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# 哥伦比亚大学数据科学硕士 Personal Statement（草稿，约700词）

Personal Statement — MS in Data Science, Columbia University

开篇：兴趣起源

My path to data science began with a proof, not a prediction. As a mathematics undergraduate, I was drawn to the elegance of linear algebra and probability theory—until I took a course in statistical learning and realized these abstractions could power real-world decisions. That revelation transformed my curiosity into a commitment: I want to build machine learning systems that solve consequential problems. Columbia's MS in Data Science is the natural next step in that journey.

学术准备：数学专业基础

My mathematics training has given me a rigorous foundation for data science. Maintaining a GPA of 3.6, I completed advanced coursework in real analysis, linear algebra, probability, and numerical methods. More importantly, I learned to think mathematically—to understand *why* an algorithm converges, not merely *how* to call it. When I later encountered gradient descent and regularization in practice, I recognized the optimization theory behind them. This depth allows me to diagnose models rather than simply train them, a distinction I believe separates practitioners who scale solutions from those who ship fragile ones.

实践经历：两段机器学习实习

Internships converted my theoretical grounding into practical capability.

第一段实习（机器学习实习生）：In my first machine learning internship, I worked on a predictive modeling pipeline for tabular business data. I handled feature engineering, trained and compared tree-based models, and learned an enduring lesson: real datasets are messy