Data Academy Honors

Data Academy Honors#

A recognition program for demonstrating mastery of data science and machine learning concepts. Complete badges to earn ranks and show your expertise as a Data Detective.

Program Overview#

Mission: Recognize students who go beyond the basics to demonstrate true understanding of data science concepts and their application to chemical engineering problems.

Timeline: Designed to be achievable within one semester

Philosophy: Like a detective earning credentials, each badge and rank represents demonstrated skill in investigating data, building models, and communicating findings responsibly.

Quick Start#

  1. Complete Lectures & Quizzes - Work through course material

  2. Earn Badges - Demonstrate mastery in each area (see Badges)

  3. Achieve Ranks - Combine badges with capstone activities (see Ranks)

  4. Track Progress - Use the tracking system to monitor your journey

The Five Required Badges#

Badge

Icon

Focus

Lectures

Data Wrangler

🔍

Data manipulation & exploration

01-03

Pattern Seeker

🗜️

Dimensionality reduction & visualization

04

Model Builder

🎯

Regression & model selection

05-07

Ensemble Master

🌲

Ensemble methods & clustering

08-09

Uncertainty Expert

📏

UQ & interpretability

10-11

The Four Ranks#

Rank

Requirements

Meaning

Data Apprentice

1 badge

Beginning the journey

Data Analyst

2 badges + midterm

Foundational competence

Data Scientist

4 badges + field experience

Working professional level

Senior Data Scientist

All 5 badges + final + project

Full mastery

How It Works#

Earning Badges#

Each badge requires:

  1. Quiz Performance: Average 75%+ on section quizzes

  2. Assignments: Complete coding assignments

  3. Badge Activity: One hands-on activity demonstrating the skill

Advancing Ranks#

Each rank requires:

  1. Badge Count: Accumulate required badges

  2. Exam Performance: Meet exam thresholds

  3. Capstone Activity: Rank-specific challenge

Recognition#

Completed achievements are recognized through:

  • Digital Badges: Shareable credentials

  • Course Records: Official documentation

  • Portfolio Building: Each badge adds to your data science portfolio

Files in This Directory#

  • README.md - This overview

  • badges.md - Detailed badge requirements and activities

  • ranks.md - Rank progression and capstone activities

  • tracking.json - Progress tracking schema

Character Guides#

Each badge is championed by course characters:

Badge

Champions

Data Wrangler

Nadia Null, Practice Panda

Pattern Seeker

Dee Dimension, Viz Vizzy

Model Builder

Reggie Regression, Val Validation

Ensemble Master

Forrest Random, Clara Cluster

Uncertainty Expert

Quinn Quantify, SHAP Shapley

See characters.md for the full cast!

“Data doesn’t lie, but it can be misunderstood. A good detective lets the data speak while questioning assumptions.”

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