TDM 40200: Project 14 — Spring 2026

Motivation: We covered a lot this semester! From CNNs, containers, APIs, MLOps, signal processing, statistical methodologies, and geospatial tools, etc, we hope that you have had the opportunity to learn a lot, and to improve your data science skills. For our final project of the semester, we want to provide you with the opportunity to give us your feedback on how we connected different concepts, built up skills, and incorporated real-world data throughout the semester, along with showcasing the skills you learned throughout the past 13 projects!

Context: This last project asks students to reflect on your work from the semester.

Scope: Data Science Tools

Learning Objectives:

  • Reflect on the semester’s content as a whole

  • Offer your thoughts on how the class could be improved in the future

Please use the regular project template to submit your feedback in Gradescope, as usual.

Questions

Question 1 (2 pts)

We would appreciate a short reflection (1-2 paragraphs) on the positive aspects of your experience in The Data Mine.

Deliverables

What have you found most valuable about The Data Mine? You may consider topics such as:

  • technical content (DS concepts, tools, methodologies)

  • hands-on/project-based learning

  • teamwork and learning community

  • preparation for internships or careers

  • development of best practices

Please share specific examples when possible.

Question 2 (2 pts)

We welcome your feedback on how The Data Mine experience could be improved.

Deliverables

What changes would you suggest for The Data Mine? You may consider areas such as:

  • course structure or organization

  • technical content and tools

  • project design or expectations

  • workload and pacing

  • alignment with your major, the College of Science curriculum, or the Data Science Certificate

Both constructive and critical feedback are encouraged. Please share specific suggestions or examples when possible.

Question 3 (2 pts)

How does The Data Mine fill some gaps in your Purdue experience? In what ways would you miss The Data Mine if it was not available?

Please reflect on how your Purdue experience would differ with and without The Data Mine.

Deliverables

Please contrast your Purdue experience with-and-without The Data Mine, in other words, help us understand what gaps that The Data Mine fills in your Purdue curriculum. This will help us to better understand how The Data Mine fits into your learning and professional development.

What would you miss the most, if The Data Mine had not been available?

Question 4 (2 pts)

Please identify 3 skills or topics related to the specific tools or to data science (in general) that you wish we had covered in our projects. For each, please provide an example that illustrates your interests, and the reason that you think they would be beneficial.

Deliverables

  • Please describe 3 skills/topics that you think we should have covered in the projects, and an example (for each skill/topic) of why you believe these topics or skills could be relevant and beneficial to students going through the course.

Question 5 (2 pts)

If students utilize generative AI, it is possible to go faster in development and analysis, and also to make a (potentially) more comprehensive analysis, in less time. On the other hand, there are tradeoffs, such as hallucinations, the risk of releasing confidential data, mistakes in the analysis, etc. Nonetheless, generative AI is here to stay. We welcome your feedback about how we could/should design projects for the 2026-27 academic year in such a way that students can leverage their knowledge of generative AI and still document the ways in which they are creating solutions to projects.

Deliverables

  • Please provide insights about how we may more effectively integrate usage of generative AI into Data Mine projects in the future. We value students' perspective on this issue!

OPTIONAL but encouraged:

Please connect with Dr Ward on LinkedIn: www.linkedin.com/in/mdw333/

and also please follow our Data Mine LinkedIn page: www.linkedin.com/company/purduedatamine/

and join our Data Mine alumni page: www.linkedin.com/groups/14550101/

Find us, @purduedatamine, on Instagram, Twitter and Facebook!!

Submitting your Work

If there are any final thoughts you have on the course as a whole, be it logistics, technical difficulties, or nuances of course structuring and content that we haven’t yet given you the opportunity to voice, now is the time. We truly welcome your feedback! Feel free to add as much discussion as necessary to your project, letting us know how we succeeded, where we failed, and what we can do to make this experience better for all our students and partners in 2026 and beyond.

We hope you enjoyed the class!

Items to submit

  • firstname_lastname_project14.ipynb

It is necessary to document your work, with comments about each solution. All of your work needs to be your own work, with citations to any source that you used. Please make sure that your work is your own work, and that any outside sources (people, internet pages, generative AI, etc.) are cited properly in the project template.

You must double check your .ipynb after submitting it in gradescope. A very common mistake is to assume that your .ipynb file has been rendered properly and contains your code, markdown, and code output even though it may not.

Please take the time to double check your work. See submission page for instructions on how to double check this.

You will not receive full credit if your .ipynb file does not contain all of the information you expect it to, or if it does not render properly in Gradescope. Please ask a TA if you need help with this.