Scholarship Matchmaker Algorithm Development

The Scholarship Whizz seeks to enhance its platform by developing an advanced algorithm that efficiently matches students with scholarship opportunities tailored to their academic profiles, preferences, and eligibility criteria. The project challenges students to design and implement a sophisticated matching algorithm that provides personalized scholarship recommendations, thereby increasing user satisfaction and platform reach. This initiative offers students a practical opportunity to apply machine learning techniques to a real-world problem, focusing on the optimization of scholarship matching processes. The project is structured to be completed within 60 hours over a period of 2-8 weeks, allowing students to gain hands-on experience in algorithm development and data analysis. By the end of the project, students will have a deeper understanding of how machine learning can be leveraged to address challenges in educational technology.

Modular Code Refinement for Data Analysis Reports

Pathomics Inc. specializes in data analytics services, generating code to deliver insightful results to clients. The current challenge is twofold: first, that the code, while functional, lacks modularity, making it difficult to reuse and adapt for different client needs. Second, the report generation is static, and could benefit from the addition of an interactive dashboard for client use. The goal of this project is to refine existing code into well-structured, reusable modules that can be easily assembled to produce customized analysis reports, and into cloud-based online dashboards for data interrogation. This will enhance the efficiency and flexibility of Pathomics Inc.'s service delivery. The project will provide learners with the opportunity to apply their programming and software engineering skills to real-world problems, focusing on code refactoring, modular design, cloud & dashboard configuration, and documentation. The tasks will involve analyzing existing code and company processes, identifying reusable components, and restructuring the codebase to improve maintainability and scalability.