📅 2025-02-27 — Session: Implemented BigQuery for Census Data Analysis
🕒 14:20–15:00
🏷️ Labels: Bigquery, Census Data, Representation Learning, Autoencoder, Cost Management
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and implement strategies for applying representation learning to socioeconomic data, specifically using autoencoders for income prediction. Additionally, it focused on making census data queryable in BigQuery and managing associated costs.
Key Activities
- Discussed the application of representation learning, particularly autoencoders, to improve income prediction from socioeconomic data.
- Explored potential failure modes in semi-supervised learning for census data.
- Outlined methods for making a census database accessible for querying, including hosting options and open data portals.
- Provided a detailed guide for uploading a Census Database to BigQuery, covering project setup and data upload methods.
- Developed strategies to manage BigQuery query costs, including using the free tier and visualizations.
- Created a step-by-step roadmap for making census data publicly queryable in BigQuery.
- Discussed handling table relationships and foreign key support in BigQuery for performance optimization.
- Provided methods for querying and exporting random samples in BigQuery for ML pipelines.
Achievements
- Established a comprehensive framework for uploading and querying census data in BigQuery.
- Developed cost management strategies for BigQuery usage.
- Enhanced understanding of representation learning applications in socioeconomic data analysis.
Pending Tasks
- Validate the hypothesis of improved income prediction using representation learning.
- Implement and test the proposed experimental steps for autoencoders in socioeconomic data.
- Further explore the handling of table relationships and foreign key simulations in BigQuery.