北京雁栖湖应用数学研究院

In the modern era of "Big Data" economics, the primary bottleneck to groundbreaking research is no longer a lack of data, but the engineering friction required to process, structure, and govern it. While traditional econometrics focuses on analysis, this course focuses on the construction—the systemic plumbing that allows an economist to capture value from complex information environments.

This course is designed to transform students into professional-grade Data Engineers by moving beyond "tool panic" and syntax-deep learning. Instead of merely teaching a programming language, we cultivate architectural thinking: the ability to design scalable, reproducible data pipelines that remain robust across evolving technological stacks.

Participants will gain four core competencies:

Systemic Mastery: Transition from a "tool user" to a "system architect," gaining the mental models to master any new data language or framework independently.

Professional-Grade Workflow: Adopt industry-standard engineering practices (Version Control, ETL/ELT orchestration, and Data Modeling) to eliminate technical debt in research.

Lab-Hardened Insights: Step inside a high-output Economics Lab to see exactly how data is sourced, constructed, and cleaned for top-tier publications—effectively bypassing the "data entry barrier."

Ready-to-Teach Templates: Every student departs with a comprehensive library of modular code and instructional materials, immediately deployable for their own teaching or future research projects.

By the conclusion of this course, you will no longer be limited by the shape of your data. You will possess the engineering sovereignty to build the foundations of your own scientific discoveries.