Course Description:
The goal of this course is to provide Professional Masters students with industry mentorship and real-world data science training. Beyond-classroom educational opportunities are an excellent way to gain practical experience on a substantial project, to learn advanced skills, to collaborate with a professional PhD researcher, to form a connection to a data science company, and to work in a team with other graduate students. Industry partners propose semester-long data science projects. Students will be grouped into 2-4 person teams, each of which work on one project throughout the semester, under the guidance of their industry mentor, additional PhD student mentors, and the course faculty instructor. Furthermore, in weekly class meetings all students receive professional development education, data science hardware and software infrastructure training, data science research presentations, and career advice. Student teams gain valuable oral presentation experience and feedback by regularly presenting their work-in-progress, as well as a final public presentation of their project at the end of the semester. Advantages of these industry relationships often include access to rich industry-scale data, learning about real-world problems, and making industry connections useful for the future.
Assignments & Grading
You will be evaluated by the instructors on your work in the course as described below. There will also be one or two intra-team peer feedback opportunities, which will be incorporated into your grade as described below:
Project Proposal (20%)
Presentation (10%) & Report (10%)
Midpoint Milestone (20%)
Presentation (10%) & Report (10%)
Final Presentation (40%)
Presentation (20%) & Report (20%)
Peer and Mentor Evaluations (10%)
Ethics Assignment (5%)
Career Panel Assignment (5%)
Minor revisions may be made to grading rubric in the first two weeks of the course and will be announced to the students on the mailing list.
Project Presentation and Report Grading
The purpose of project presentations and reports is to demonstrate thoroughness of understanding, exploration, and completion with respect to all aspects of the project. Students' presentations and reports will be evaluated based on these criteria. Communication should be efficient in conveying the problems and solutions and why a particular solution should be or was implemented and evaluated. Achieving state-of-the-art results is neither sufficient nor necessary for a satisfactory grade.
Performance Evaluations
This course is entirely a group research project. It is imperative that each group works as a team. One or two group members doing the vast majority of the work is unacceptable and will lead to a failed project. It is not solely the fault of the lagging group members for the disproportionate workload — it is also the responsibility of those doing the bulk of the work. It is the responsibility of every group member that work is distributed evenly. If one group member knows something that the others do not, they need to teach that to the rest of the group members. If one student does not understand something, they are obligated to ask the other group members for an explanation, no matter how uncomfortable it may be for that student.
You will complete four peer and mentor evaluations throughout the semester. This will allow the course staff to assess the strength of the team dynamic.
Lecture Format
The course meetings will take a variety of forms including: research method strategies and best practices; teams giving presentations on their current progress; panels and discussion about career paths; and more.