Track 07 · 18 projects · ~6 months part-time
Data Science.
Data Science
Data science is using data to answer questions and inform decisions. Not just "what happened" but "why did it happen, will it happen again, and what should we do about it?" The work spans exploratory analysis, statistical modeling, prediction, causal inference, and communicating findings to people who need to act on them.
§ Projects18 PROJECTS · P.01 → P.18
P.01
Descriptive Analysis and Verification
CLIENT · WANJIKU MUTHONI
7 units
→P.02—
First Prediction Model
CLIENT · WANJIKU MUTHONI
6 units
→P.03—
Multi-Source Analysis and Inferential Depth
CLIENT · SOMCHAI RATTANAPONG
7 units
→P.04—
Classification and Imbalanced Evaluation
CLIENT · LUCIANA MORETTI
6 units
→P.05—
Method-Driven Preparation and Leakage Prevention
CLIENT · EUNJI CHO
6 units
→P.06—
Question Typology Ownership
CLIENT · HASSAN EL-AMIN
7 units
→P.07—
AI Infrastructure Introduction
CLIENT · ASTRID LINDQVIST
7 units
→P.08—
First MCP Connection: DuckDB Integration
CLIENT · BUDI HARTONO
6 units
→P.09—
Causal Inference: Loyalty Program Evaluation
CLIENT · GABRIELA DOMINGUEZ
7 units
→P.10—
Causal Validation: Sensitivity, Delegation, and Cross-Tool Verification
CLIENT · GABRIELA DOMINGUEZ
7 units
→P.11—
Time Series Forecasting: Seasonal Patterns and Prediction Intervals
CLIENT · CARLOS FERREIRA
7 units
→P.12—
Multi-Specification Robustness for Prediction
CLIENT · FATIMA AL-MANSOORI
7 units
→P.13—
AI Infrastructure at Scale: Multi-Agent Analytical Work
CLIENT · FATIMA AL-MANSOORI
7 units
→P.14—
Sensitivity as the Deliverable: Robust-vs-Fragile Reporting
CLIENT · LIAM GALLAGHER
7 units
→P.15—
Whether to Predict at All: Recommendation and Handoff
CLIENT · SARAH OKONKWO
7 units
→P.16—
Multi-Method Synthesis and the Communication Package as Deliverable
CLIENT · MOUSSA DIALLO
7 units
→P.17—
Inheritance Review: Diagnosing a Broken Analysis
CLIENT · RIMA NASSAR
6 units
→P.18—
Capstone: A Multi-Method Engagement End to End
CLIENT · TOM KESSLER
8 units
→—