TDM 30100: Project 14 — 2025

Motivation: We covered a lot this semester, including phython scripts, regression, classifiers, image segmentation, decision trees, bagging, boosting and time series. 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: Reflections on Data Science learning

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.

For this project, using AI tools is NOT allowed. It is forbidden on this project. We want you to use your own words. (It does not matter if you make grammar mistakes.)

Please provide answers from your heart/mind/feelings without using any external resource. It is important for us to hear the words from YOU.

Questions

Question 1 (2 pts)

We would love to have a couple of paragraphs about positive aspects of your Data Mine experience.

Deliverables

What do you value most about The Data Mine? It is OK to give us feedback about technical content, tools, methodologies, hands-on learning, the teamwork and learning community nature of working together, the preparation for internships and careers, learning best practices related to artificial intelligence (AI), machine learning (ML), and data science (DS), etc. Any positive feedback that you have is welcome.

Question 2 (2 pts)

We are open to any comments about things that you would change about The Data Mine experience.

Deliverables

What would you change about The Data Mine? We genuinely care about student feedback and we are listening to you as we strategize for 2026 and for more potential alignment with the College of Science curriculum and (if you aren’t in CoS) also for the Data Science Certificate. Even negative/critical feedback is welcome!

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?

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)

Any additional feedback for the faculty or staff?

Deliverables

If you have additional feedback and insights for the Purdue faculty, staff, and administration, we would really appreciate your insights. All feedback is welcome!

Question 5 (2 pts)

Please identify 3 skills or topics related to ML, classifiers, regression, neural networks, etc., or 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

  • A markdown cell containing 3 skills/topics that you think we should’ve covered in the projects, and an example of why you believe these topics or skills could be relevant and beneficial to students going through the course.

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 2025 and beyond.

We hope you enjoyed the class, and we look forward to seeing you next semester!

Items to submit

  • firstname_lastname_project14.ipynb

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 here 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.