Fall 2025 Syllabus - The Data Mine Corporate Partners (

Quick Reference Table

No Section Description

1

Course Description

Overview of course structure, goals, skills, and industry-focused learning.

2

Course Registration

Course numbers, CRNs, and enrollment details.

3

Course Prerequisites

Requirements for undergraduate and graduate students.

4

Course Commitments

Weekly time expectations for lectures, labs, and outside work.

5

Course Schedule & Due Dates

Link to full semester schedule and alternating PD/Sprint Report deadlines.

6

Required Tools & Course Resources

Tools, software, and platforms needed for this course.

7

Team & Communication

Roles of staff, mentors, and TAs; communication expectations.

8

Assignments and Grades

PD + Sprint Report structure; grading breakdown; late policy.

9

Learning Outcomes

Skills and competencies gained by the end of the course.

10

Project Management & Agile

Overview of Agile workflow used in Corporate Partner projects.

11

Course Policies

Attendance, confidentiality, NDA/IP, AI policy, classroom behavior.

12

Purdue Policies & Resources

Integrity, accessibility, mental health, inclusion, campus safety.

Course Description

Students in The Data Mine Corporate Partners Learning Community build both technical and professional skills through real industry projects and structured professional development.

In this course, students will:

  • Work in interdisciplinary teams on real corporate-sponsored data projects.

  • Use an adapted Agile workflow, including iterative development, sprint planning, and retrospectives.

  • Strengthen communication, teamwork, and professional presence.

  • Apply data science, analytics, and visualization skills to solve authentic business problems.

  • Develop career-readiness through guided professional-development activities.

  • Collaborate with Corporate Partner Mentors and Data Mine staff for support and feedback

Course Registration

Course Number and Title CRN

TDM 11100 – Corporate Partners I

CRNs vary

TDM 21100 – Corporate Partners III

CRNs vary

TDM 31100 – Corporate Partners V

CRNs vary

TDM 41100 – Corporate Partners VII

CRNs vary

TDM 51100 – Corporate Partners

CRNs vary

Course Prerequisites

Student Prerequisites

Undergraduate students

- Submitted Application
- Enrolled in 1 credit hour data science seminar: General Cohort

Graduate students

- Submitted Application

Course Commitments

Course credit hours: 3 credits

This course operates differently from standard academic classes. Below is an overview of the expected weekly time commitments for each component of the course. The team meeting times are listed on your registered course schedule. Students are expected to actively participate in meetings and in all individual and group work.

Meeting Type Duration Description

Lecture (Team Meeting)

50 minutes

Facilitated by your TA and attended by the industry mentor(s). This meeting focuses on sharing updates, discussing next steps, and aligning on project goals. Active participation and preparation are expected.

Lab

1 hour 50 minutes

Led by your TA, this session provides focused time for collaboration and project work. Labs include brief check-ins, sprint reminders, and hands-on progress toward team goals. Active participation and attendance are required.

Outside Class

5-7 hours

Reserved time for completing assignments, progressing on project tasks, preparing reports or presentations, and meeting with teammates for collaboration.

Required Tools & Course Resources

Type Item Description

Required Tools

Laptop

Used for project work, meetings, and presentations.

Microsoft Office Suite

Free for all Purdue students.

Microsoft Teams

Install and log in with your Purdue account. You will be added to your Corporate Partner MS Team.

Project-Specific Access

Depending on your Corporate Partner, you may need access to:
- Company platforms or environments
- Purdue compute resources: Anvil

Course Resources

Course Material

Corporate Partners.

Assignment Submission

Gradescope.

Grades

Brightspace.

Team & Communication

Team

The Data Mine Staff

The data scientist team has a varied background in topics such as natural language processing (NLP), geospatial information systems (GIS), high performance computing, and machine learning. When scheduling a meeting with a data scientist keep in mind that they are designed to be collaborative. The team wants to see any solutions that you’ve attempted and where you may be getting stuck. Also, for more complicated questions it helps to give some advance notice of the topics over email. We aren’t experts in all of data science and some research may be required.

