Data Science Experience

Homework

The goal of this assignment is not just to observe data science—but to begin participating in the data science ecosystem as an emerging professional.

The world of data science extends far beyond the classroom. Professional identity, ethical awareness, technical growth, and community engagement are just as important as coding and modeling.

The Data Science Experience (DSE) assignment is designed to:

You will complete one Data Science Experiences this semester due at the end of term.

📌 Must be a unique experience (e.g., don’t copy over one from another of my courses…)

📌 No late submissions are accepted.

📌 You may submit anytime before the deadline.

Global Requirements

You must complete both parts for each DSE:

Acceptable Category Pairings

You must choose:

  • Two different categories among 1, 2, and 3, OR

  • Category 4 alone, OR

  • Category 5 alone

If uncertain, email your Professor before participating.

PART 1 — The Data Science Experience (Field Engagement)

Category 1: Attend a Talk, Conference, or Workshop

Attend a data science–related: * Talk * Panel * Workshop * Conference session * Hackathon onboarding or tutorial

Requirements

  1. Must be ≥ 30 minutes
  2. May be in-person or virtual (cannot be recordings)
  3. Must involve active data science, analytics, AI, or statistics content

Examples

  • University data science seminars
  • Posit::conf, PyData, SciPy talks
  • Government or industry AI briefings
  • Arizona Data Lab workshops
  • Responsible AI / ethics panels

Category 2: Interview or Shadow a Data Scientist

Engage directly with someone who uses data science professionally, such as: * Industry professional * Research scientist * Graduate student * Faculty researcher * Government analyst

Minimum Expectations

  • 30-minute conversation
  • You must prepare at least 8 thoughtful questions
  • Topics should include:
    • Career path
    • Daily workflow
    • Tools used
    • Ethical challenges
    • Advice for students

You may conduct the interview:

  • In person

  • Via Zoom/Teams

  • Phone call

Category 3: Podcast, Documentary, or Long-Form Video

Watch or listen to a serious data science–focused program.

Requirements

  • Must be ≥ 30 minutes
  • Must be substantive and technical, ethical, or industry-focused
  • Entertainment-only content does not qualify

Suggested Sources

This list is not exhaustive. You may listen to other podcasts or watch other data science videos not included on this list. Ask your professor if you are unsure whether a particular podcast or video will count towards the data science experience.

  • For reference, here are some other podcasts.

Category 4: Participate in a real modeling challenge, individually or in a team.

Examples

Category 5: Book Study in Data Science, Statistics, or Ethics

There are a lot of books about statistics, data science, and related topics. A few suggestions are below. If you decide to read a book that isn’t on this list, ask your professor to make sure it counts toward the experience. Many of these books are available through University of Arizona library.

  • The Signal and the Noise: Why so many predictions fail - but some don’t by Nate Silver
  • Weapons of Math Destruction by Cathy O’Neil
  • How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo
  • The Art of Statistics: How to learn from data by David Spiegelhalter
  • List of books about data science ethics

📌 You must read at least one full section, half, or the full book depending on length (defined per book).

PART 2 — Professional Artifact & Reflection

You will submit TWO deliverables for the Data Science Experience:

Deliverable A: Professional One-Slide Summary (PDF)

You must create one professional-quality slide using:

  • Google Slides → Export PDF
  • PowerPoint → Export PDF
  • Quarto RevealJS → Export PDF
  • OR a comparable professional tool

[!Note] Feel free to use this template or use slide.qmd

Your slide MUST include:

  1. Title & Experience Type

    • Event name / Interviewee role / Podcast title / Competition name / Book title

  2. Core Topic Summary

    • What was this experience actually about?

  3. Something New, Surprising, or Challenging

    • A technical concept you didn’t know
    • A career reality you didn’t expect
    • A modeling limitation that surprised you
    • An ethical tension that stood out

  4. Explicit Course Connection
    You MUST reference at least one:

    • Statistical method
    • Model type
    • Visualization principle
    • Data cleaning issue
    • Evaluation metric
    • Ethical framework

    Example: > “This directly connects to our discussion of class imbalance and ROC/AUC trade-offs.”

  5. Credible Citation or Link

    • Event webpage
    • Podcast URL
    • Book reference
    • Competition URL
Important

Slides that are vague, overly generic, or purely descriptive will not receive full credit.

Deliverable B: Written Reflection (600–1,000 words)

Your reflection must be submitted as a Quarto file named:

reflection.qmd

Your reflection MUST address ALL of the following sections:

1. Context & Motivation

  • Why did you choose this experience?
  • What did you expect going in?

2. Technical & Conceptual Learning

  • What did you actually learn about:
    • Data?
    • Models?
    • Tools?
    • Decision-making?
  • What technical content connected most strongly to this course?

3. Professional Insight

  • What did this reveal about:
    • Industry practice?
    • Research timelines?
    • Career paths?
    • Collaboration?
    • Ethics?

4. Critical Reflection

  • What limitations, biases, or tradeoffs were present?
  • What was not addressed that should have been?
  • What would you question, challenge, or investigate further?

5. Forward Connection

  • How will this experience influence:
    • Your future coursework?
    • Your projects?
    • Your career goals?
    • Your understanding of data science as a discipline?
Important

Summaries without analysis will not receive full credit.

PART 3 — Evidence & Verification

To ensure authenticity, you must include at least one form of verification evidence.

Acceptable Evidence by Experience Type:

Experience Type Acceptable Evidence
Talk / Workshop Screenshot of registration, agenda, or attendance
Interview Photo (if permitted), email confirmation, or signed acknowledgment
Podcast / Video Screenshot of playback with visible timestamp
Competition Screenshot of submission page or leaderboard
Book Photo of book + page range OR reading log

Save this as:

ds-experience/
├── slide.pdf
├── reflection.qmd
├── evidence.pdf (or /evidence folder)

Grading Rubric (100 Points)

Category Points
Quality of Experience Selection 20
Slide Content & Clarity 20
Technical & Course Connection 20
Written Reflection Depth 25
Evidence & Professionalism 15
Total 100

Why This Assignment Matters

By the end of the semester, you will have:

  • ✅ One real-world data science engagements
  • ✅ One professional portfolio artifacts
  • ✅ One deep reflections on your technical and ethical growth
  • ✅ A clearer understanding of how data science actually works in practice

Creativity is encouraged. Professionalism is required.