Fall 2026 Syllabus - The Data Mine Seminar

Course Information

Course Number and Title CRN

TDM 10100 - The Data Mine I

possible CRNs 31842 or 12067 or 19810 or 12072 or 12073 or 12071 or 32121 or 32120

Course credit hours: 1 credit hour, so you should expect to spend about 3 hours per week doing work for the class

Prerequisites: TDM 10100 is an introductory course, with introduction to data analysis in Python, R, SQL. Students will learn about large language models, agent-based (agentic) models, image processing, containers, APIs, etc.

Course Web Pages

Meeting Times

There are officially 6 weekly class times: 8:30 am and 9:30 am, on Mondays, Wednesdays, and Fridays (all are in the Hillenbrand Dining Court atrium—no meal swipe 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. Instead, the seminar time is a good time to ask questions and get help from Dr. Ward, the T.A.s, and your classmates. Attendance is not required. The T.A.s will have many daytime and evening office hours throughout the week.

Course Description

The Data Mine is a supportive environment for students in any major and from any background who want to learn some 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.

Learning Outcomes

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

  1. Discover data science and professional development opportunities in order to prepare for a career.

  2. Explain the difference between research computing and basic personal computing data science capabilities in order to know which system is appropriate for a data science project.

  3. Design efficient search strategies in order to acquire new data science skills.

  4. Devise the most appropriate data science strategy in order to answer a research question.

  5. Apply data science techniques in order to answer a research question about a big data set.

Mapping and Assessment of Foundational Learning Outcomes (FLO) = Information Literacy

Please see the separate page for the section on Foundational Learning Outcomes:

the-examples-book.com/projects/fall2026/flo

Required Materials

  • A laptop so that you can easily work with others. Having audio/video capabilities is useful.

  • Access to Brightspace, Gradescope, and Piazza course pages.

  • Access to Jupyter Lab at the On Demand Gateway on Anvil: ondemand.anvil.rcac.purdue.edu/

  • "The Examples Book": the-examples-book.com

  • Good internet connection.

Guidance on Generative AI

The policy in this document applies to seminar coursework and to Corporate Partners projects. Some companies will also have additional AI policies that all students on their team need to follow. Use of generative AI tools needs to be approved by your company mentor prior to being used in the project. Work with your TA to check for approval and document it with The Data Mine.

AI tools may be used for:

  • Code debugging or optimization suggestions

  • Rewording or summarizing your own writing

  • Practicing concepts (e.g., asking AI to quiz you)

AI tools CANNOT be used for:

  • Submitting AI-generated responses as your own, without meaningful modification. Students need to provide explanations of their work, using full English sentences that the student wrote herself/himself.

  • Using AI to generate entire reports, essays, or coding projects, with minimal personal input.

  • Uploading or sharing confidential company or partner data to AI tools.

  • Using AI tools in assessment or exam settings, unless explicitly allowed.

Disclosure Requirement for Students

The usage of generative AI must always be documented. This is similar to the need to document books, papers, notes from other people, online sources, electronic resources, Stack Exchange / Stack Overflow, any websites, etc. It is necessary to document any source of any information that you use anytime!

If you use AI tools, you must include an explanation about where you used the AI tools in your work (e.g., you must provide such an explanation in your project template, or in your reporting to your Corporate Partner) describing:

  • Which tool was used

  • What it was used for

  • How the output was modified.

As the world of machine learning, deep learning, and AI continues to evolve, we wanted to offer some guidance on The Data Mine’s perspective for generative AI tools, such as ChatGPT.

New emergent technologies can be incredibly valuable tools. However, at the same time, it is important to keep perspective on how and when we utilize these new systems.

When using ChatGPT (or other generative AI) on a Data Mine project:

  • Never share a company’s code, data, information, or any other proprietary property with the tool.

    • While not all tools incorporate user input into their training, it is a very common practice and can lead to breaches in the NDA agreements.

  • Always question the response that the tool provides.

    • It is OK to ask different apps for suggestions on things like common algorithms or good starting points for problem solutions. However, it is VITAL to understand factors like where the solutions fit, how they perform, and how to measure their performance.

    • It is OK for a tool to recommend an algorithm for research. It is unacceptable to assume that the algorithm is the only correct answer and to not be able to explain why it was chosen. ("ChatGPT told me" will not be accepted.)

    • It is also occasionally possible that the tool will make up an answer, and you do not want to get stuck presenting false information.

  • If you are ever unsure about if a tool can be used, ask your mentor and The Data Mine BEFORE you use it.

    • We want to use new tools and adapt to the new environments, but our number 1 priority is to provide a safe and secure data environment. We cannot do anything that puts that at risk.

  • When using generative AI for code it is very important to understand the fundamental code’s functionality.

    • While generative AI can easily write if/else functions or for loops, if you do not understand how they work you will have a much harder time when it comes to writing a novel or highly specific code function.

