Earning Criteria

• Students are tasked to collaborate in small groups to create compelling visualizations by cleaning and formatting datasets from external APIs and Matploblib. In addition to writing a detailed report of their data exploration and cleanup, students are expected to prepare a professional presentation that includes an illustration of the final data analysis.
• Extract, Transform, and Load ("ETL") Mini Project: For this project, students will gain hands-on experience using ETL design. Students will use their new skills to extract datasets by scraping or using a third-party API. Once complete, students will clean, join, filter, and aggregate information.  The data will then be loaded into a relational or nonrelational database. Students will complete this project by submitting a final report detailing their process from start to finish.
• For their third project, students are tasked to work in small groups to display data for stakeholders in an interactive and visually compelling way. In addition to using their fundamental knowledge of R programming language, it is expected that they include Python Flask application powered by a large dataset. The end product should be user-focused and have multiple different views on data visualization.
• For their final projects, students are challenged to identify a real-world problem which they will attempt to solve by analyzing and or visualizing data using Machine Learning Libraries. Students are encouraged to use creative freedom for this project but are also expected to incorporate technologies, languages, and concepts learned throughout the course. Students will present and defend their final projects to their classmates in a professional manner. 
• In addition to the four projects, students must complete and turn in all but two homework assignments in order to stay eligible for the course certificate.