Credit hours: 2
This repo is meant to hold any code examples or other tutorials for class. The class moodle page serves as the primary source of material.
Data sharing and modern open data standards have been creating large repositories of data that remain disconnected. Many data science and machine learning techniques are boosted by incorporating data representing a variety of domains and granularities. Topics on data curation, data cleaning, copyright, web scraping, storage, processing, and automation will be reviewed. This course seeks to explore techniques and perspectives of combining various data sources to create a dataset ready for analysis, but in a project oriented space so that each topic is synthesized with practice and experienced in context. Students will select a project area and explore the technical and conceptual requirements of that project space, eventually producing a proof of concept around it. All project domains and areas are open, with the only requirement being that they combine several data sources into a new dataset. This course is meant for students who have completed at least two semesters of coursework, are comfortable with programming in Python (the project can be completed in any language, but instruction will be in Python), and desire a space to explore and develop a capstone or independent study project. However, further work on the project is not a requirement.
Guest speakers and field experts from the University Library will be invited. Students will be encouraged to share and publish their datasets at the end of the semester.
IS452 or demonstrated programming experience, 20 hours of completed coursework