WQ | Applied Data Science Lab

Domain Overview

WQ (WorldQuant University) domain implements agentic cockpits for automating the Applied Data Science Lab curriculum. A unified agent spans all 7 projects, enabling cross-project analysis and educational integration.

7 Projects

Real estate, air quality, earthquake damage, bankruptcy, clustering, hypothesis testing, and GARCH modeling.

41 Tools

Progressive tool sets from 23 core tools to 41 with advanced diagnostics.

22 Chapters

Integrated textbook reference covering Python, Pandas, ML, and time series.

Project Domains

Proj2: Real Estate

Buenos Aires & Mexico City price prediction with feature engineering.

Linear Regression CSV

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Proj3: Air Quality

Nairobi PM2.5 forecasting with time series and AR models.

ARMA MongoDB

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Proj4: Earthquake

Nepal building damage classification with decision trees.

Decision Tree SQLite

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Proj5: Bankruptcy

Taiwan company bankruptcy detection with ensemble methods.

Random Forest JSON

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Proj6: Consumer

SCF consumer finance clustering with K-Means and PCA.

K-Means CSV

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Proj7: Applicants

DS Lab applicant analysis with Chi-Square and A/B testing.

Chi-Square ETL

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Proj8: Volatility

MTN stock volatility forecasting with GARCH and API integration.

GARCH API

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Agentic Flow

IDLE -> PLANNING -> AWAITING_APPROVAL -> RUNNING -> COMPLETED

PlanBuilder generates step sequences from natural language queries, the Blueprint Panel surfaces plans for approval, and the Agentic Engine executes with full traceability.

Quick Start Queries

"Run lesson 2.1 on price prediction" "Apply GARCH from project 8 to air quality data" "Compare random forest vs gradient boosting on bankruptcy" "Explain logistic regression and show example" "What's the best model for this classification problem?"