MAT 395, Applied Data Science, MWF 1:00-2:20 pm, Noyce 2517

Syllabus

Professor: Jeff Blanchard, Noyce 2516, 269-3304, blanchaj (at) math.grinnell.edu

Resources: There is no required textbook for this course.

Office Hours:
Tuesday, Thursday: 2:20-3:15
By appointment weekdays excluding 8:30-10:45 am; please email me to set up a time. See my weekly schedule.

Learning Goals:

Applied Data Science involves the skills and tools necessary to solve problems, make recommendations, deepen insights, or create knowledge using data. Typically, data science applications involve problems, recommendations, or insights required by a client who help pose questions. The breadth of problems makes it likely that new skills and techniques will be needed to complete a specific data science project. This course will help prepare students to navigate an applied data science project by serving as members of a team working on a data project for an on-campus client. The learning goals for this course are:

Workload:

This class will meet approximately 2 hours 40 minutes per week. To accomplish the course learning goals and contribute to high quality projects, students should expect to spend at least an additional 9 hours 20 minutes per week of focused effort related to this course.

Graded Items:

There are several items that will be assigned a grade during the course of the semester. These items comprise 55% of the final grade.

Final Products:

The final products make up 45% of the final grade. The final products consist of four items:
Course Grade:
Individual Development: 15%
  • Communication skills: 5%
  • Creative, analytical, computing, and data skills: 5%
  • Participation: 5%
Project Management and Assessment: 25%
  • Weekly Progress Briefings: 10%
  • Goal Assessments: 5%
  • Advisor Team Assessment: 10%
Peer Review: 15%
  • Submitted Peer Reviews: 10%
  • External Peer Assessment: 5%
Course Project: 45%
  • Data Product: 10%
  • Executive Summary: 10%
  • Presentation: 10%
  • Technical Report: 15%

Academic Honesty:

All students must be aware of and comply with the Grinnell College Academic Honesty policy.

In this course students will work in teams to create data products for on-campus clients. It is a matter of academic honesty to maintain confidentiality with respect to off-campus communication of the clients' ideas, questions, data sources, etc. and any insights or tools you have generated using the intellectual property of the client. All public announcements related to the projects and any sharing of data provided by clients must be approved by Dr. Blanchard.

It is imperative that students properly cite sources used to obtain data, software, software packages, and analysis techniques in the course of the projects.

Attendance:

Absences permitted by the college (athletics, performance, religious observation, etc.) must be coordinated prior to the class period; this coordination must be done in person prior to the absence.

This course is collaborative by design and therefore requires attendance in class and at team meetings. While absences will impact the participation portion of a student's grade, excessive absences may result in reduced final letter grades:

Accommodations:

If you are in need of specific learning accommodations, please let me know early in the semester so that your learning needs may be appropriately met. If you have not already done so, you will need to provide documentation to and discuss your needs with the Coordinator for Student Disability Resources, John Hirschman (hirschma), located on the 3rd floor of the Rosenfield Center (x3089).

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