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Merge pull request #76 from DataTalksClub/podcast-improvements-seo
## All podcasts that had transcripts now have This update improves SEO by making each podcast page more informative and relevant to search engines. Updated 190+ podcast markdown files: * **Updated `titles` and timestamps** make content clearer and more structured, helping both users and crawlers understand what each episode covers. * **`Descriptions`** enhance how pages appear in search results, increasing the chance of clicks. * **`Intro` text** adds context and keyword-rich content, improving topical relevance and readability. Added 190+ timestamp files in `podcast-timestamps/` Together, these changes strengthen on-page SEO and help podcast episodes rank better for specific topics and guest names. Addresses many features described here: #61 ## Added schema markup for podcast pages Schema markup helps search engines understand the content of each podcast page more precisely. By providing structured data about videos, audio, and episode details, we make it easier for Google to display rich snippets, video previews, and podcast listings in search results. This improves visibility, click-through rates, and discoverability across platforms like Google Search and Google Podcasts. Elements include: - VideoObject - BreadcrumbList - AudioObject - PodcastEpisode Includes author/guest info, platform links (YouTube, Apple, Spotify, Anchor), and action metadata. These comply with: - https://schema.org/ - https://developers.google.com/search/docs/appearance/structured-data/search-gallery Each individual page can be checked using these validators by either copying and pasting the URL of the page or its source code: - https://validator.schema.org/ - https://developers.google.com/search/docs/appearance/structured-data ## New front matter fields for podcast pages required for schema markup These new front matter fields improve both **structured data accuracy** and **content discoverability** in search results. * **`dateadded`** helps search engines identify when the episode was first published, supporting features like “new podcast” highlights and improving freshness signals for SEO. * **`duration`** provides the exact length of each episode, making podcast previews more informative and eligible for enhanced display formats. * **`quotableClips`** adds structured snippets linking to key moments in the episode, increasing engagement and enabling deep linking to specific topics or quotes. By generating and maintaining these fields automatically through scripts, we ensure all podcast pages are complete, consistent, and optimized for rich results. ### `dateadded` - date when the podcast was uploaded to the website How to add it: Created a script to add the `dateadded` field to each podcast page front matter based on the Git log ### `duration` - a calculated duration of the podcast recording based on the transcript How to add it: Created a script to calculate the duration of the podcast and add it to the frontmatter of each podcast. ### `quotableClips` - a list of structured snippets linking to key moments in the episode, increasing engagement and enabling deep linking to specific topics or quotes How to add: Created a script to add episode clips of the podcast and add it to the frontmatter of each podcast. Each clip in the quotableClips array contains: - name: The timestamp title/description (e.g., "Podcast Introduction: guest Antonis and episode themes") - startOffset: Start time in seconds (e.g., 0, 148) - url: YouTube URL with time parameter (e.g., https://www.youtube.com/watch?v=-Gj7SaI-QW4&t=148) - endOffset: Start time in seconds (e.g., 0, 148)
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‎_layouts/podcast.html‎

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‎_podcast/s01e01-roles.md‎

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---
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title: "Data Team Roles Explained"
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short: "Roles in a Data Team"
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guests: [alexeygrigorev]
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title: 'Data Team Roles Explained: Skills, Responsibilities, and How Teams Ship ML
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Products'
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short: Roles in a Data Team
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guests:
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- alexeygrigorev
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image: images/podcast/s01e01-roles.jpg
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keywords: "data team roles, data scientist, data engineer, machine learning engineer, data analyst, MLOps engineer, product manager, data team structure, data science roles, ML engineer vs data engineer, data team responsibilities, data science career"
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keywords: data team roles, data scientist, data engineer, machine learning engineer,
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data analyst, MLOps engineer, product manager, data team structure, data science
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roles, ML engineer vs data engineer, data team responsibilities, data science career
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season: 1
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episode: 1
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ids:
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anchor: Roles-in-a-data-team---Alexey-Grigorev-emqcft
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links:
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anchor: https://anchor.fm/datatalksclub/episodes/Roles-in-a-data-team---Alexey-Grigorev-emqcft
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spotify: TODO
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apple: TODO
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dateadded: '2021-02-23'
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---
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The topic today is the roles in data teams. We want to understand what kind of people work in the data team, what responsibilities they have, what they do, and what they need to know.

