{"id":11,"date":"2019-12-14T13:16:57","date_gmt":"2019-12-14T13:16:57","guid":{"rendered":"http:\/\/vargas-solar.com\/data-ml-studios\/?page_id=11"},"modified":"2020-01-24T15:11:24","modified_gmt":"2020-01-24T15:11:24","slug":"content","status":"publish","type":"page","link":"http:\/\/vargas-solar.com\/data-ml-studios\/content\/","title":{"rendered":"CONTENT"},"content":{"rendered":"\n<ol class=\"wp-block-list\"><li><strong>Introduction<\/strong> [<a rel=\"noreferrer noopener\" aria-label=\"PDF (opens in a new tab)\" href=\"http:\/\/vargas-solar.com\/data-ml-studios\/wp-content\/uploads\/sites\/43\/2020\/01\/1.-Introduction.pdf\" target=\"_blank\">PDF<\/a>]<ol><li>Data centric sciences: principles &amp; common aspects<\/li><li>Digital data collections: characteristics &amp; properties<\/li><li>Data science: big data, data analytics algorithms &amp; tools<\/li><\/ol><\/li><li><strong>From centralized to high scale WIDES, data science laboratories to artificial intelligences studios<\/strong> [<a rel=\"noreferrer noopener\" aria-label=\"PDF (opens in a new tab)\" href=\"http:\/\/vargas-solar.com\/data-ml-studios\/wp-content\/uploads\/sites\/43\/2020\/01\/2.WIDES-platforms.pdf\" target=\"_blank\">PDF<\/a>]<ol><li>In house data analytics environments: Jupyter<\/li><li>Targeting large scale: Zeppelin, Spark [<a href=\"http:\/\/vargas-solar.com\/data-ml-studios\/wp-content\/uploads\/sites\/43\/2020\/01\/spark.pdf\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"PDF (opens in a new tab)\">PDF<\/a>]<\/li><li>Data science virtual machines: cloud solutions<\/li><li>Data science labs: CoLab, Kaggle, Azure Notebooks<\/li><li>Artificial intelligences execution environments &amp; studios: tensor, caf\u00e9, Azure ML studio<\/li><\/ol><\/li><li><strong>Designing experiments environments<\/strong><ol><li> Data labs: data collections, quality, &amp; profiling <\/li><li>Architectural settings: from in house to large scale experiments<ul><li>Parallel execution platforms &amp; environments<\/li><li>Multi-core programming <\/li><li>Externalising computation<\/li><\/ul><\/li><\/ol><\/li><li><strong>Data engineering<\/strong> [<a rel=\"noreferrer noopener\" aria-label=\"PDF (opens in a new tab)\" href=\"http:\/\/vargas-solar.com\/data-ml-studios\/wp-content\/uploads\/sites\/43\/2020\/01\/4.Super-Unsupervised-learning.pdf\" target=\"_blank\">PDF<\/a>] [<a rel=\"noreferrer noopener\" aria-label=\"Data (opens in a new tab)\" href=\"http:\/\/vargas-solar.com\/data-ml-studios\/wp-content\/uploads\/sites\/43\/2020\/01\/ACCIDENTS_GU_BCN_2010.csv\" target=\"_blank\">Data<\/a>]<br>a. Data formats, transformations, distribution<br>b. Studying data quality<br>c. Statistical properties<br>d. Techniques for adjusting data and building data samples<br>e. An overview of applied mathematics to machine learning<\/li><li><strong>Designing data science pipelines<\/strong> [[<a rel=\"noreferrer noopener\" href=\"http:\/\/vargas-solar.com\/data-ml-studios\/wp-content\/uploads\/sites\/43\/2020\/01\/4.Super-Unsupervised-learning.pdf\" target=\"_blank\">PDF<\/a>]<ol><li>Linear regression<ul><li>Simple linear regression<\/li><li>Multiple linear regression &amp; polynomial regression<\/li><li>Sparse model<\/li><\/ul><\/li><li>Logistic regression<\/li><li>Supervised &amp; unsupervised learning projects <ul><li>Learning curves<\/li><li>Training, validation &amp; test <\/li><li>Two learning models<ul><li>Super vector machines <\/li><li>Random forest<\/li><\/ul><\/li><\/ul><\/li><li>Clustering<ul><li>Assessing clustering: metrics <\/li><li>Techniques taxonomy<\/li><\/ul><\/li><li>Graph processing: network science<ul><li>Background on networks and graphs<\/li><li>Graph operation<\/li><li>Similarity &amp; distances<\/li><\/ul><\/li><\/ol><\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Introduction [PDF] Data centric sciences: principles &amp; common aspects Digital data collections: characteristics &amp; properties Data science: big data, data analytics algorithms &amp; tools From centralized to high scale WIDES, data science laboratories to artificial intelligences studios [PDF] In house data analytics environments: Jupyter Targeting large scale: Zeppelin, Spark [PDF] Data science virtual machines: cloud [&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-11","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/pages\/11","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":12,"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":131,"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/pages\/11\/revisions\/131"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}