Students will submit a capstone proposal to their intended faculty capstone advisor before the dates indicated on the form. Once the capstone advisor approves the proposal, the form will route to the Recorder who will add permission to enroll within 48 hours. Students should also complete the intent to graduate form at this time.
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Faculty Advisors
Ideally, you want to work with a faculty whose expertise aligns with your academic interests, experience, and career path. Faculty availability is not guaranteed, depending on the faculty’s other assignments and duties. Plan your capstone early and thoroughly. Here is the list of faculty advisors in the Informatics program.
Students are given the opportunity to select from real-world or from various community organizations projects. The students are mentored on how to best apply all their acquired skills, academic knowledge, and use of the Systems Development Life Cycles (SDLCs) and sound Informatics project management to scope, design, develop, and present their completed project. Students are mentored mainly in two types of Capstone projects; (1) Digital Transformation (DX) and the design, development, use, and leverage of Technology and Computing to harness the power of the data to solve problems or explore opportunities in all aspects of our lives, including software development, technology integration, the use of APIs with apps and web applications, and data analytics and visualization; (2) The applications of modern Ai models and bot platforms such as the implementation of Robotic Process Automation (RPA) to deploy a digital workforce or the implementation of cognitive bots to improve the user interface/experience (UI/UX) and increase the throughputs in business, organization, and technology by improving user experience and business insights.
Students will identify real-world accessibility problems or issues through direct interaction with people with disabilities, and then develop an information system as the solution to solve the problems or address the issues. Students will be responsible for identifying participants with disabilities, conducting formative research using ability-appropriate data collection methods, developing personas or portraits to represent participant needs, and then generating accessible design artifacts and soliciting user feedback. Prerequisites: INFO-I481 Experience Design and Evaluation of Access Technologies.
Students will collaborate in the activities of the Interactive Embodied Spaces (IES) Laboratory. They will contribute to the design, implementation, and evaluation of interactive data visualizations for informal learning. Activities will be coordinated with the instructor, and may include working with Unity and C#, leading user studies, conducting statistical analysis of quantitative data and thematic analysis of qualitative data.
Students will identify a problem and devise a solution to the problem using available datasets or curating a dataset. The data-driven solution should involve techniques from (but not limited to) machine learning, deep learning, and natural language processing. The students are expected to visualize their results and validate their findings through a sound evaluation plan. The problem should ideally address a real-world issue, preferably within the domains of agriculture, health, education, and sustainability.
Option 1: This course assumes prior knowledge and experience with procedural and object-oriented programming in PHP, designing information systems with the MVC pattern, and developing dynamic, data-driven web applications with server-side technologies and relational databases. Prior to taking this course, students are expected to complete INFO I211 Information Infrastructure II with a C or better grade or have acquired equivalent experiences, and INFO-I425 Applications of Web Services.
Option 2: If the students are interested in the Data Mining related project topic, INFO-I223 Data Fluency and INFO-I421 Applications of Data Mining is required. And you need to be familiar to Python or R programming language.
Prerequisite: Be familiar with Python.
Students will identify an NLP-related problem or question. Given the nature of the problem, students will either use publicly available datasets to address the problem or develop a pipeline to curate a new data resource for problem-solving. The design of the solution tends to focus on learning-based models. Students are expected to update their results and findings regularly to the instructor and submit a final project report at the end of the semester.
Students are expected to propose a data-intensive problem or question of their own choosing, and to identify a data source that can be used to address this problem. Students will then design and implement an interactive data analysis and presentation (e.g., visualization) pipeline using tools such as R, Python, Tableau, Vega, and D3. Students will report their conclusion and reflect on their solution in a project report and/or an interactive portfolio. The student will regularly coordinate with and report their progress to the instructor via bi-weekly meetings.
Prerequisite: INFO-I 211
Students will identify real-world problems or issues and then develop an information system as the solution to solve the problems or address the issues. The information system must be designed with the MVC pattern. The underlying business logic must be modeled with OOP objects. The data layer can be implemented with relational databases or RESTful APIs. The application must be capable of performing CRUD operations, authenticating, and authoring users.