Instructor:
Class Meetings:
Office Hours:
Students will work in small teams on an applied data science project completing the full spectrum of the data science process including developing the problem statement, collecting and processing data, identifying and employing mathematical and/or statistical methods to extract information from the data, implementing the quantitative methods in an appropriate programming environment, and generating conclusions supported by data
There are no required textbooks for this course, but the following resources are recommended (and freely available):
Other materials will be made available via our course website: https://remiller1450.github.io/s330s25.html
The overarching goal of this course is to prepare students to successfully navigate an applied data science project involving working on a team to answer questions posed by an external client. This includes developing “soft” skills, such as how to understand and communicate client needs, translating client needs into actionable items, and making data-driven recommendations; and also “hard” skills, such as learning and implementing the techniques, tools, and methods needed to solve problems.
Specific learning goals for this goal include:
Coding proficiency is expected prior taking this course. For
most projects you may use any programming language your team deems
suitable; however using either R or Python is strongly
recommended. These are currently the most commonly languages among
professional data scientists.
Attendance
This is a highly collaborative course that involves substantial peer-review and ongoing feedback, thus making regular attendance critical.
All absences permitted by the college (athletics, religious observation, documented medical, etc.) must be communicated as early as possible and will not count against you. All other absences (illnesses, job interviews, etc.) can negatively impact your grade if they are persistent and excessive. The schedule below summarizes the impact of such absences (reminder that college permitted absences do not count towards this schedule):
Additionally, this should go without saying but the course begins at 1:00pm and we will start promptly at that time. Persistent tardiness may be counted towards the absence schedule above, which every 20 minutes of tardiness constituting an absence (since this equates to missing an entire progress presentation). For example, being 10-minutes late on two different days will count as an absence. However, being over 20-minutes late on a single day will not count as an absence unless you are also late on another day (since being late once is not “persistent tardiness” and I recognize that it sometimes will happen),
Workload
This class meets twice a week for 80 minutes (totaling 2 hours and 40 minutes), to adhere to the standard workload expectations at Grinnell it is expected that you devote, on average, approximately 9 hours and 20 minutes of time outside of class each week towards the course. While I hope you find your project personally exciting, I encourage you not to devote more time than this. Additionally, if you suspect someone in your group is not putting in an appropriate amount of time I ask that you contact me as soon as possible rather than waiting until the end of the semester to let me know that imbalanced workload was an issue within your group.
Academic Honesty
At Grinnell College you join a conversation among scholars, professors, and students, one that helps sustain both the intellectual community here and the larger world of thinkers, researchers, and writers. The tests you take, the research you do, the writing you submit-all these are ways you participate in this conversation.
The College presumes that your work for any course is your own contribution to that scholarly conversation, and it expects you to take responsibility for that contribution. That is, you should strive to present ideas and data fairly and accurately, indicate what your own work is, and acknowledge what you have derived from others. This care permits other members of the community to trace the evolution of ideas and check claims for accuracy.
Failure to live up to this expectation constitutes academic dishonesty. Academic dishonesty is misrepresenting someone else’s intellectual effort as your own. Within the context of a course, it also can include misrepresenting your own work as produced for that class when in fact it was produced for some other purpose. A complete list of dishonest behaviors, as defined by Grinnell College, can be found here.
Inclusive Classroom
Grinnell College makes reasonable accommodations for students with documented disabilities. Students need to provide documentation to the Coordinator for Disability Resources, information can be found here. Students should then speak with me as early as possible in the semester so that we can discuss ways to ensure your full participation in the course and coordinate your accommodations.
Religious Holidays
Grinnell College encourages students who plan to observe holy days that coincide with class meetings or assignment due dates to consult with your instructor in the first three weeks of classes so that you may reach a mutual understanding of how you can meet the terms of your religious observance, and the requirements of the course.
Intermediate items (60%):
Final Products (40%):
A schedule for the semester can be found on the course website: https://remiller1450.github.io/s330s25.html