Name Title

Kevin Amstutz

Senior Data Scientist

Ashley Arroyo

Data Science Techincal Specialist

Donald Barnes

Guest Relations Administrator

Maggie Betz

Managing Director of The Data Mine at Indianapolis

Kimmie Casale

ASL Tutor

Bryce Castle

Corporate Partners Technical Specialist

Cai Chen

Corporate Partners Technical Specialist

Doug Crabill

Senior Data Scientist

Peter Dragnev

Corporate Partners Technical Specialist

Stacey Dunderman

Program Administration Specialist

Dan Hirleman

Regional Director of The Data Mine of the Rockies

Jessica Jud

Interim Director of Partnerships

Kali Lacy

Associate Research Engineer

Nicholas Lenfestey

Corporate Partners Technical Specialist

Naomi Mersinger

ASL Interpreter / Strategic Initiatives Coordinator

Kim Rechkemmer

Senior Program Administration Specialist

Katie Sanders

Chief Operating Officer

Betsy Satchell

Senior Administrative Assistant

Shakir Syed

Managing Director of Corporate Partnerships

Dr. Fulya Gökalp Yavuz

Director of Data Science

Dr. Mark Daniel Ward

Executive Director

Corporate Partner Mentors

Your Corporate Partner Mentor(s) are employees of the company you are working with. You will meet once a week with them online (unless the company is local or visiting Purdue for a meeting). Please treat all communication with them in a professional manner – they are like your supervisor during an internship. They also act as a product owner and can help to answer questions or find resources for any product specific questions that you have during the project.

Corporate Partner Teaching Assistants (TAs)

Our Corporate Partner TAs serve as a team leaders for each of our projects. Nearly all of our CRP TAs have been in The Data Mine Corporate Partners program in past years and many are returning to the same project. They also act as a scrum master and are the first person to go to when you have a technical question or questions about your project..

Communication

Communication Guidance

Who How What

Data Science Team

Purdue Email: Submit a ticket
Non-Purdue Email: Send an email

Helps students with the technical topics and concepts they will encounter during their projects.

Corporate Partner Mentors

Check with your TA

Provide domain knowledge about the company, clarify project requirements, and help you find resources or answers to project-specific questions.

Corporate Partner Teaching Assistants

TA email; MSTeams; during meetings

First point of contact for technical questions or general questions about your project.

Assignments and Grades

Assignments

In this course, assignments are split into Professional Development (PD) and the Sprint Report. Each sprint contains assignments from both categories, but the due dates alternate: one Wednesday EOD is for the PD assignment, and the next Wednesday EOD is for the Sprint Report. This repeating schedule keeps the workload balanced and predictable for all students. All of these assignments together make up 70% of the final grade.

Sprint Assignment What: Section 1 What: Section 2 What: Section 3

1

PD

Resume Update (40 points)

Meeting Expectations (25 points)

Time Management (15 points)

Sprint Report

Self Evaluation (80)
- Presence
- Contributions ×2
- Collaboration

n/a

Reflection (20) (2 out 3)
- TOOLS
- AGILE
- Feedback

2

PD

Elevator Pitch (40 points)

Psychological Safety + Norm (30 points)

Personal Branding (30 points)

Sprint Report

Self Evaluation (80)
- Presence
- Contributions ×2
- Collaboration

Implementation Check (PD1) (10):
Participation in Mentor Meetings

Reflection (10) (1 out 2)
- AGILE
- Feedback

3

PD

Mock Interview (45 points)

Documentation (30 points)

Conflict Resolution (25 points)

Sprint Report

Self Evaluation (80)
- Presence
- Contributions ×1
- Collaboration

Implementation Check (PD2) (10):
Practiced Team Contract Norm

Mid-Semester Checkpoint (20)

4

PD

How to Give a Strong Presentation (40 points)

Data Storytelling (30 points)

Addressing Different Audiences (30 points)

Sprint Report

Self Evaluation (80)
- Presence
- Contributions ×2
- Collaboration

Implementation Check (PD3) (10):
Practice Conflict Resolution

Reflection (10) (1 out 3)
- AGILE
- Personal Growth
- Feedback

5

PD

Networking (45 points)

