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TechKeysXSlicing Tuple in python: Slicing a tuple in Python means extracting a portion of it using the syntax tuple[start :stop :step]. <a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://mastodon.social/tags/PythonTips" class="mention hashtag" rel="nofollow noopener" target="_blank">#PythonTips</a> <a href="https://mastodon.social/tags/Tuple" class="mention hashtag" rel="nofollow noopener" target="_blank">#Tuple</a> <a href="https://mastodon.social/tags/PythonSlicing" class="mention hashtag" rel="nofollow noopener" target="_blank">#PythonSlicing</a> <a href="https://mastodon.social/tags/CodeSnippet" class="mention hashtag" rel="nofollow noopener" target="_blank">#CodeSnippet</a> <a href="https://mastodon.social/tags/LearnPython" class="mention hashtag" rel="nofollow noopener" target="_blank">#LearnPython</a> <a href="https://mastodon.social/tags/DevTips" class="mention hashtag" rel="nofollow noopener" target="_blank">#DevTips</a> <a href="https://mastodon.social/tags/100DaysOfCode" class="mention hashtag" rel="nofollow noopener" target="_blank">#100DaysOfCode</a> <a href="https://mastodon.social/tags/Programming" class="mention hashtag" rel="nofollow noopener" target="_blank">#Programming</a> <a href="https://mastodon.social/tags/pythonlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#pythonlearning</a>

Sam GutentagStand back! I'm doing a developer! <a href="https://pypi.org/project/gutentag-world/" rel="nofollow noopener" translate="no" target="_blank">https://pypi.org/project/gutentag-world/</a><a href="https://mastodon.social/tags/pythonlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#pythonlearning</a> <a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://mastodon.social/tags/gutentagworld" class="mention hashtag" rel="nofollow noopener" target="_blank">#gutentagworld</a>

LogicLuminaryBill📅 Monday, November 11, 2024 Progress Update🎯 <a href="https://mastodon.social/tags/365DaysofCode" class="mention hashtag" rel="nofollow noopener" target="_blank">#365DaysofCode</a> Day 316 🎯 <a href="https://mastodon.social/tags/100daysofcode" class="mention hashtag" rel="nofollow noopener" target="_blank">#100daysofcode</a> Day 50 🎯 <a href="https://mastodon.social/tags/freeCodeCamp" class="mention hashtag" rel="nofollow noopener" target="_blank">#freeCodeCamp</a> <a href="https://mastodon.social/tags/Round_3_100daysofcode" class="mention hashtag" rel="nofollow noopener" target="_blank">#Round_3_100daysofcode</a> 🎯 <a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://mastodon.social/tags/GreatLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#GreatLearning</a> <a href="https://mastodon.social/tags/GitHub" class="mention hashtag" rel="nofollow noopener" target="_blank">#GitHub</a> <a href="https://mastodon.social/tags/CS50P" class="mention hashtag" rel="nofollow noopener" target="_blank">#CS50P</a> <a href="https://mastodon.social/tags/GameOff2024" class="mention hashtag" rel="nofollow noopener" target="_blank">#GameOff2024</a> <a href="https://mastodon.social/tags/Pygame" class="mention hashtag" rel="nofollow noopener" target="_blank">#Pygame</a>📖 Daily Reading: freeCodeCamp news: 1 article Daily.dev: 1 article ✅ Tasks Completed: Harvard CS50P: Watched Week 7 Lecture, diving deeper into concepts Game Off 2024: Updated README on GitHub with new details<a href="https://mastodon.social/tags/PythonLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#PythonLearning</a> <a href="https://mastodon.social/tags/CS50" class="mention hashtag" rel="nofollow noopener" target="_blank">#CS50</a> <a href="https://mastodon.social/tags/GitHubProjects" class="mention hashtag" rel="nofollow noopener" target="_blank">#GitHubProjects</a> <a href="https://mastodon.social/tags/DailyCoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#DailyCoding</a> <a href="https://mastodon.social/tags/GameDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#GameDev</a>

Jeff SikesI know I'm singing to the choir here but as someone that hasn't used Python in my day job until recently, I continue to be happy with its extensibility and usefulness.It is so obviously built by real people that use it to solve their everyday problems. Parsing files, creating reports, evaluating messy data. Wish I had this in my toolbox sooner.<a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://mastodon.social/tags/PythonLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#PythonLearning</a>

Statistics GlobeRisk modeling is a fascinating aspect of data science, particularly in the context of Frequency-Severity modeling and Monte Carlo simulations.Thanks to Gabriel Ryan for sharing this graph in a recent post, which explains the topic in further detail.More information: <a href="http://eepurl.com/gH6myT" rel="nofollow noopener" translate="no" target="_blank">http://eepurl.com/gH6myT</a><a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#datavisualization</a> <a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#data</a> <a href="https://mastodon.social/tags/statisticians" class="mention hashtag" rel="nofollow noopener" target="_blank">#statisticians</a> <a href="https://mastodon.social/tags/pythonlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#pythonlearning</a> <a href="https://mastodon.social/tags/datascienceeducation" class="mention hashtag" rel="nofollow noopener" target="_blank">#datascienceeducation</a> <a href="https://mastodon.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#bigdata</a>

Statistics GlobeI've created a series of tutorial posts on Principal Component Analysis (PCA) across my social media accounts.- PCA Explained: <a href="https://www.facebook.com/joachim.schork/posts/pfbid02Yx3RHmDakew21EiZBU9ZAP8hSAqoERc3ThiMBwevYSDTwZh2b7JDgEHgB7TmWKtGl" rel="nofollow noopener" translate="no" target="_blank">https://www.facebook.com/joachim.schork/posts/pfbid02Yx3RHmDakew21EiZBU9ZAP8hSAqoERc3ThiMBwevYSDTwZh2b7JDgEHgB7TmWKtGl</a> - Pros & Cons of PCA: <a href="https://x.com/JoachimSchork/status/1820334157219647749" rel="nofollow noopener" translate="no" target="_blank">https://x.com/JoachimSchork/status/1820334157219647749</a> - How to Visualize PCA Results: <a href="https://www.linkedin.com/posts/joachim-schork_dataanalytics-rprogramming-bigdata-activity-7224307564044267520-V8Q1/" rel="nofollow noopener" translate="no" target="_blank">https://www.linkedin.com/posts/joachim-schork_dataanalytics-rprogramming-bigdata-activity-7224307564044267520-V8Q1/</a>This series is also a teaser for my comprehensive online course on "Principal Component Analysis (PCA) – From Theory to Application in R." Further details: <a href="https://statisticsglobe.com/online-course-pca-theory-application-r" rel="nofollow noopener" translate="no" target="_blank">https://statisticsglobe.com/online-course-pca-theory-application-r</a><a href="https://mastodon.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://mastodon.social/tags/pythonlearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#pythonlearning</a> <a href="https://mastodon.social/tags/datascienceenthusiast" class="mention hashtag" rel="nofollow noopener" target="_blank">#datascienceenthusiast</a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#datascience</a>

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