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SEO optimization (#53)
* SEO optimization * Enhanced SEO and accessibility across multiple pages, including improved descriptions, structured data, and semantic HTML elements. * Updated titles, subtitles, and descriptions for several blog posts to enhance clarity and SEO and remove duplicate_title_tags * Improved accessibility and clarity in blog posts by adding alt text to images, enhancing descriptions, and refining content structure for better readability and SEO. * Enhanced content for two books and a podcast episode by improving descriptions, adding structured sections, and clarifying roles and processes in data science teams. Updated links and ensured consistency in formatting for better readability and SEO. * Updated podcast episode titles, descriptions, and keywords for improved clarity and SEO. Added written versions of the podcasts. * Fixed or removed broken links * Removed broken link
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‎_books/20211018-blueprints-for-text-analytics-using-python.md‎

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- name: Christian Winkler
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text: Thanks for your question, Nikhil Shrestha. You could use semantic embeddings
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like word2vec for supporting information retrieval. Another option would be to
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use something more full-fledged like [txtai]([https://github.com/neuml/txtai](https://github.com/neuml/txtai))
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use something more full-fledged like txtai.
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replies: []
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- name: Christian Winkler
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text: Good question, Asmita! I think it depends on the kind of task you are automating.

‎_books/20250728-production-ready-data-science.md‎

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title: Production‑Ready Data Science
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---
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Are you a data scientist or analyst struggling to take your Jupyter Notebook prototypes to the next level? Have you encountered challenges with code organization, reproducibility, or collaboration as your data science projects grow in complexity? This book is the solution you’ve been seeking.
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Are you a data scientist or analyst struggling to take your Jupyter Notebook prototypes to the next level? Have you encountered challenges with code organization, reproducibility, or collaboration as your data science projects grow in complexity? This book is the solution you've been seeking.
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This book aims to bridge the gap between data analysis and software engineering, providing you with the essential tools and best practices to transform your data science projects into scalable, maintainable, and reproducible solutions.
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This comprehensive guide bridges the gap between data analysis and software engineering, providing you with the essential tools and best practices to transform your data science projects into scalable, maintainable, and collaborative solutions.
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**Why This Book?**
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Complex concepts explained in simple, accessible terms. Each concept includes clear diagrams and code examples, all coming from actual data science workflows.
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**Skills You Will Gain**
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Through practical examples and clear explanations, you'll master techniques for:
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* Transform messy notebooks into organized, maintainable code
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* Create reproducible environments across teams and deployments
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* Write modular, reusable, and testable Python code
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* Implement robust data validation and error handling
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* Leverage version control for code and data integrity
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* Implement automated testing to catch bugs early
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* And much more!
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**About the Author**
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Khuyen Tran transforms how data scientists learn and work. She has written over 180 articles as a top writer on Towards Data Science, helping data professionals bridge the gap between prototyping and production. As founder of CodeCut, she publishes daily Python tips in her newsletter that reach over 10,000 views per month and has built a community of 110,000 LinkedIn followers. Previously an MLOps Engineer and Senior Data Engineer at Accenture, she built enterprise data solutions for clients worldwide.

‎_books/20250908-machine-learning-algorithms-in-depth.md‎

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title: Machine Learning Algorithms in Depth
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---
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Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models.
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Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
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This book guides you from the core mathematical foundations to Python implementations across a variety of applications, including finance, computer vision, and NLP. With a particular emphasis on probabilistic algorithms, you'll learn the fundamentals of Bayesian inference and deep learning. Each algorithm is mathematically derived, followed by hands-on implementation with insightful code annotations and informative graphics.
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**Algorithms You'll Explore**
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* Monte Carlo Stock Price Simulation
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* Image Denoising using Mean-Field Variational Inference
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* EM algorithm for Hidden Markov Models
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* Imbalanced Learning, Active Learning and Ensemble Learning
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* Bayesian Optimization for Hyperparameter Tuning
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* Dirichlet Process K-Means for Clustering Applications
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* Stock Clusters based on Inverse Covariance Estimation
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* Energy Minimization using Simulated Annealing
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* Image Search based on ResNet Convolutional Neural Network
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* Anomaly Detection in Time-Series using Variational Autoencoders

