setup for users to interact with. It provides a data table filtered by animal type and breed,
a data display pie chart, and an interactive geo-location map.
You can reach me at: AnthonyBaratti@gmail.com