‎_podcast/s01e02-processes.md‎

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title: "CRISP-DM Methodology: Complete Guide to Data Science Project Process"
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short: "Processes in a Data Science Project"
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guests: [alexeygrigorev]
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title: 'CRISP-DM Methodology for Data Science Projects: Business Understanding, Data
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Preparation, Modeling, Evaluation & Deployment'
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short: Processes in a Data Science Project
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guests:
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- alexeygrigorev
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image: images/podcast/s01e02-processes.jpg
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description: "Learn the CRISP-DM methodology for managing data science projects. Step-by-step guide covering business understanding, data preparation, modeling, evaluation, and deployment."
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keywords: "CRISP-DM, data science process, machine learning methodology, data science project management, ML project lifecycle, data science workflow, A/B testing, model deployment, data science best practices, ML model evaluation, cross-functional data teams"
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description: Learn the CRISP-DM methodology for managing data science projects. Step-by-step
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guide covering business understanding, data preparation, modeling, evaluation, and
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deployment.
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keywords: CRISP-DM, data science process, machine learning methodology, data science
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project management, ML project lifecycle, data science workflow, A/B testing, model
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deployment, data science best practices, ML model evaluation, cross-functional data
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teams
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season: 1
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episode: 2
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ids:
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youtube: SesVTDklFYQ
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anchor: Processes-in-a-Data-Science-Project---Alexey-Grigorev-encdlg
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links:
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anchor: https://anchor.fm/datatalksclub/episodes/Processes-in-a-Data-Science-Project---Alexey-Grigorev-encdlg
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dateadded: '2021-02-23'
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---
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The topic today is the processes in a data science project. We want to understand how cross-functional teams work together to ship real value. We'll use a concrete example (auto-categorizing marketplace listings) and walk through CRISP-DM step by step.

‎_podcast/s01e03-building-ds-team.md‎

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‎_podcast/s01e04-standing-out-as-a-data-scientist.md‎

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‎_podcast/s01e05-mentoring.md‎

