Samiur Rahman

Personable and highly motivated, with foundation in relevant academic coursework and projects. Demonstrates fundamental understanding of key industry principles and possesses strong analytical and problem-solving skills. Aims to contribute effectively and make positive impact through diligent work and innovative solutions.

2024

• Developed a single-page application (SPA) using vanilla JavaScript for users to search, bookmark, and upload food recipes.
• Implemented key features such as state management, pagination, and dynamic loading of search results using the Fetch API and Promises.
• Designed a custom DOM updating algorithm with the DocumentFragment API, enhancing performance and minimizing re-rendering.
• Utilized local storage to save user-generated recipes and bookmarks for future retrieval.
• Deployed the application using Vite and hosted on Netlify.

2023

• Built a full-stack recipe-sharing web application using the MERN stack (MongoDB, Express, React, Node.js).
• Implemented secure user authentication and registration with JWT (JSON Web Tokens) and bcrypt for password hashing.
• Added features allowing users to create, edit, delete, and rate recipes, with data stored in a MongoDB database.
• Designed a responsive front-end with React and Material-UI, ensuring a seamless experience on both desktop and mobile.
• Developed back-end RESTful API endpoints for recipe management using Express.js and Node.js.

2023

• Created a recipe discovery application with React, allowing users to browse, search, and explore recipes from diverse cuisines.
• Integrated React Router for smooth navigation across sections like popular recipes, veggie picks, and various cuisine categories.
• Built a clean, responsive user interface using Material-UI.
• Optimized application performance and development speed by deploying with Vite for fast builds and hot module replacement.

2023

• Conducted exploratory data analysis on a food claim dataset from a fast-food chain, focusing on claim processing times across multiple locations.
• Cleaned and validated the data, addressing missing values, outliers, and standardizing formats.
• Used Matplotlib and Seaborn to visualize patterns and provide insights.
• Presented findings on geographical variations in claim closure times and recommended improvements for processing efficiency.