{"id":7463,"date":"2026-08-10T09:44:26","date_gmt":"2026-08-10T09:44:26","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-mastering-large-datasets-with-python-video-edition-2024-11\/"},"modified":"2026-08-10T09:44:26","modified_gmt":"2026-08-10T09:44:26","slug":"oreilly-mastering-large-datasets-with-python-video-edition-2024-11","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-mastering-large-datasets-with-python-video-edition-2024-11\/","title":{"rendered":"Oreilly \u2013 Mastering Large Datasets with Python, Video Edition 2024-11"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p data-sourcepos=\"4:1-4:329\"><span style=\"vertical-align: inherit\">Mastering Large Datasets with Python Video Edition. This course teaches you how to scale your big data analytics projects using Python. By learning key concepts like map and reduce and using Python\u2019s powerful tools, you\u2019ll be able to run your algorithms in parallel and take advantage of vast computing resources like cloud clusters.<\/span><\/p>\n<p data-sourcepos=\"7:1-7:420\"><span style=\"vertical-align: inherit\">In today\u2019s world, data is growing rapidly, and analyzing this big data requires special tools and methods. This course will help you meet this challenge using Python, one of the most popular programming languages \u200b\u200bin the field of data mining. By learning parallelization and distributed techniques, you can perform complex analyses on very large data sets and obtain more accurate results in less time.<\/span><\/p>\n<h3 data-sourcepos=\"9:1-9:24\"><span style=\"vertical-align: inherit\">What you will learn<\/span><\/h3>\n<ul data-sourcepos=\"10:1-14:0\">\n<li data-sourcepos=\"10:1-10:81\"><span style=\"vertical-align: inherit\">Concept of map and reduce: Deep understanding of these two fundamental concepts in parallel data processing<\/span><\/li>\n<li data-sourcepos=\"11:1-11:112\"><span style=\"vertical-align: inherit\">Parallelization with multiprocessing and pathos: Learn how to run multiple processes simultaneously to speed up computations<\/span><\/li>\n<li data-sourcepos=\"12:1-12:98\"><span style=\"vertical-align: inherit\">Hadoop and Spark: Using these two powerful tools for large-scale distributed data processing<\/span><\/li>\n<li data-sourcepos=\"13:1-14:0\"><span style=\"vertical-align: inherit\">AWS: Run data processing tasks on Amazon cloud services to take advantage of vast computing resources<\/span><\/li>\n<\/ul>\n<h3 data-sourcepos=\"15:1-15:30\"><span style=\"vertical-align: inherit\">This course is suitable for people who:<\/span><\/h3>\n<ul data-sourcepos=\"16:1-19:0\">\n<li data-sourcepos=\"16:1-16:70\"><span style=\"vertical-align: inherit\">Python programmers who need to work with larger volumes of data<\/span><\/li>\n<li data-sourcepos=\"17:1-17:63\"><span style=\"vertical-align: inherit\">People looking to learn advanced data analysis techniques<\/span><\/li>\n<li data-sourcepos=\"18:1-19:0\"><span style=\"vertical-align: inherit\">Those who want to work in the field of data mining and machine learning<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Software Telemetry Video Edition Course Specifications<\/span><\/h3>\n<ul>\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/mastering-large-datasets\/9781617296239VE\/\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a href=\"https:\/\/downloadlynet.ir\/tag\/john-wolohan\/\"><span style=\"vertical-align: inherit\">John Wolohan<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: Beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 7 hours and 43 minutes<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<ul>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1.<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Introduction<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Why large datasets?<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. What is parallel computing?<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. The map and reduced style<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Distributed computing for speed and scale<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Hadoop: A distributed framework for map and reduce<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Spark for high-powered map, reduce, and more<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. AWS Elastic MapReduce\u2014Large datasets in the cloud<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Accelerating large dataset work: Map and parallel computing<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Parallel processing<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Putting it all together: Scraping a Wikipedia network<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Function pipelines for mapping complex transformations<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Unmasking hacker communications<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Twitter demographic projections<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Processing large datasets with lazy workflows<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Some lazy functions to know<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Understanding iterators: The magic behind lazy Python<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. The poetry puzzle: Lazily processing a large dataset<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Lazy simulations: Simulating fishing villages<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 4. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Accumulation operations with reduction<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. The three parts of reduction<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Reductions you\u2019re familiar with<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Using map and reduce together<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Analyzing car trends with reduction<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Speeding up map and reduce<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 5. