Fall 2023 Syllabus - The Data Mine Seminar

There are officially 4 Monday class times: 8:30 am, 9:30 am, 10:30 am (all in the Hillenbrand Dining Court atrium—​no meal swipe required), and 4:30 pm (synchronous online, recorded and posted later). Attendance is not required.

All the information you need to work on the projects each week will be provided online on the Thursday of the previous week, and we encourage you to get a head start on the projects before class time. Dr. Ward does not lecture during the class meetings, but this is a good time to ask questions and get help from Dr. Ward, the T.A.s, and your classmates. The T.A.s will have many daytime and evening office hours throughout the week.

The Data Mine is a supportive environment for students in any major and from any background who want to learn data science skills. Students will have hands-on experience with computational tools for representing, extracting, manipulating, interpreting, transforming, and visualizing data, especially big data sets, and in effectively communicating insights about data. Topics include: the R environment, Python, visualizing data, UNIX, bash, regular expressions, SQL, XML and scraping data from the internet, as well as selected advanced topics, as time permits.

By the end of the course, you will be able to:

Attendance is not required.

When conflicts or absences can be anticipated, such as for many University-sponsored activities and religious observations, the student should inform the instructor of the situation as far in advance as possible.

For unanticipated or emergency absences when advance notification to the instructor is not possible, the student should contact the instructor as soon as possible by email or phone. When the student is unable to make direct contact with the instructor and is unable to leave word with the instructor’s department because of circumstances beyond the student’s control, and in cases falling under excused absence regulations, the student or the student’s representative should contact or go to the Office of the Dean of Students website to complete appropriate forms for instructor notification. Under academic regulations, excused absences may be granted for cases of grief/bereavement, military service, jury duty, parenting leave, and medical excuse. For details, see the Academic Regulations & Student Conduct section of the University Catalog website.

The Data Mine Team uses a shared email which functions as a ticketing system. Using a shared email helps the team manage the influx of questions, better distribute questions across the team, and send out faster responses.

For the purposes of getting help with this seminar class, your most important people are:

The office hours schedule is posted here.

Office hours are held in person in Hillenbrand lobby and on Zoom. Check the schedule to see the available schedule.

Piazza is an online discussion board where students can post questions at any time, and Data Mine staff or T.A.s will respond. Piazza is available through Brightspace. There are private and public postings. Last year we had over 11,000 interactions on Piazza, and the typical response time was around 5-10 minutes!

Click here to view the Fall 2023 Course Schedule

See the schedule and later parts of the syllabus for more details, but here is an overview of how the course works:

In the first week of the beginning of the semester, you will have some "housekeeping" tasks to do, which include taking the Syllabus quiz and Academic Integrity quiz.

Generally, every week from the very beginning of the semester, you will have your new projects released on a Thursday, and they are due 8 days later on the following Friday at 11:59 pm Purdue West Lafayette (Eastern) time. This semester, there are 14 weekly projects, but we only count your best 10. This means you could miss up to 4 projects due to illness or other reasons, and it won’t hurt your grade.

We suggest trying to do as many projects as possible so that you can keep up with the material. The projects are much less stressful if they aren’t done at the last minute, and it is possible that our systems will be stressed if you wait until Friday night causing unexpected behavior and long wait times. Try to start your projects on or before Monday each week to leave yourself time to ask questions.

Outside of projects, you will also complete 3 Outside Event reflections. More information about these is in the "Outside Event Reflections" section below.

The Data Mine does not conduct or collect an assessment during the final exam period. Therefore, TDM Courses are not required to follow the Quiet Period in the Academic Calendar.

We generally do NOT accept late work. For the projects, we count only your best 10 out of 14, so that gives you a lot of flexibility. We need to be able to post answer keys for the rest of the class in a timely manner, and we can’t do this if we are waiting for other students to turn their work in.

In this class grades reflect your achievement throughout the semester in the various course components listed above. Your grades will be maintained in Brightspace. This course will follow the 90-80-70-60 grading scale for A, B, C, D cut-offs. If you earn a 90.000 in the class, for example, that is a solid A. /- grades will be given at the instructor's discretion below these cut-offs. If you earn an 89.11 in the class, for example, this may be an A- or a B.

Academic integrity is one of the highest values that Purdue University holds. Individuals are encouraged to alert university officials to potential breaches of this value by either emailing or by calling 765-494-8778. While information may be submitted anonymously, the more information that is submitted provides the greatest opportunity for the university to investigate the concern.

In TDM 10100/20100/30100/40100/50100, we encourage students to work together. However, there is a difference between good collaboration and academic misconduct. We expect you to read over this list, and you will be held responsible for violating these rules. We are serious about protecting the hard-working students in this course. We want a grade for The Data Mine seminar to have value for everyone and to represent what you truly know. We may punish both the student who cheats and the student who allows or enables another student to cheat. Punishment could include receiving a 0 on a project, receiving an F for the course, and incidents of academic misconduct reported to the Office of The Dean of Students.

Good Collaboration:

Academic Misconduct:

The Purdue Honor Pledge "As a boilermaker pursuing academic excellence, I pledge to be honest and true in all that I do. Accountable together - we are Purdue"

Please refer to the student guide for academic integrity for more details.

This syllabus is subject to small changes. All questions and feedback are always welcome!