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title: "How to Find a Mentor and Advance Your Tech Career"
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short: "Mentoring"
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guests: [rahuljain]
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title: 'How to Find a Mentor and Become One: Mentoring Strategies for Tech Careers'
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short: Mentoring
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guests:
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- rahuljain
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image: images/podcast/s01e05-mentoring.jpg
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description: "Learn how to find the right mentor, become an effective mentor yourself, and advance your tech career. Expert advice on overcoming imposter syndrome, cold outreach strategies, and balancing technical work with leadership roles."
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keywords: "mentoring, career development, tech mentorship, finding a mentor, becoming a mentor, imposter syndrome, tech leadership, career advice, professional development, data engineering"
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description: 'Discover practical mentoring strategies for tech careers: find mentors,
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master cold outreach, run effective sessions, start paid mentorship & boost leadership.'
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keywords: mentoring, career development, tech mentorship, finding a mentor, becoming
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a mentor, imposter syndrome, tech leadership, career advice, professional development,
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data engineering
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episode: 5
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anchor: Mentoring---Rahul-Jain-eo7cmu
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intro: 'Struggling to find a mentor — or wondering how to become one — in a fast-moving
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tech career? In this episode, Rahul Jain, a senior solutions engineer and data/AI
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leader with 15+ years driving enterprise data transformations and a career arc from
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mining engineering to data engineering and leadership, walks through practical mentoring
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strategies for tech professionals. We define mentoring (purpose, scope, types),
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explore early models like Thoughtworks’ sponsorship, and show how to find mentors
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through networks, platforms, and cold outreach — with concrete outreach best practices:
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specificity, background, and follow‑up. Rahul covers preparing mentoring sessions
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(goals, agendas), mentoring formats (one‑off advice vs long‑term relationships),
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and how to start as a mentor using simple first steps and platforms. Topics include
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benefits of mentoring, transferable workplace guidance, developing people skills
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(empathy, listening), balancing technical work and leadership, tackling imposter
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syndrome, coaching vs managing, setting boundaries and paid mentorship, and maintaining
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development plans. Listen to gain actionable steps, templates, and mindset shifts
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to both secure meaningful mentorship and build a sustainable mentoring practice
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in your tech career.'
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dateadded: '2021-02-23'
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quotableClips:
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- name: Episode Introduction
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startOffset: 0
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=0
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endOffset: 80
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- name: 'Career Journey: From Mining Engineering to Data Engineering & Leadership'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=80
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endOffset: 370
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- name: 'Defining Mentoring: Purpose, Scope, and Types'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=370
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endOffset: 570
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- name: 'Early Mentoring Experience: Thoughtworks Model and Sponsorship'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=570
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endOffset: 770
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- name: 'Finding a Mentor: Networks, Cold Outreach & Platforms'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=770
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endOffset: 990
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- name: 'Cold Outreach Best Practices: Specificity, Background, and Follow‑up'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=990
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endOffset: 1180
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- name: 'Preparing for Mentoring Sessions: Goals, Expectations, and Agenda'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=1180
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- name: 'Mentoring Formats: One‑Off Advice vs. Long‑Term Relationships'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=1350
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endOffset: 1510
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- name: 'Benefits of Being a Mentor: Listening, Pattern Recognition, and Growth'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=1510
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endOffset: 1680
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- name: 'Mentoring Across Domains: Transferable Workplace Guidance'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=1680
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endOffset: 1840
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- name: 'Developing People Skills: Empathy, Listening, and Avoiding the "Advice Monster"'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=1840
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endOffset: 2010
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- name: 'Balancing Technical Work and Leadership: Blended Career Paths'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=2010
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endOffset: 2200
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- name: 'Common Mentee Challenges: Imposter Syndrome & Tech‑vs‑Management Choices'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=2200
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endOffset: 2390
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- name: 'Coaching vs. Managing: When to Use External Mentors'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=2390
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endOffset: 2550
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- name: 'Setting Boundaries: Expectations, Cadence, and When to Charge'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=2550
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endOffset: 2710
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- name: 'Paid Mentorship: Accountability, Pricing, and Professional Coaching'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=2710
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- name: 'Building Reciprocal Mentoring Relationships: Authenticity & Retrospectives'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=2870
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- name: 'Starting as a Mentor: Practical First Steps and Platforms'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=3020
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endOffset: 3160
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- name: 'Maintaining Development Plans: Review, Visibility, and Follow‑through'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=3160
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- name: 'Managing Overwhelm: Diagnosing Causes and Practical Steps'
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=3290
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endOffset: 3390
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- name: Audience Q&A Highlights
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url: https://www.youtube.com/watch?v=LQvwTNQbPg4&t=3390
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endOffset: 3480
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- name: Closing Thoughts and Resources
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endOffset: 3480
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Today we're discussing mentoring with [Rahul Jain](/people/rahuljain.html), a technical leader with about 20 years of experience building and running software products. He currently leads the Business Intelligence and Data Engineering units at Omio, a ticket-booking company, and mentors engineers and managers through The Mentoring Club.

