BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Africa/Cairo BEGIN:DAYLIGHT DTSTART:20240426T010000 TZOFFSETFROM:+0200 TZOFFSETTO:+0300 RRULE:FREQ=YEARLY;BYDAY=-1FR;BYMONTH=4 TZNAME:EEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20241031T230000 TZOFFSETFROM:+0300 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1TH;BYMONTH=10 TZNAME:EET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240819T131435Z UID:6602792B-4C89-44CF-AD68-3B767366C411 DTSTART;TZID=Africa/Cairo:20240817T170000 DTEND;TZID=Africa/Cairo:20240817T183000 DESCRIPTION:Welcome to our exclusive workshop\, Introduction to Kaggle & Ju pyter for Data Science (Entry Level) designed to gap you with the essentia l tools and skills for a exploratory journey in data science.\n\nIn this s ession\, you will:\n\nknow what Kaggle is:\nLearn how to create a Kaggle a ccount\, navigate its rich environment\, and leverage its features for com petitions and datasets.\n\nNavigate Python-Jupyter Notebook:\nGain hands-o n experience with Anaconda installation and explore the Jupyter Notebook i nterface.\n\nUnderstand Data Science Tasks:\nDelve into core data science tasks such as classification\, regression\, clustering\, etc.\n\nPython Pa ckage Manager: Discover how to install and utilize key Python libraries fo r effective data manipulation using PIP and Anaconda.\n\nExplore Machine L earning Workflow: Acquire foundational knowledge on building and training machine learning models\, address common challenges\, and implement method s for evaluating model performance.\n\nEngage in Practical Application: Pu t your learning into practice with a guided example\, including the creati on and submission of predictions in a Kaggle competition.\n\nThis workshop is meticulously designed for those at the entry level\, offering a simple introduction to Kaggle and Python-Jupyter.\nJoin us to refine your skills and gain practical insights from an experienced data scientist.\n\nDate: Saturday\, August 17\, 2024\nLocation: Virtually via Google Meet\n\nSpeake r(s): Ahmad Al-qaisi\n\nAgenda: \nWorkshop Outline: Introduction to Kaggle and Jupyter for Data Science (Entry Level)\n\n1. Kaggle (10 minutes)\n\n- \nCreate Account\n\n-\nStep-by-step guide on setting up a Kaggle account.\ n\n-\nGetting started with Kaggle Environment\n\n-\nOverview of Kaggle's f eatures\, competitions\, and datasets.\n\n2. Python-Jupyter (20 minutes)\n \n-\nIntroduction to IPython\n\n-\nInstall Anaconda on Windows\n\n-\nInstr uctions on downloading and installing Anaconda.\n\n-\nGetting started with Jupyter-Notebook Environment\n\n-\nIntroduction to Jupyter Notebook inter face and functionalities.\n\n3. Data Science Processes Overview (10 minute s)\n\n-\nTypes of Data Science Tasks\n\n-\nExplanation of various data sci ence tasks (e.g.\, classification\, regression\, clustering).\n\n-\nInstal l Required Libraries Using PIP and Anaconda\n\n-\nInstructions on installi ng essential Python libraries: pandas\, numpy\, matplotlib\, scikit-learn\ , etc.\n\n4. Machine Learning Workflow (10 minutes)\n\n-\nMachine Learning Training\n\n-\nIntroduction to building and training machine learning mod els.\n\n-\nModel Challenges\n\n-\nDiscussion on common issues such as over fitting and underfitting.\n\n-\nModel Testing\n\n-\nMethods for evaluating model performance.\n\n5. Practical Example and Simple Submission in a Kag gle Competition (10 minutes)\n\n-\nSubmission\n\n-\nGuide on creating and submitting predictions to Kaggle.\n\n6. Q&A Session\n\n-\nOpen Discussion\ n\n-\nParticipants can ask questions and get answers related to the worksh op topics.\n\nVirtual: https://events.vtools.ieee.org/m/429875 LOCATION:Virtual: https://events.vtools.ieee.org/m/429875 ORGANIZER:ieee.computersociety.aabu@gmail.com SEQUENCE:77 SUMMARY:Introduction to Kaggle and Jupyter for Data Science URL;VALUE=URI:https://events.vtools.ieee.org/m/429875 X-ALT-DESC:Description: <br /><p>Welcome to our exclusive workshop\,&nbsp\; <strong>Introduction to Kaggle &amp\; Jupyter for Data Science (Entry Leve l)</strong>&nbsp\;designed to gap you with the essential tools and skills for a exploratory journey in data science.</p>\n<p>In this session\, you w ill:<br><br><strong>know what Kaggle is</strong>: <br>Learn how to create a <strong>Kaggle</strong> account\, navigate its rich environment\, and le verage its features for competitions and datasets.</p>\n<p><strong><br>Nav igate Python-Jupyter Notebook</strong>: <br>Gain hands-on experience with <strong>Anaconda</strong> installation and explore the <strong>Jupyter Not ebook</strong> interface.</p>\n<p><br><strong>Understand Data Science Task s</strong>: <br>Delve into core data science tasks such as <em>classificat ion</em>\, <em>regression</em>\, <em>clustering</em>\, etc.