    • Generative AI is great to help with ideas, but should not be used with no thought.

As with any new technologies, the world of generative AI is changing quickly. We encourage open discussion and welcome any feedback to The Data Mine concerning these technologies.

Ethical Considerations

  • Before submitting your work, you must critically evaluate AI-generated content for accuracy, bias, and fairness.

  • Please keep in mind that AI should support learning, not replace it.

  • Carefully keep in mind that AI-assisted work is still your responsibility.

The Purdue Honor Pledge is "As a Boilermaker pursuing academic excellence, I pledge to be honest and true in all that I do. Accountable together – We are Purdue."

Attendance Policy

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 link: Academic Regulations & Student Conduct of the University Catalog website.

How to succeed in this course

If you would like to be a successful Data Mine student:

  • Start on the weekly projects on or before Mondays so that you have plenty of time to get help from your classmates, TAs, and Data Mine staff. Don’t wait until the due date to start!

  • Be excited to challenge yourself and learn impressive new skills. Don’t get discouraged if something is difficult—you’re here because you want to learn, not because you already know everything!

  • Remember that Data Mine staff and TAs are excited to work with you! Take advantage of us as resources.

  • Network! Get to know your classmates, even if you don’t see them in an actual classroom. You are all part of The Data Mine because you share interests and goals. You have over 800 potential new friends!

  • Use "The Examples Book" with lots of explanations and examples to get you started. Google, Stack Overflow, etc. are all great, but "The Examples Book" has been carefully put together to be the most useful to you. the-examples-book.com

  • Expect to spend approximately 3 hours per week on the projects. Some might take less time, and occasionally some might take more.

  • Don’t forget about the syllabus quiz, academic integrity quiz, and outside event reflections. They all contribute to your grade and are part of the course for a reason.

  • If you get behind or feel overwhelmed about this course or anything else, please talk to us!

  • Stay on top of deadlines. Announcements will also be sent out every Monday morning, but you should keep a copy of the course schedule where you see it easily.

  • Read your emails!

Information about the Instructors

The Data Mine Staff

Name Title

Shared email we all read

[email protected]

Katie Sanders

Chief Operating Officer

Dr. Fulya Gökalp Yavuz

Director of Data Science

Dr. Mark Daniel Ward

Executive Director

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. You can use the Piazza forum to get in touch. In particular, Dr. Ward responds to questions on Piazza faster than by email.

Communication Guidance

  • For questions about how to do the homework, use Piazza or visit office hours. You will receive the fastest response by using Piazza versus emailing us.

  • For general Data Mine questions, students with Purdue e-mail accounts should use our ticketing system ;students without a Purdue e-mail account can e-mail [email protected]

  • For regrade requests, use Gradescope’s regrade feature within Brightspace. Regrades should be requested within 1 week of the grade being posted.

Office Hours

Office hours are held in person in Hillenbrand lobby and on Zoom. Check the schedule to see the available times. Links coming soon!

Piazza

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.

Assignments and Grades

Course Schedule & Due Dates

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 Monday, and they are usually due 9 days later on the following Wednesday at 11:55 pm Purdue West Lafayette (Eastern) time (there are a few exceptions to the Wednesday deadline - see the current schedule). 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 Wednesday 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.

Projects

  • The projects will help you achieve Learning Outcomes #2-5.

  • Each weekly programming project is worth 10 points.

  • There will be 14 projects available over the semester, and your best 10 will count.

  • The 4 project grades that are dropped could be from illnesses, absences, travel, family emergencies, or simply low scores. No excuses necessary.

  • No late work will be accepted, even if you are having technical difficulties, so do not work at the last minute.

  • There are many opportunities to get help throughout the week, either through Piazza or office hours. We’re waiting for you! Ask questions!

  • Follow the instructions for how to submit your projects properly through Gradescope in Brightspace.

  • It is ok to get help from others or online, although it is important to document this help in the comment sections of your project submission. You need to say who helped you and how they helped you.

  • Each week, the project will be posted on the Monday for the seminar, the project will be the topic of the seminar and any office hours that week, and then the most projects will be due by 11:55 pm Eastern time on the following Wednesday. See the schedule for specific dates.

  • If you need to request a regrade on any part of your project, use the regrade request feature inside Gradescope. The regrade request needs to be submitted within one week of the grade being posted (we send an announcement about this).

Outside Event Reflections

  • The Outside Event reflections will help you achieve Learning Outcome #1. They are an opportunity for you to learn more about data science applications and career development.

  • Throughout the semester, The Data Mine will have many special events and speakers, typically happening in person so you can interact with the presenter, but some may be online and possibly recorded.

  • These eligible opportunities will be posted on The Data Mine’s website (datamine.purdue.edu/events/) and updated frequently. Feel free to suggest good events that you hear about, too.

