Podcast
Master Technical Writing: 7-Day Workflow to Accelerate Your Data Science Career
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Master Technical Writing: 7-Day Workflow to Accelerate Your Data Science Career
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Episode Overview
How can technical writing accelerate your data science career in just one week? In this episode, Eugene Yan — an Applied Scientist at Amazon who previously led data science teams at Lazada and uCare.ai and writes about ML in production and career growth — walks through a practical, repeatable 7-day workflow for technical writing tailored to data scientists.
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Chapter Summary
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- 0:00 - Podcast Introduction
- 1:40 - Career Transition: Psychology to Applied Scientist
- 6:00 - First Public Writing: Early Blog Posts and Meetups
- 9:30 - Writing Motivations: Share, Learn, Be a Beacon
- 14:00 - Audience Targeting: Readers, Peers, and Future Teammates
- 16:30 - Writing as Product: Weekly Shipping and UX Mindset
- 20:00 - Weekly Writing Cadence: 7-Day Workflow and Schedule
- 25:00 - Outline-First Method: Memory Rewriting and Idea Filtering
- 29:00 - Time Budget & Editing: 25 Hours/Week and Avoiding Over-Editing
- 33:00 - Idea Sources and Topic Prioritization for Technical Writing
- 37:00 - Title Crafting and Article Length Decisions
- 41:00 - Getting Started: Start Writing, Overcome Friction
- 43:30 - Blogging Tools: Medium, Substack, WordPress, Jekyll (GitHub Pages)
- 46:00 - Writing Habits: Morning Reps and Weekend Deep Work
- 48:30 - Audience Growth: Distribution via Twitter, LinkedIn, Consistency
- 51:00 - Writing at Work: Press Release, Working Backwards, and Design Docs
- 54:00 - Technical Documentation: Decision Logs, Rationales, and Team Memory
- 56:30 - Portfolio Best Practices: Clear README, Quick Start, Repo Tour
- 58:30 - Practical Tips: Iterate Outlines, Ship Weekly, Learn by Teaching