Eugene Yan on Writing Weekly and His Favorite Project
In a recent spotlight, Eugene Yan shares his writing habits, his favorite project, and what he wants to see more of in data science content.
AI & ML
Frontier models, research results, agents, and developer tooling.
Eugene Yan's 2020 Retrospective: Writing Stats and Visuals
A data-driven look at a year of writing, with graphs and a word cloud.
Chip Huyen on Turning ML Setbacks Into a Writing Practice
In an informal mentor interview, Chip Huyen recounts how career setbacks led her to use writing as a tool for clarity, connection, and growth.
Three AI Job Roles: Applied Scientists, Research Scientists, ML Engineers
Eugene Yan's guide separates the three roles by what they ship, the skills they need, and how teams should use them.
What Data Analytics Is and How Students Can Enter the Field
Eugene Yan's talk for SMU undergraduates lays out what data science involves and how newcomers can break in.
Data science careers: myths, daily reality, and advice from Eugene Yan
A practitioner's reflection separates common myths from the actual work of data science and offers grounded career guidance.
Machine Learning's Seven Life Lessons: From Overfitting to Ensembling
A practitioner's essay maps machine learning concepts like overfitting and ensembling onto everyday decision-making.
Agile Can Work for Data Science Teams, GovTech Panel Says
A panel at GovTech STACK, moderated by Eugene Yan, concluded that data science teams can indeed adopt agile practices.
Lazada’s Data Science VP on Data Use and an Unusual Career Path
Eugene Yan’s INSEAD lunchtime talk pairs Lazada’s data practices with lessons from a nontraditional route to a VP data-science role.
A Practical On-Ramp for Aspiring Data Scientists
Eugene Yan's talk and written guide both stress learning core tools and practicing them through hands-on projects.
From IC to Data Science Lead: What Changes in the First 100 Days
A data science lead's first 100 days reveal a fundamental shift from technical output to enabling the team and aligning with business goals.
Review: OMSCS CS7641 Machine Learning Course and Tips
A practitioner's review of the OMSCS CS7641 machine learning course highlights its value for revisiting fundamentals and learning new techniques.
OMSCS CS6300 Review: Java and Android App Development
A review of Georgia Tech's OMSCS CS6300 course describes its focus on Java and collaborative Android app development, but the content is unrelated to AI or machine learning.
Why Data Science Teams Need Modified Agile Frameworks
Eugene Yan outlines how agile methods can be reshaped for the uncertain, iterative reality of data science.
Predicting Hospitalization Costs at Admission: Lessons from SEA's Largest Hospital Group
Eugene Yan shares how a production machine learning system was built to forecast patient costs in real time, offering a rare look at applied ML in Southeast Asian healthcare.
What Agile Gets Right and Wrong for Data Science
A new essay examines the practical strengths and weaknesses of Agile methods in data science projects, with a follow-up still to come.
Review: OMSCS Machine Learning for Trading Course
A practitioner's review of Georgia Tech's online ML for Trading course highlights its practical value and heavy workload.
Why Data Science Team Culture Outranks Process and Hierarchy
A strong team culture, not rigid structure, is the key lever for data science success, argues Eugene Yan.