</p>\n<p>&nbsp\ ;<br><strong>Python Package Manager:&nbsp\;</strong>Discover how to instal l and utilize key Python libraries for effective data manipulation using < strong>PIP</strong> and <strong>Anaconda</strong>.</p>\n<p><br><strong>Exp lore Machine Learning Workflow</strong>: Acquire foundational knowledge on building and training machine learning models\, address common challenges \, and implement methods for evaluating model performance.</p>\n<p><strong ><br>Engage in Practical Application</strong>: Put your learning into prac tice with a guided example\, including the creation and submission of pred ictions in a <strong>Kaggle</strong> competition.</p>\n<p><br><br>This wor kshop is meticulously designed for those at the entry level\, offering a s imple introduction to <strong>Kaggle</strong> and <strong>Python-Jupyter</ strong>. <br>Join us to refine your skills and gain practical insights fro m an experienced data scientist.</p>\n<p>Date: Saturday\, August 17\, 2024 <br>Location: Virtually via Google Meet</p><br /><br />Agenda: <br /><h3 d ir="ltr">Workshop Outline: Introduction to Kaggle and Jupyter for Data Sci ence (Entry Level)</h3>\n<h4 dir="ltr">1. Kaggle (10 minutes)</h4>\n<ul>\n <li dir="ltr" aria-level="1">\n<p dir="ltr" role="presentation">Create Acc ount</p>\n</li>\n<ul>\n<li dir="ltr" aria-level="2">\n<p dir="ltr" role="p resentation">Step-by-step guide on setting up a Kaggle account.</p>\n</li> \n</ul>\n<li dir="ltr" aria-level="1">\n<p dir="ltr" role="presentation">G etting started with Kaggle Environment</p>\n</li>\n<ul>\n<li dir="ltr" ari a-level="2">\n<p dir="ltr" role="presentation">Overview of Kaggle's featur es\, competitions\, and datasets.</p>\n</li>\n</ul>\n</ul>\n<h4 dir="ltr"> 2. Python-Jupyter (20 minutes)</h4>\n<ul>\n<li dir="ltr" aria-level="1">\n <p dir="ltr" role="presentation">Introduction to IPython</p>\n</li>\n</ul> \n<ul>\n<li dir="ltr" aria-level="1">\n<p dir="ltr" role="presentation">In stall Anaconda on Windows</p>\n</li>\n<ul>\n<li dir="ltr" aria-level="2">\ n<p dir="ltr" role="presentation">Instructions on downloading and installi ng Anaconda.</p>\n</li>\n</ul>\n<li dir="ltr" aria-level="1">\n<p dir="ltr " role="presentation">Getting started with Jupyter-Notebook Environment</p >\n</li>\n<ul>\n<li dir="ltr" aria-level="2">\n<p dir="ltr" role="presenta tion">Introduction to Jupyter Notebook interface and functionalities.</p>\ n</li>\n</ul>\n</ul>\n<h4 dir="ltr">3. Data Science Processes Overview (10 minutes)</h4>\n<ul>\n<li dir="ltr" aria-level="1">\n<p dir="ltr" role="pr esentation">Types of Data Science Tasks</p>\n</li>\n<ul>\n<li dir="ltr" ar ia-level="2">\n<p dir="ltr" role="presentation">Explanation of various dat a science tasks (e.g.\, classification\, regression\, clustering).</p>\n</ li>\n</ul>\n<li dir="ltr" aria-level="1">\n<p dir="ltr" role="presentation ">Install Required Libraries Using PIP and Anaconda</p>\n</li>\n<ul>\n<li dir="ltr" aria-level="2">\n<p dir="ltr" role="presentation">Instructions o n installing essential Python libraries: pandas\, numpy\, matplotlib\, sci kit-learn\, etc.</p>\n</li>\n</ul>\n</ul>\n<h4 dir="ltr">4. Machine Learni ng Workflow (10 minutes)</h4>\n<ul>\n<li dir="ltr" aria-level="1">\n<p dir ="ltr" role="presentation">Machine Learning Training</p>\n</li>\n<ul>\n<li dir="ltr" aria-level="2">\n<p dir="ltr" role="presentation">Introduction to building and training machine learning models.</p>\n</li>\n</ul>\n<li d ir="ltr" aria-level="1">\n<p dir="ltr" role="presentation">Model Challenge s</p>\n</li>\n<ul>\n<li dir="ltr" aria-level="2">\n<p dir="ltr" role="pres entation">Discussion on common issues such as overfitting and underfitting .</p>\n</li>\n</ul>\n<li dir="ltr" aria-level="1">\n<p dir="ltr" role="pre sentation">Model Testing</p>\n</li>\n<ul>\n<li dir="ltr" aria-level="2">\n <p dir="ltr" role="presentation">Methods for evaluating model performance. </p>\n</li>\n</ul>\n</ul>\n<h4 dir="ltr">5. Practical Example and Simple S ubmission in a Kaggle Competition (10 minutes)</h4>\n<ul>\n<li dir="ltr" a ria-level="1">\n<p dir="ltr" role="presentation">Submission</p>\n</li>\n<u l>\n<li dir="ltr" aria-level="2">\n<p dir="ltr" role="presentation">Guide on creating and submitting predictions to Kaggle.</p>\n</li>\n</ul>\n</ul> \n<h4 dir="ltr">6. Q&amp\;A Session</h4>\n<ul>\n<li dir="ltr" aria-level=" 1">\n<p dir="ltr" role="presentation">Open Discussion</p>\n</li>\n<ul>\n<l i dir="ltr" aria-level="2">\n<p dir="ltr" role="presentation">Participants can ask questions and get answers related to the workshop topics.</p>\n</ li>\n</ul>\n</ul>\n<h4 dir="ltr">&nbsp\;</h4> END:VEVENT END:VCALENDAR