Tuckman’s Review (40 points)

Team Values / Contract (15 points)

Sprint Report

Self Evaluation (80)
- Presence
- Contributions ×2
- Collaboration

Implementation Check (PD4) + Sprint 5 Lab 2 (10):
Applied Presentation Strategies during lab

Reflection (10) (1 out 3)
- Storytelling
- Team dynamics
- Feedback

6

PD

Giving Feedback

Initiative

Presentation Reflection

Sprint Report

Self Evaluation (80)
- Presence
- Contributions ×2
- Collaboration

Implementation Check (PD5) (10):
Networking
Team Stage Discussion

Reflection:
- Presentation Preparation Draft

7

PD

Review your ACCESS Account

Professional Development Adventure

Sprint Report

Survey

PD Ranking

Late Policy

We do NOT accept late work, unless there are extenuating circumstances (usually in coordination with ODOS).

Extenuating circumstances do NOT include:

  • Having exams near or on the due date

  • Working on other course projects on or near the due date

  • Being sick for a few days on or near the due date

  • Traveling for any reason

  • Forgetting the due date

  • Having technical difficulties (wifi, computer, etc)

It is better to submit a partially done report than nothing at all. Partial credit can be earned for work turned in on time. The electronic submission systems also do not allow for late work.

Grade Expectations

This is a research-type, project-based course, so the majority of your grade for the semester will be determined holistically based on work with Corporate Partners in addition to reports and other assignments per the schedule. Students will receive their own individual grade, but the success of the group will be a component of that individual grade.

It is very important to check your @purdue.edu email, Brightspace, Gradescope, and The Examples Book pages frequently! Please review the schedule. More details for each assignment will be available within the corresponding sprint page.

Due dates are listed in the semester schedule with assignments to be completed on Gradescope.

You will need to complete the tasks detailed on each sprint page. The first sprint is covered here: Sprint 0. Additional tasks specific to your project will be discussed with your CRP Mentor, TA, and team.

During the last week of fall semester in December, there will be a final presentation to showcase the work you have done throughout the semester and what you plan to accomplish in the spring semester. All Corporate Partner students will be required to make a final presentation with their teams and present it to their Corporate Partner leadership team. More details will be forthcoming and posted in The Examples Book.

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

Grade Breakdown

Agile 2-week Sprints

75%

A 1-week orientation sprint worth 5% and seven 2-week sprints each worth 10% of your grade. Click on the pages for each sprint for specific assignments.

Sprint 0

5%

Sprint 1

10%

Sprint 2

10%

Sprint 3

10%

Sprint 4

10%

Sprint 5

10%

Sprint 6

10%

Sprint 7

10%

Corporate Partners Mentor and TA Evaluation

15%

Mid-Semester Evaluation

5%

Final Evaluation (cumulative of entire fall 2025 semester)

10%

Final Presentation

10%

Drafts (practice presentation, draft deliverables)

3%

Final Deliverables & Presentation

7%

TOTAL

100%

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 depending on the course grade distribution at the end of the semester.

  • A: 100.000% – 90.000%

  • B: 89.999% – 80.000%

  • C: 79.999% – 70.000%

  • D: 69.999% – 60.000%

  • F: 59.999% – 0.000%

Project Management and Agile

The Data Mine will be applying Agile project management to all of our Corporate Partner projects. Most of our Corporate Partners use Agile methods at their workplace. Agile allows complex projects to be broken down into small manageable tasks that can be assigned to individuals or teams. Agile also has built-in processes that help to enable team communication and collaboration.

Many corporations utilize Agile in environments from software development to data science. While the specifics of each Agile practice may vary by corporation it is beneficial to understand the high-level architecture of the Agile practices and how they can be beneficial in a team development environment. Agile implementation specifics may differ by team. However, each team will be working toward the same goals focused on the breakdown and accomplishment of work tasks and the constant open collaboration between team members.

