BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Riyadh BEGIN:DAYLIGHT DTSTART:20380119T061407 TZOFFSETFROM:+0300 TZOFFSETTO:+0300 RRULE:FREQ=YEARLY;BYDAY=3TU;BYMONTH=1 TZNAME:+03 END:DAYLIGHT BEGIN:STANDARD DTSTART:19470313T235308 TZOFFSETFROM:+0306 TZOFFSETTO:+0300 RRULE:FREQ=YEARLY;BYDAY=2TH;BYMONTH=3 TZNAME:+03 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250330T100200Z UID:BAD412FE-266A-410D-A835-5D40E7217FDE DTSTART;TZID=Asia/Riyadh:20250426T200000 DTEND;TZID=Asia/Riyadh:20250426T213000 DESCRIPTION:Workshop Series Introduction\n\nThis workshop is one of a serie s of workshops that aim to enhance understanding of data modeling processe s for those interested in artificial intelligence and machine learning. Sp ecifically\, it is designed to prepare participants for the first edition of the upcoming [IEEEModelthon 1.0](https://events.vtools.ieee.org/m/44393 8) on [Kaggle](https://www.kaggle.com/).\n\nWorkshop Overview\n\nNow it’ s time to discover and visualize the data to gain insights. In this worksh op we will go into a little more depth and understand the data.\nIn this p hase\, we will create statistical visuals to get an idea about the distrib ution of data values and how they are correlated to each other using corre lation measures by building scatter plots and heat maps. We will also dete ct outliers (if they exist) and how to deal with them.\n\nIn addition\, we will evaluate the quality of the data and identify any dirty data to deal with later in the next step of building a machine learning model -data pr eprocessing and cleansing.\n\nIn this workshop\, we will start working wit h the Python Matplotlib library to build visualizations and extract insigh t from them. We will also learn about the different types of visualization s and how to choose the appropriate visualization based on each data type. \n\nPurpose\n\nPrimarily\, it will cover how to create data charts using P ython Matplotlib library and how to extract the insight about that chart! This workshop is ideal for beginners to intermediate learners eager to und erstand the initial steps of the data science process.\n\nObjective and Ou tcome\n\nThis workshop is essential for anyone in a data-driven field to g ain deeper understanding of the story behind the data. It introduces metho ds for representing data as charts of various types and understanding the relationships between columns. The main outcome of this phase is to unders tand the data well to decide how to deal with it in the next phase -data p reprocessing.\n\nFor more information about the IEEEModelthon1.0\, click [ here](https://events.vtools.ieee.org/m/443938).\n\nVirtual: https://events .vtools.ieee.org/m/444016 LOCATION:Virtual: https://events.vtools.ieee.org/m/444016 ORGANIZER:ieee.computersociety.aabu@gmail.com SEQUENCE:31 SUMMARY:Matplotlib Essentials: Exploratory Data Analysis (EDA) URL;VALUE=URI:https://events.vtools.ieee.org/m/444016 X-ALT-DESC:Description: <br /><h3><strong>Workshop Series Introduction</str ong></h3>\n<p>This workshop is one of a series of workshops that aim to en hance understanding of data modeling processes for those interested in art ificial intelligence and machine learning. Specifically\, it is designed t o prepare participants for the first edition of the upcoming&nbsp\;<a titl e="IEEEModelthon 1.0" href="https://events.vtools.ieee.org/m/443938" targe t="_blank" rel="noopener"><em><strong>IEEEModelthon 1.0</strong></em></a>& nbsp\;on&nbsp\;<strong><em><a title="Kaggle" href="https://www.kaggle.com/ " target="_blank" rel="noopener">Kaggle</a></em>.<br><br></strong></p>\n<h 3><strong>Workshop Overview</strong></h3>\n<p>Now it&rsquo\;s time to disc over and visualize the data to gain insights. In this workshop we will go into a little more depth and understand the data.<br>In this phase\, we wi ll create <em>statistical visuals</em> to get an idea about the distributi on of data values and how they are correlated to each other using correlat ion measures by building <em>scatter plots</em> and <em>heat maps</em>. We will also detect outliers (if they exist) and how to deal with them. <br> <br>In addition\, we will evaluate the <strong>quality of the data</strong > and identify any <strong>dirty data</strong> to deal with later in the n ext step of building a machine learning model -<strong>data preprocessing and cleansing.<br><br></strong>In this workshop\, we will start working wi th the&nbsp\;<strong>Python Matplotlib</strong> library to build visualiza tions and extract insight from them. We will also learn about the differen t types of visualizations and how to choose the appropriate visualization based on each data type.</p>\n<h3><br><strong>Purpose</strong></h3>\n<p>Pr imarily\, it will cover how to create data charts using <strong>Python Mat plotlib</strong> library and how to extract the insight about that chart! This workshop is ideal for beginners to intermediate learners eager to und erstand the initial steps of the data science process.<br><br></p>\n<h3><s trong>Objective and Outcome</strong></h3>\n<p>This workshop is essential f or anyone in a data-driven field to gain deeper understanding of the story behind the data. It introduces methods for representing data as charts of various types and understanding the relationships between columns. The ma in outcome of this phase is to understand the data well to decide how to d eal with it in the next phase&nbsp\;<strong>-data preprocessing</strong>.< br><br><br>For more information about the&nbsp\;<strong>IEEEModelthon1.0</ strong>\, click&nbsp\;<a title="IEEEModelthon1.0" href="https://events.vto ols.ieee.org/m/443938" target="_blank" rel="noopener">here</a>.</p> END:VEVENT END:VCALENDAR