‎_includes/head.html‎

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<meta property="twitter:site" content="{{ site.twitter }}">
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<meta property="twitter:card" content="summary_large_image" />
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<meta property="og:type" content="website">
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<!--
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SEO OPTIMIZATION: Page-specific Open Graph Meta Tags
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- Different og:type for book pages vs author pages vs general website pages
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- Page-specific metadata for better social sharing and search understanding
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- Author information, release date, and categorization for books
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-->
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{% if page.layout == 'book' %}
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<meta property="og:type" content="book">
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<!-- SEO: Book author information for Open Graph -->
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<meta property="book:author" content="{% for author_id in page.authors %}{% assign author = site.people | where: 'name', author_id | first %}{{ author.name | default: author_id }}{% unless forloop.last %}, {% endunless %}{% endfor %}">
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<!-- SEO: Book release date for better categorization -->
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<meta property="book:release_date" content="{{ page.start | date: '%Y-%m-%d' }}">
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<!-- SEO: Book categorization for content understanding -->
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<meta property="book:tag" content="Book of the Week">
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{% elsif page.layout == 'author' %}
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<meta property="og:type" content="profile">
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<!-- SEO: Author-specific Open Graph metadata -->
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<meta property="profile:first_name" content="{{ page.title | split: ' ' | first }}">
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<meta property="profile:last_name" content="{{ page.title | split: ' ' | last }}">
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<meta property="profile:username" content="{{ page.short }}">
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{% if page.twitter %}
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<meta property="profile:username" content="{{ page.twitter }}">
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{% endif %}
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{% else %}
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<meta property="og:type" content="website">
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{% endif %}
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<meta property="og:site_name" content="{{ site.name }}">
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<meta property="og:url" content="{{ site.url }}{{ page.url }}">
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<link rel="canonical" href="{{ site.url }}{{ page.url }}">
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<meta property="og:image" content="{{ site.url }}/{{ page.image }}">
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<meta property="twitter:image" content="{{ site.url }}/{{ page.image }}">
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<link rel="image_src" href="{{ site.url }}/{{ page.image }}">
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<!--
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SEO OPTIMIZATION: Enhanced Image Meta Tags
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- Image dimensions for better social media display
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- Alt text for accessibility and SEO
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- Consistent image tags for both Open Graph and Twitter Cards
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- Author-specific image handling
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-->
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{% if page.layout == 'author' %}
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<meta property="og:image" content="{{ site.url }}/{{ page.picture }}">
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<!-- SEO: Image dimensions for optimal social media display -->
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<meta property="og:image:width" content="400">
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<meta property="og:image:height" content="400">
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<!-- SEO: Descriptive alt text for image accessibility -->
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<meta property="og:image:alt" content="{{ page.title }} - Author Profile Photo">
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<meta property="twitter:image" content="{{ site.url }}/{{ page.picture }}">
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<!-- SEO: Twitter-specific image alt text -->
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<meta property="twitter:image:alt" content="{{ page.title }} - Author Profile Photo">
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<link rel="image_src" href="{{ site.url }}/{{ page.picture }}">
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{% else %}
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<meta property="og:image" content="{{ site.url }}/{{ page.image }}">
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<!-- SEO: Image dimensions for optimal social media display -->
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<meta property="og:image:width" content="1200">
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<meta property="og:image:height" content="630">
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<!-- SEO: Descriptive alt text for image accessibility -->
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<meta property="og:image:alt" content="{{ page.title }} book cover">
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<meta property="twitter:image" content="{{ site.url }}/{{ page.image }}">
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<!-- SEO: Twitter-specific image alt text -->
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<meta property="twitter:image:alt" content="{{ page.title }} book cover">
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<link rel="image_src" href="{{ site.url }}/{{ page.image }}">
