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Data science brings together techniques from computing, mathematics, and statistics to extract knowledge from data in fields of application as diverse as climate science, particle physics, electoral politics, literary analysis, and countless others. This course provides an introduction to data science techniques. First, using the computational package R, students will learn how to acquire, clean, explore, summarize, visualize, and communicate data. Second, in a series of nontechnical guest lectures, professional data scientists will share the types of work they do. Finally, students will carry out a small project, applying their data science skills to problems that interest them. This course requires no background in computer programming, mathematics, or statistics. However, this is an intensive course, and students must have enthusiasm to learn programming and a willingness to practice this skill for several hours per day.
Format: seminar; To afford students flexibility during the COVID pandemic, this course is taught online. Students will watch videos and complete data science modules asynchronously, and will participate in occasional synchronous lectures.
Grading: pass/fail only
Students will complete data science modules and a final project. To move towards a non-hierarchical, transparent, and egalitarian grading system, the instructor follows the policy of "ungrading." Over the course of the term, students will develop a rubric to assess their own learning and will evaluate themselves according to this rubric.
Willingness to learn a new field; willingness to practice computer programming intensively.
Contact the Office of the Dean of the College.
This course is designed to count for both full semester, Winter Study, and QFR credit. Once a dean approves enrollment, the Registrar's Office will register students in both MATH 101 and MATH 41.