To become more familiar with Agile methodologies you will complete online training and interactive team training focused on Agile. You will also take a quiz on applying Agile to The Data Mine. Since The Data Mine Corporate Partners is a learning environment (and not your typical 8 AM - 5 PM workplace), we have modified some of the practice to best suit the student schedule.

The MS Teams Planner (or other Agile software) application will also be available to teams for task tracking. The Data Mine staff will provide resources on the use of MS Teams Planner and how it related to the Agile concepts in the materials above. The tool that the team utilizes for Agile task tracking can be determined on a project-by-project basis between the students and the Corporate Partner Mentor or TA.

Learning Outcomes

By the end of this course, you will:

  1. Engage with data-driven projects in industry by following all or some of the seven data science lifecycle steps

    1. Business understanding

    2. Data collection

    3. Data cleaning and preparation

    4. Exploratory Data Analysis (EDA)

    5. Data visualization

    6. Solution deployment

    7. Support and documentation

  2. Participate as a member of the development team on a project following Agile project management methodologies under guidance of the scrum master (TA) and product owner (Corporate Partner Mentor).

    1. Participate in Agile ceremonies such as sprint planning, sprint reviews, and sprint retrospectives

    2. Complete assigned tasks during the 2-week sprints

  3. Collaborate with peers to solve complex data science challenges.

    1. Respond to communications in a timely manner (within 2 business days)

    2. Attend and actively engage in the weekly team meeting (50 minutes) and lab (1 hour 50 minutes).

    3. Uphold values written in team contract

  4. Document and present technical research and project outcomes to a variety of stakeholders ranging from subject matter experts to non-technical colleagues.

    1. Maintain thorough documentation of project successes, setbacks, and all technical materials throughout the project’s duration.

    2. Present on updates during weekly team meetings and labs

    3. Present a mid-year update to broader corporate partner team in December and final poster and video to the public in April.

  5. Explore and evaluate professional development pathways in data science.

    1. Complete a career readiness plan including skill gaps and action steps

      1. Resume

      2. Personal branding

      3. Networking

    2. Attend events that host industry speakers on campus

Course Policies

This course permits you, the student to participate in a class project that has been sponsored by a third party other than the University. The University encourages and supports your participation in this practical learning experience. Although your course requirements may include a practical learning project, you are not required to participate in a project that is sponsored by an outside third party. Prior to your participation in a project sponsored by an outside third party, we would like you to carefully consider that your participation (i) may require you to assign your intellectual property (IP) rights to any intellectual property for which a student would retain ownership under the University’s Policy I.A.1 on Intellectual Property and/or (ii) may require you sign a non-disclosure (confidentiality) agreement with the sponsor. If you sign an agreement regarding intellectual property rights or a non-disclosure agreement, you may incur personal liability (with respect to breach of a non- disclosure agreement) or you may lose economic benefits associated with your ownership of intellectual property (with respect to a license or assignment of intellectual property). You are encouraged to retain independent legal counsel for advice on these types of agreements. In addition, if you choose not to sign a non-disclosure or intellectual property rights agreement, you may be reassigned to a different project or you may not be able to participate in The Data Mine Corporate Partners.

Confidentiality of The Data Mine Corporate Partner Projects

It is important to note that you are working on real-world problems that your Corporate Partner is trying to solve. These projects weren’t created as busywork to keep you occupied for 9 months; you have the opportunity to make a real impact with your Corporate Partner. Past work from Data Mine students has been put into production code!

With that being said, the work you do and the data you have access to must be kept fully confidential! Nearly all Corporate Partner students will be required to sign an NDA and/or IP agreement with the company. Even if you do not have to sign an NDA for your project, please keep the project details private. While each NDA will have unique terms, some basics include:

  • Do not move or copy the data from the original storage. Never email data, text it to your teammates, copy it to MS Teams, or put it in Google drive (or any other cloud storage system). For example, if the data lives on Anvil, do not move it off Anvil and do not move it to a different folder. including your home directory.

  • Do not share any screenshots of the data or any findings (graphs, pictures, etc.) from the project with those who are not on your team.