  • You are required to attend 3 of these over the semester, with 1 due each month. See the schedule for specific due dates.

  • You are welcome to do all 3 reflections early. For example, you could submit all 3 reflections in September.

  • You must submit your outside event reflection within 1 week of attending the event or watching the recording.

  • Follow the instructions on Brightspace for writing and submitting these reflections.

  • At least one of these events should be on the topic of Professional Development. These events will be designated by "PD" next to the event on the schedule.

  • You will answer questions directly in Gradescope including the name of the event and speaker, the time and date of the event, what was discussed at the event, what you learned from it, what new ideas you would like to explore as a result of what you learned at the event, and what question(s) you would like to ask the presenter if you met them at an after-presentation reception. This should not be just a list of notes you took from the event—it is a reflection.

  • We read every single reflection! We care about what you write! We have used these connections to provide new opportunities for you, to thank our speakers, and to learn more about what interests you.

  • For best practices: Meeting Etiquette

Here is some additional info:

  • Outside events should contribute to skill development and/or knowledge transfer, for example, by listening to a speaker, gaining new perspectives, or learning a new skill.

  • Callouts are eligible only if they involve skills related to data science, statistics, computing, or other technical areas.

  • Career Fair Criteria

    • Conversations at career fairs may be counted if they meet the following standards:

      • The interaction must demonstrate a meaningful exchange at a sufficient level of technical depth and/or business depth.

      • The conversation should provide evidence of substantive discussion beyond surface-level introductions.

      • Brief interactions, such as handing over a resume or engaging in a short exchange of two minutes or less, do not qualify.

Regarding videos:

YouTube videos may also count as outside events, provided they are relevant to data science, statistics, computing, or other technical fields. However, passive viewing is not sufficient. To receive credit:

  • The video must have clear educational value (tutorials, lectures, or presentations).

  • The key points learned should be summarized with an explanation of how they contribute to skill development.

  • Short clips (under 5 minutes) or entertainment-focused videos do not qualify.

  • Videos should be at least 45 minutes.

  • Please be sure to cite the video that you used in your report.

Late Work Policy

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.

Grade Distribution

Projects (best 10 out of Projects #1-14)

86%

Outside event reflections (3 total)

12%

Academic Integrity Quiz

1%

Syllabus Quiz

1%

Total

100%

Grading Scale

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 range, B range, C range, D range, and F range cut-offs. If you earn a 90.000 in the class, for example, that is A range, which could include A-, or A, or A+, given at the instructor’s discretion between these cut-offs.

  • A range [A-, A, A+]: 100.000% - 90.000%

  • B range [B-, B, B+]: 89.999% - 80.000%

  • C range [C-, C, C+]: 79.999% - 70.000%

  • D range [D-, D, D+]: 69.999% - 60.000%

  • F range: 59.999% - 0.000%

Academic Integrity

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 [email protected] 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, 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:

  • First try the project yourself, on your own.

  • After trying the project yourself, then get together with a small group of other students who have also tried the project themselves to discuss ideas for how to do the more difficult problems. Document in the comments section any suggestions you took from your classmates or your TA.

  • Finish the project on your own so that what you turn in truly represents your own understanding of the material.

  • Look up potential solutions for how to do part of the project online, but document in the comments section where you found the information.

  • If the assignment involves writing a long, worded explanation, you may proofread somebody’s completed written work and allow them to proofread your work. Do this only after you have both completed your own assignments, though.

Academic Misconduct:

  • Divide up the problems among a group. (You do #1, I’ll do #2, and he’ll do #3: then we’ll share our work to get the assignment done more quickly.)

  • Attend a group work session without having first worked all of the problems yourself.

  • Allowing your partners to do all of the work while you copy answers down, or allowing an unprepared partner to copy your answers.

  • Letting another student copy your work or doing the work for them.

  • Sharing files or typing on somebody else’s computer or in their computing account.

  • Getting help from a classmate or a TA without documenting that help in the comments section.

  • Looking up a potential solution online without documenting that help in the comments section.

  • Reading someone else’s answers before you have completed your work.

  • Have a tutor or TA work though all (or some) of your problems for you.

  • Uploading, downloading, or using old course materials from Course Hero, Chegg, or similar sites.

  • Using the same outside event reflection (or parts of it) more than once. Using an outside event reflection from a previous semester.

  • Using somebody else’s outside event reflection rather than attending the event yourself.

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.

Incidents of academic misconduct in this course will be addressed by the course instructor and referred to the Office of Student Rights and Responsibilities (OSRR) for review at the university level. Any violation of course policies as it relates to academic integrity will result minimally in a failing or zero grade for that assignment, and at the instructor’s discretion may result in a failing grade for the course. In addition, all incidents of academic misconduct will be forwarded to OSRR, where university penalties, including removal from the university, may be considered.

Purdue Policies & Resources

Disclaimer

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