Data Academy Honors - Ranks

Data Academy Honors - Ranks#

Four ranks representing progressive mastery in data science for chemical engineering. Each rank builds on the previous, culminating in Senior Data Scientist status.

Rank Progression#

Data Apprentice → Data Analyst → Data Scientist → Senior Data Scientist
    (Week 4)        (Week 8)       (Week 12)          (Week 16)

Rank 1: Data Apprentice#

“Beginning the journey into data-driven discovery.”

Symbol: 🔍 (Magnifying Glass)

Timeline: Achievable by Week 4

Requirements#

Requirement

Details

Badges

1 badge (typically Data Wrangler)

Quizzes

Passing average on lectures 01-03

Capstone

Reflection essay

Capstone Activity#

Write a 1-page reflection: “How Data Science Changed My View of Chemical Engineering Problems”

Prompts to address:

  • What surprised you about working with real data?

  • How might data science apply to problems in your research or interests?

  • What skills do you most want to develop further?

Recognition#

  • Digital badge for portfolio

  • Listed in course recognition page

Rank 2: Data Analyst#

“Demonstrating foundational competence in data analysis.”

Symbol: 📊 (Bar Chart)

Timeline: Achievable by Week 8

Requirements#

Requirement

Details

Badges

2 badges

Midterm

Score 70% or higher

Capstone

Analysis presentation OR peer teaching

Capstone Activity (choose one)#

Option A: Analysis Presentation#

Present a 5-minute analysis of a chemical engineering dataset:

  • Explain your data exploration process

  • Show key visualizations

  • Discuss insights and limitations

Option B: Peer Teaching Session#

Create and lead a 15-minute tutorial on a course concept:

  • Topics: Missing data, PCA interpretation, cross-validation, etc.

  • Include hands-on exercise for peers

  • Demonstrate deep understanding through teaching

Recognition#

  • Digital badge for portfolio

  • Peer teaching opportunities

  • Study group leadership role

Rank 3: Data Scientist#

“Working professional level competency in ML/data science.”

Symbol: 🎯 (Target)

Timeline: Achievable by Week 12

Requirements#

Requirement

Details

Badges

4 badges

Performance

Maintained 75%+ quiz average

Field Experience

Complete one real-world experience

Capstone

Full modeling project

Field Experience Options (choose one)#

Experience

Description

Documentation

Research Collaboration

Analyze data from a campus research group

Summary of contribution

Industry Connection

Interview a data scientist at a company

1-page interview summary

Competition Participation

Participate in a Kaggle competition

Submission evidence and reflection

Professional Development

Attend a data science meetup or webinar

Summary and key takeaways

Capstone Activity#

Complete a Full Modeling Project:

  • Choose a chemical engineering prediction problem

  • Apply the complete data science workflow

  • Include proper validation and uncertainty

  • Document all decisions and trade-offs

Deliverables:

  1. Jupyter notebook with full analysis

  2. 2-page summary report

  3. Brief presentation (5-10 minutes)

Recognition#

  • Digital badge for portfolio

  • Potential TA or peer mentor role

  • Project may be featured in course materials

Rank 4: Senior Data Scientist#

“Full mastery of data science for chemical engineering.”

Symbol: 🏆 (Trophy)

Timeline: Achievable by Week 16

Requirements#

Requirement

Details

Badges

All 5 badges

Final Exam

Score 70% or higher

Project

Complete semester project with distinction

Capstone

Presentation with uncertainty quantification

Capstone Activity#

Presentation with Full Uncertainty Quantification:

Present your semester project (10-15 minutes) demonstrating:

  1. Clear Problem Statement

    • Why this problem matters

    • What decisions depend on the answer

  2. Complete Methodology

    • Data sources and quality assessment

    • Model selection justification

    • Validation strategy

  3. Results with Uncertainty

    • Predictions with confidence intervals

    • Sensitivity to assumptions

    • Known limitations

  4. Interpretability

    • What drives the predictions?

    • SHAP or similar analysis

    • Physical/chemical interpretation

  5. Recommendations

    • What should stakeholders do with these results?

    • When should the model be trusted vs. questioned?

Recognition#

  • Digital badge for portfolio

  • Certificate of Distinction

  • Project archived in course materials

  • Strong recommendation letter support

Rank Summary#

Rank

Badges

Exam

Capstone

Week

Data Apprentice 🔍

1

-

Reflection

4

Data Analyst 📊

2

Midterm 70%+

Presentation OR Teaching

8

Data Scientist 🎯

4

75%+ avg

Modeling Project + Field Exp

12

Senior Data Scientist 🏆

5

Final 70%+

UQ Presentation

16

Timeline at a Glance#

Week 1-4:   Lectures 01-03  →  Data Wrangler badge  →  Data Apprentice
Week 5-8:   Lectures 04-07  →  Pattern Seeker, Model Builder  →  Data Analyst
Week 9-12:  Lectures 08-09  →  Ensemble Master  →  Data Scientist
Week 13-16: Lectures 10-11  →  Uncertainty Expert  →  Senior Data Scientist

Notes for Success#

Start Early#

Don’t wait until the last week to attempt badge activities. The quizzes and activities build on each other.

Document Everything#

Keep a running notebook of your analyses. This becomes your portfolio and makes capstones easier.

Ask for Help#

Characters like Query Quinn remind us: asking good questions is a sign of strength, not weakness.

Connect to Your Research#

The most meaningful projects connect data science to problems you care about in chemical engineering.

“The rank is not the goal—the skills are. The rank is just recognition of what you’ve learned to do.”

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