‎_podcast/s02e01-writing.md‎

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title: "The Importance of Writing in a Tech Career"
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short: "The Importance of Writing in a Tech Career"
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guests: [eugeneyan]
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title: 'Master Technical Writing: 7-Day Workflow to Accelerate Your Data Science Career'
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short: 'Master Technical Writing: 7-Day Workflow to Accelerate Your Data Science Career'
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guests:
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image: images/podcast/s02e01-writing.jpg
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description: "Learn why technical writing is crucial for career growth in data science and ML. Eugene Yan shares his 7-day writing process, tips for documentation, and how writing helped him transition from psychology to Amazon Applied Scientist."
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keywords: "technical writing, data science career, ML engineer writing, documentation skills, technical communication, data science blog, career growth, writing process, Amazon data scientist, Eugene Yan, technical documentation, data science portfolio, ML career advice, technical writing tips, data science writing"
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description: 'Master technical writing for data science with a practical 7-day workflow:
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outline-first cadence, portfolio tips, docs & distribution to accelerate your career.'
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keywords: technical writing, data science career, ML engineer writing, documentation
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skills, technical communication, data science blog, career growth, writing process,
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Amazon data scientist, Eugene Yan, technical documentation, data science portfolio,
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ML career advice, technical writing tips, data science writing
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intro: How can technical writing accelerate your data science career in just one week?
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In this episode, Eugene Yan — an Applied Scientist at Amazon who previously led data
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science teams at Lazada and uCare.ai and writes about ML in production and career
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growth — walks through a practical, repeatable 7-day workflow for technical writing
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tailored to data scientists. <br><br> We cover Eugene’s career transition and first
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public writing, motivations for sharing work, and how to target readers (peers,
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future teammates, and hiring managers). He frames writing as a product with a weekly
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shipping cadence, explains the outline-first method for filtering ideas, and outlines
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a realistic time budget and editing limits. You’ll get concrete guidance on idea
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sourcing, title crafting, article length, blogging tools (Medium, Substack, WordPress,
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Jekyll), writing habits, distribution via Twitter and LinkedIn, and writing at work
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(press releases, design docs, decision logs). Practical portfolio advice — clear
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README, quick start, repo tour — and tips to iterate outlines and ship weekly round
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out the episode. <br><br> Listen to learn a concrete 7-day workflow, documentation
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and portfolio best practices, and distribution tactics to boost your technical writing
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and advance your data science career.
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dateadded: '2021-02-23'
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quotableClips:
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- name: Podcast Introduction
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=0
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endOffset: 100
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- name: 'Career Transition: Psychology to Applied Scientist'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=100
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endOffset: 360
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- name: 'First Public Writing: Early Blog Posts and Meetups'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=360
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endOffset: 570
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- name: 'Writing Motivations: Share, Learn, Be a Beacon'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=570
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endOffset: 840
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- name: 'Audience Targeting: Readers, Peers, and Future Teammates'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=840
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endOffset: 990
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- name: 'Writing as Product: Weekly Shipping and UX Mindset'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=990
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endOffset: 1200
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- name: 'Weekly Writing Cadence: 7-Day Workflow and Schedule'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=1200
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endOffset: 1500
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- name: 'Outline-First Method: Memory Rewriting and Idea Filtering'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=1500
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- name: 'Time Budget & Editing: 25 Hours/Week and Avoiding Over-Editing'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=1740
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endOffset: 1980
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- name: Idea Sources and Topic Prioritization for Technical Writing
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startOffset: 1980
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=1980
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endOffset: 2220
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- name: Title Crafting and Article Length Decisions
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endOffset: 2460
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- name: 'Getting Started: Start Writing, Overcome Friction'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=2460
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endOffset: 2610
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- name: 'Blogging Tools: Medium, Substack, WordPress, Jekyll (GitHub Pages)'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=2610
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endOffset: 2760
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- name: 'Writing Habits: Morning Reps and Weekend Deep Work'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=2760
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endOffset: 2910
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- name: 'Audience Growth: Distribution via Twitter, LinkedIn, Consistency'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=2910
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endOffset: 3060
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- name: 'Writing at Work: Press Release, Working Backwards, and Design Docs'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=3060
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endOffset: 3240
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- name: 'Technical Documentation: Decision Logs, Rationales, and Team Memory'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=3240
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endOffset: 3390
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- name: 'Portfolio Best Practices: Clear README, Quick Start, Repo Tour'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=3390
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endOffset: 3510
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- name: 'Practical Tips: Iterate Outlines, Ship Weekly, Learn by Teaching'
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url: https://www.youtube.com/watch?v=vXWGd7olv3c&t=3510
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endOffset: 3630
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Today we're discussing technical writing, logging, documentation, and more. Our special guest is [Eugene Yan](/people/eugeneyan). Eugene works at the intersection of machine learning and product, building pragmatic ML systems while writing and speaking about effective data science, ML in production, and career growth.

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