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{% endif %}
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<meta property="og:description" content="{{ page.description }}">
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<meta property="twitter:description" content="{{ page.description }}">
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<meta name="description" content="{{ page.description }}">
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<!-- SEO: Page-specific meta descriptions for search engines and social media -->
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{% if page.layout == 'author' %}
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<meta property="og:description" content="{{ page.content | strip_html | truncate: 160 | escape | default: 'Learn more about ' | append: page.title | append: ', a data science professional and member of the DataTalks.Club community.' }}">
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<meta property="twitter:description" content="{{ page.content | strip_html | truncate: 160 | escape | default: 'Learn more about ' | append: page.title | append: ', a data science professional and member of the DataTalks.Club community.' }}">
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<meta name="description" content="{{ page.content | strip_html | truncate: 160 | escape | default: 'Learn more about ' | append: page.title | append: ', a data science professional and member of the DataTalks.Club community.' }}">
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<!-- SEO: Author-specific keywords -->
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<meta name="keywords" content="{{ page.title | downcase }}, author, data scientist, machine learning, data talks club, {% if page.twitter %}{{ page.twitter }}, {% endif %}{% if page.github %}{{ page.github }}, {% endif %}{% if page.linkedin %}{{ page.linkedin }}, {% endif %}data science professional, AI expert">
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{% else %}
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<meta property="og:description" content="{{ page.description }}">
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<meta property="twitter:description" content="{{ page.description }}">
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<meta name="description" content="{{ page.description }}">
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<!--
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SEO OPTIMIZATION: Keywords Meta Tag
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- Comprehensive keywords including book title, authors, and site context
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- Helps search engines understand page content and relevance
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- Includes site-specific terms for better categorization
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-->
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<meta name="keywords" content="book, {{ page.title | downcase }}, {% for author_id in page.authors %}{% assign author = site.people | where: 'name', author_id | first %}{{ author.name | default: author_id | downcase }}{% unless forloop.last %}, {% endunless %}{% endfor %}, book of the week, data talks club">
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{% endif %}
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<link rel="apple-touch-icon" sizes="152x152" href="/apple-touch-icon.png">
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<link rel="icon" type="image/png" sizes="32x32" href="/favicon-32x32.png">
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<meta name="msapplication-TileColor" content="#da532c">
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<meta name="theme-color" content="#ffffff">
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<link rel="stylesheet"
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href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css"
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integrity="sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh"
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crossorigin="anonymous">
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<link href="https://fonts.googleapis.com/css?family=Alegreya+Sans:300,400,700" rel="stylesheet" />
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<link href="https://fonts.googleapis.com/css?family=Raleway:300,400,500,600,700" rel="stylesheet" />
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<!--
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SEO OPTIMIZATION: Performance & Core Web Vitals
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- Preload critical CSS for faster rendering
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- Async loading prevents render blocking
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- display=swap prevents font flash (FOIT/FOUT)
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- Noscript fallbacks for accessibility
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-->
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<!-- Preload critical resources -->
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<link rel="preload" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css" as="style" onload="this.onload=null;this.rel='stylesheet'">
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<noscript><link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css"></noscript>
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<!-- SEO: Google Fonts with display=swap for better performance -->
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<link rel="preload" href="https://fonts.googleapis.com/css?family=Alegreya+Sans:300,400,700&display=swap" as="style" onload="this.onload=null;this.rel='stylesheet'">
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<noscript><link href="https://fonts.googleapis.com/css?family=Alegreya+Sans:300,400,700&display=swap" rel="stylesheet"></noscript>
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<link rel="preload" href="https://fonts.googleapis.com/css?family=Raleway:300,400,500,600,700&display=swap" as="style" onload="this.onload=null;this.rel='stylesheet'">
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<noscript><link href="https://fonts.googleapis.com/css?family=Raleway:300,400,500,600,700&display=swap" rel="stylesheet"></noscript>
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<script src="https://kit.fontawesome.com/c37fbeb327.js"></script>
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<!-- SEO: Load non-critical resources asynchronously -->
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<script src="https://kit.fontawesome.com/c37fbeb327.js" crossorigin="anonymous" async></script>
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‎_layouts/author.html‎

