TDM 30100: Project 14 — 2024

Motivation: We covered a lot this semester, including machine learning, classifiers, regression, and neural networks. 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 will work as a consolidation of everything we’ve learned thus far, and may require you to back-reference your work from earlier in 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

Make sure to read about, and use the template found here, and the important information about project submissions here.

Questions

Question 1 (2 pts)

The Data Mine team is writing a Data Mine book to be (hopefully) published in 2025. We would love to have a couple of paragraphs about your Data Mine experience. What aspects of The Data Mine made the biggest impact on your academic, personal, and/or professional career? Would you recommend The Data Mine to a friend and/or would you recommend The Data Mine to colleagues in industry, and why? You are welcome to cover other topics too! Please also indicate (yes/no) whether it would be OK to publish your comments in our forthcoming Data Mine book in 2025.

Deliverables

Feedback and reflections about The Data Mine that we can potentially publish in a book in 2025.

Question 2 (2 pts)

Reflecting on your experience working with different projects, which one did you find most enjoyable, and why? Illustrate your explanation with an example from one question that you worked on.

Deliverables

  • A markdown cell detailing your favorite project, why, and a working example and question you did involving that project.

Question 3 (2 pts)

While working on the projects, how did you validate the results that your code produced? Are there better ways that you would suggest for future students (and for our team too)? Please illustrate your approach using an example from one problem that you addressed this semester.

Deliverables

  • A few sentences in a markdown cell on how you conducted your work, and a relevant working example.

Question 4 (2 pts)

Reflecting on the projects that you completed, which question(s) did you feel were most confusing, and how could they be made clearer? Please cite specific questions and explain both how they confused you and how you would recommend improving them.

Deliverables

  • A few sentences in a markdown cell on which questions from projects you found confusing, and how they could be written better/more clearly, along with specific examples.

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/

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.