{"id":588,"date":"2025-12-07T16:50:07","date_gmt":"2025-12-07T16:50:07","guid":{"rendered":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/?page_id=588"},"modified":"2025-12-07T17:41:42","modified_gmt":"2025-12-07T17:41:42","slug":"hands-on-2","status":"publish","type":"page","link":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/hands-on-2\/","title":{"rendered":"HANDS-ON"},"content":{"rendered":"<h3>Understanding data collections content: a quantitative vision<\/h3>\n<pre>Keep in mind that for performing data analytics you are willing to make sense of data and this implies acquiring Data Literacy. Have a look at this reference for background.\u00a0<\/pre>\n<p>Michel Bowen, Anthony Bartley, <a href=\"https:\/\/drive.google.com\/file\/d\/117xsuTUIOtad5M6KQ66cUiUBPQ5BFJAq\/view?usp=sharing\">The Basics of Data Literacy: making your students (and you!) make sens of data<\/a>, NST Press, Arlington Virginia<\/p>\n<h3><strong>Useful information: cheat sheets<\/strong><\/h3>\n<ol>\n<li>Some of the following hands on will be done in Python. So here a memento of the language [<a href=\"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-content\/uploads\/sites\/42\/2018\/09\/2.Python-memento.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">PDF<\/a>]<\/li>\n<li>Imbalanced Data in Classification <a href=\"https:\/\/drive.google.com\/file\/d\/118cArj4IiQcETQmFZi8LvcOGjX_WZfBg\/view?usp=sharing\">Cheat Sheet<\/a><\/li>\n<\/ol>\n<h3><span style=\"color: #3366ff;\">Data and experimental lab: Kaggle &amp; Colab<\/span><\/h3>\n<ul>\n<li>Access your Kaggle account (<a href=\"https:\/\/www.kaggle.com\/\">https:\/\/www.kaggle.com\/<\/a>)\u00a0<\/li>\n<li>Prepare your Kaggle environment following the instructions <a href=\"http:\/\/vargas-solar.com\/data-centric-smart-everything\/getting-started-with-kaggle\/\" target=\"_blank\" rel=\"noopener noreferrer\">here<\/a><\/li>\n<li>Create a gmail account for using Colab (<a href=\"https:\/\/colab.research.google.com\/\">https:\/\/colab.research.google.com\/<\/a>) and follow instructions in class.<\/li>\n<li><span style=\"font-size: 14px; color: #0000ff;\"><span style=\"color: #000000;\">Working Locally on your computer (why not?)<\/span> If you are willing to use your own computer <span style=\"color: #ff0000;\">outside the course<\/span>, u<\/span><span style=\"color: #0000ff;\">sing a self contained Data Science environment follow two steps (requires medium technical skills):<\/span>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\n<li>Download <a href=\"https:\/\/www.anaconda.com\/download\/#macos\" target=\"_blank\" rel=\"noopener noreferrer\">Anaconda<\/a> based on your machine and OS.<\/li>\n<li>Install Anaconda following the instructions according to your OS (<a href=\"https:\/\/docs.anaconda.com\/anaconda\/install\/windows\/\" target=\"_blank\" rel=\"noopener noreferrer\">Windows<\/a>, <a href=\"https:\/\/docs.anaconda.com\/anaconda\/install\/mac-os\/\" target=\"_blank\" rel=\"noopener noreferrer\">MacOS<\/a>).<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<\/li>\n<\/ul>\n<h2><a href=\"http:\/\/vargas-solar.com\/data-centric-smart-everything\/instructions-for-hands-on-exercises-ense3-programs\/\" target=\"_blank\" rel=\"noopener\">ENSE3 Big Data Storage and Analytics exercises<\/a><\/h2>\n<h2>Hands-on Datathon for ENSE3 &#8211; engineering program<\/h2>\n<div><a href=\"http:\/\/vargas-solar.com\/data-centric-smart-everything\/intelligent-energy-consumption-fairness-in-smart-cities\/\" target=\"_blank\" rel=\"noopener\"><strong><span lang=\"EN-US\">Intelligent Energy Consumption &amp; Fairness in Smart Cities<\/span><\/strong><\/a><\/div>\n<p style=\"font-weight: 400;\">During a 4-hour datathon, students develop a mini-project implemented through three 1-hour challenges. The project focuses on intelligent energy consumption and fairness-aware analytics for smart cities, combining data cleaning, clustering, fairness evaluation, SQL-based sampling, and machine learning modelling.<\/p>\n<h2><a href=\"http:\/\/vargas-solar.com\/data-centric-smart-everything\/egi-smart-analytics-lab-2025\/\" target=\"_blank\" rel=\"noopener\"><strong>EGI Big Data Analytics for Industry 4.0 exercises<\/strong><\/a><\/h2>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Understanding data collections content: a quantitative vision Keep in mind that for performing data analytics you are willing to make sense of data and this implies acquiring Data Literacy. Have a look at this reference for background.&nbsp; Michel Bowen, Anthony Bartley, The Basics of Data Literacy: making your students (and you!) make sens of data, [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width.php","meta":{"footnotes":""},"class_list":["post-588","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/588","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/comments?post=588"}],"version-history":[{"count":2,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/588\/revisions"}],"predecessor-version":[{"id":610,"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/pages\/588\/revisions\/610"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/data-centric-smart-everything\/wp-json\/wp\/v2\/media?parent=588"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}