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<body>
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{% include header.html %}
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<main class="container">
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<img class="author-image" src="/{{ page.picture }}" />
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{{ page.title }}
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</div>
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<!-- SEO: Author Profile Header with Semantic HTML -->
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<header class="my-3 author-name text-center">
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<img class="author-image" src="/{{ page.picture }}"
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alt="{{ page.title }} - Author Profile Photo"
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loading="eager" />
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<h1 class="author-title">{{ page.title }}</h1>
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</header>
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<div class="content-main my-3">
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<section class="content-main my-3" aria-labelledby="author-bio">
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<h2 id="author-bio" class="sr-only">Author Biography</h2>
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</section>
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<nav class="author-links my-3" aria-label="Author Social Media Links">
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<a href="https://twitter.com/{{ page.twitter }}" target="_blank" rel="noopener noreferrer" aria-label="Follow {{ page.title }} on Twitter">
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<i class="fab fa-twitter" aria-hidden="true"></i>
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<span class="sr-only">Twitter</span>
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</a>
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<a href="https://linkedin.com/in/{{ page.linkedin }}" target="_blank"><i class="fab fa-linkedin-in"></i></a>
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<a href="https://linkedin.com/in/{{ page.linkedin }}" target="_blank" rel="noopener noreferrer" aria-label="Connect with {{ page.title }} on LinkedIn">
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<i class="fab fa-linkedin-in" aria-hidden="true"></i>
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<span class="sr-only">LinkedIn</span>
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</a>
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<a href="https://github.com/{{ page.github }}" target="_blank"><i class="fab fa-github"></i></a>
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<a href="https://github.com/{{ page.github }}" target="_blank" rel="noopener noreferrer" aria-label="View {{ page.title }}'s GitHub profile">
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<i class="fab fa-github" aria-hidden="true"></i>
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<span class="sr-only">GitHub</span>
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</a>
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<a href="{{ page.web }}" target="_blank" rel="noopener noreferrer" aria-label="Visit {{ page.title }}'s personal website">
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<i class="fas fa-link" aria-hidden="true"></i>
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<span class="sr-only">Personal Website</span>
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</a>
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<ul class="articles-list">
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{% for post in articles %}
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<li class="article-item">
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<a href="{{ post.url }}" title="Read: {{ post.title }}">{{ post.title }}</a>
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</li>
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<ul class="events-list">
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{% for event in events %}
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{% endfor %}
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{% assign books = site.books
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<h3>Books</h3>
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<ul>
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{% for book in books %}
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<li><a href="{{ book.id }}.html">{{ book.title }}</a> (the book of the week from {{ book.start | date_to_string }} to {{ book.end | date_to_string }})</li>
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<ul class="books-list">
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{% for book in books %}
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<li class="book-item">
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<script type="application/ld+json">
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{
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"@context": "https://schema.org",
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"@type": "Person",
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"name": "{{ page.title }}",
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"image": "{{ site.url }}/{{ page.picture }}",
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"description": "{{ page.content | strip_html | truncate: 160 | escape }}",
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"url": "{{ site.url }}{{ page.url }}",
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{% if page.twitter %}
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"sameAs": [
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"https://twitter.com/{{ page.twitter }}",
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{% if page.linkedin %}"https://linkedin.com/in/{{ page.linkedin }}",{% endif %}
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{% if page.github %}"https://github.com/{{ page.github }}",{% endif %}
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{% if page.web %}"{{ page.web }}"{% endif %}
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],
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{% endif %}
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"jobTitle": "Data Science Professional",
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"worksFor": {
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"@type": "Organization",
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"name": "DataTalks.Club"
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},
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"alumniOf": {
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"@type": "EducationalOrganization",
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"name": "DataTalks.Club Community"
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}
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}
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</script>
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</body>

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