  • You cannot share things you learn from the data with anyone who is not working on the project. This includes your roommate, your parents, and your best friend.

  • Do not disclose project specifics to anyone, including:

    • In an interview for an internship or job

    • On your LinkedIn profile

    • Your family/friends/roommate/boyfriend/girlfriend/professor

  • Do not discuss the details of projects when you are in a public space. You should find a private place to join the weekly online team meetings. Also, be careful working on the project in a public space when others could walk by and see your screen.

  • If you ever have questions about what you can talk about, always ask your Corporate Partner Mentor first. If you’re ever in doubt about what to share it’s often best to not share initially and check with your corporate partner. They can help clarify any confusion.

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.

Disclosure Requirement for Students

The usage of generative AI must always be documented and disclosed in your submission. 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.

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 an assessment or exam setting, unless explicitly allowed.

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."

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.

Data Mine AI Approval Process

  1. If the company’s stance on AI is not discussed in the project charter, the TA should reach out to the company project mentor and get written approval for the use of generative AI tools in the project.

  2. The approval email should then be forwarded to [email protected] for documentation.

    • The email subject line should read Generative AI Approval - <team name>. With the "team name" replace with your group’s name.

Attendance Policy

This course follows Purdue University Academic Regulations regarding class attendance, which states that students are expected to be present for every meeting of the classes in which they are enrolled. For the purposes of this course, being “present” means attending all face-to-face meetings and all online meetings, unless you are ill or need to be absent for reasons excused by University regulations: grief/bereavement, military service, jury duty, parenting leave or or emergent medical care. Attendance will be taken at the beginning of each class and lateness will be noted.

Regardless if your absence is planned or unplanned, excused or unexcused, please notify your TA as soon as possible and work with them to catch up on missed information and work.

Excused Absences

The Office of the Dean of Students is able to verify and provide notifications for absences that meet the criteria of the excused absence policies established by University Senate.

The University Senate recognizes the following as types of absences that must be excused:

  • Grief Absence Policy for Students

  • Jury Duty Policy for Students

  • Medical Excused Absence Policy for Students

  • Military Absence Policy for Students

  • Parenting Leave Policy for Students- Facilitated by the Office of Institutional Equity

Students needing an absence notification sent for one of the above-listed excused absence policies should complete the corresponding request form.

Unexcused Absences

  1. What if the absence does not meet the criteria of one of the excused absence policies? (link)

    1. "Absences outside of those covered by the University’s excused class absence policies are at the discretion of the individual course instructors. Students should work with their instructors directly to discuss their absence and the opportunity to complete missed coursework. The Office of the Dean of Students cannot to verify or provide notification for an absence outside of the excused class absence policies."

  2. What should you do if it does not meet the criteria for an excused absence?

    1. Do not come to class if you are feeling ill, but DO email/message your TA immediately. They do not need details about your symptoms; simply let them know you are feeling ill and cannot come to class. If it is an emergency situation, please follow the University regulations on emergent medical care (see above).

    2. Unless it falls under the University excused absence regulations (see above), any work due should be submitted on time.

Most absences not excused by ODOS will not be excused by The Data Mine. However, if you believe you have an extenuating circumstance, please notify us by submitting a ticket (or emailing [email protected] if you cannot submit a ticket.)

Class Behavior

You are expected to behave in a way that promotes a welcoming, inclusive, productive learning environment. You need to be prepared for your individual and group work each week, and you need to include everybody in your group in any discussions. Respond promptly to all communications and show up for any appointments that are scheduled. If your group is having trouble working well together, try hard to talk through the difficulties—this is an important skill to have for future professional experiences. If you are still having difficulties, ask The Data Mine staff to meet with your group. Visit the Student Code of Conduct page to understand expectations on “Net-etiquette,” dress-code, in-person meetings, meal etiquette, work expectations, networking expectations, written communication, and time management.

Adding The Data Mine to your Resume

Please see the Professional Development section to learn how to add The Data Mine to your resume.

Disclaimer

This syllabus is subject to change. Changes will be made by an announcement via email and the corresponding course content will be updated.