Objectives
What You Will Learn
How will you succeed in this course?
Participate. You are expected to participate actively in the course based on your own learning goals. Since you all come from different backgrounds and experiences of data science, your peers are valuable resources for learning. Don’t shortchange them and yourself by coming to class without preparing or by sitting quietly during class discussion.
Communicate. This course may be unlike any of your previous courses, with increasingly complex content and new kinds of technical challenges. I am committed to helping you address these new challenges, and therefore have an open door policy in addition to class and office hours; I will meet with you or respond to your email within 24 hours whenever possible. You should let me know what ideas and tools are challenging to you and how you are doing in the class. If you start this habit early in the semester, then I will be able to better tailor our activities to help you learn. If you’re not comfortable with email or office hours, then post a comment in Anonymous Feedback in the class Canvas site.
Take risks. Programming often requires personal judgments about what to include or ignore, which structural approach to follow, and/or how to interpret complex data. Sometimes the “right” answer is unknown, incomplete, or even wrong! Nobel Prize breakthroughs have often resulted from attempting to support a “best guess” with incomplete data or from finding evidence to explain an “experiment gone wrong.” You will be rewarded for going out on a limb to defend your ideas as long as your assumptions and decision‐making process are transparent in your answers. If you’re not sure how to start a problem, don’t be scared to defend your assumptions and go for it!