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Speeding up map and reduce with advanced parallelization<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Solving the parallel map and reduce paradox<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 6. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 2.<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Processing truly big datasets with Hadoop and Spark<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Hadoop for batch processing<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Using Hadoop to find high-scoring words<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Spark for interactive workflows<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Document word scores in Spark<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 7. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Best practices for large data with Apache Streaming and mrjob<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Tennis analytics with Hadoop<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. mrjob for Pythonic Hadoop streaming<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Tennis match analysis with mrjob<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 8. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. PageRank with map and reduce in PySpark<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Tennis rankings with Elo and PageRank in PySpark<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 9. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Faster decision-making with machine learning and PySpark<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Machine learning basics with decision tree classifiers<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Fast random forest classifications in PySpark<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 10. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3.<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Large datasets in the cloud with Amazon Web Services and S3<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Storing data in the cloud with S3<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 11. Summary<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. MapReduce in the cloud with Amazon\u2019s Elastic MapReduce<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Machine learning in the cloud with Spark on EMR<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Exercises<\/span><\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 12. Summary<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Images of the Mastering Large Datasets with Python Video Edition course<\/span><\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-949434 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2024\/12\/Mastering-Large-Datasets-with-Python-Video-Edition1.png\" alt=\"Mastering Large Datasets with Python Video Edition\" width=\"1203\" height=\"376\"><\/p>\n<h3><span style=\"vertical-align: inherit\">Sample course video<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-148367-1\"><video class=\"wp-video-shortcode\" id=\"video-148367-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mastering_Large_Datasets_with_Python_Video_Edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; height: 360px;\"><source type=\"video\/mp4\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mastering_Large_Datasets_with_Python_Video_Edition_Downloadly.ir.mp4?_=1\"><a href=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mastering_Large_Datasets_with_Python_Video_Edition_Downloadly.ir.mp4?nocache=1786109784620\">https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Mastering_Large_Datasets_with_Python_Video_Edition_Downloadly.ir.mp4<\/a><\/video><\/mediaelementwrapper><\/div>\n<div class=\"mejs-layers\">\n<div class=\"mejs-poster mejs-layer\" style=\"display: none; width: 100%; height: 100%;\"><\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-loading\"><span class=\"mejs-overlay-loading-bg-img\"><\/span><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer\" style=\"display: none; width: 100%; height: 100%;\">\n<div class=\"mejs-overlay-error\"><\/div>\n<\/div>\n<div class=\"mejs-overlay mejs-layer mejs-overlay-play\" style=\"width: 100%; 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height: 100%;\"><\/div>\n<div class=\"mejs-volume-handle\" style=\"bottom: 100%; margin-bottom: -3px;\"><\/div>\n<\/div>\n<p><\/a><\/div>\n<div class=\"mejs-button mejs-fullscreen-button\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Fullscreen\" aria-label=\"Fullscreen\" tabindex=\"0\"><\/button><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div>\n<h3><span style=\"vertical-align: inherit\">Installation Guide<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">After Extract, view with your favorite player.<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Subtitles: None<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Quality: 1080p<\/span><\/p>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">Download link<\/span><\/h3>\n<p><a href=\"https:\/\/dl2.downloadly.ir\/Files\/Elearning\/Oreilly_Mastering_Large_Datasets_with_Python_Video_Edition_2024-11_Downloadly.ir.rar?nocache=1786109779\"><span style=\"vertical-align: inherit\">Download file \u2013 0.98 GB<\/span><\/a><\/p>\n<p><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><\/p>\n<h3><span style=\"vertical-align: inherit\">File size<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">0.98 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Mastering Large Datasets with Python Video Edition. This course teaches you how to scale your big data analytics projects using Python. 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