{"id":51,"date":"2020-01-19T21:35:08","date_gmt":"2020-01-19T21:35:08","guid":{"rendered":"http:\/\/vargas-solar.com\/data-ml-studios\/?page_id=51"},"modified":"2020-01-21T14:33:51","modified_gmt":"2020-01-21T14:33:51","slug":"ho-1-getting-acquainted-to-notebooks-for-developing-data-science-projects","status":"publish","type":"page","link":"http:\/\/vargas-solar.com\/data-ml-studios\/ho-1-getting-acquainted-to-notebooks-for-developing-data-science-projects\/","title":{"rendered":"HO-1: Getting acquainted to notebooks for developing data science projects"},"content":{"rendered":"\n<h4 class=\"wp-block-heading\">Objective<\/h4>\n\n\n\n<ol class=\"wp-block-list\"><li>Understand the different tools available in a WIDE for developing data science projects.<\/li><li>Recall basic concepts of Descriptive Statistics and apply them to data exploration tasks.<\/li><\/ol>\n\n\n\n<h4 class=\"wp-block-heading\">Material<\/h4>\n\n\n\n<ul class=\"wp-block-list\"><li>Azure Notebooks account (<a rel=\"noreferrer noopener\" aria-label=\"https:\/\/notebooks.azure.com (opens in a new tab)\" href=\"https:\/\/notebooks.azure.com\" target=\"_blank\">https:\/\/notebooks.azure.com<\/a>)<ul><li>Follow the\u00a0<a rel=\"noreferrer noopener\" aria-label=\"here\u00a0 (opens in a new tab)\" href=\"http:\/\/vargas-solar.com\/data-ml-studios\/getting-started-on-azure-notebooks\/\" target=\"_blank\">here\u00a0<\/a>instructions for starting\u00a0<a rel=\"noreferrer noopener\" href=\"https:\/\/notebooks.azure.com\" target=\"_blank\">Microsoft Azure Notebooks<\/a>\u00a0and get started for executing the memento and hands-on exercise.<\/li><\/ul><\/li><li>Basic knowledge of Pandas in Python. If not have a look at  [<a href=\"http:\/\/vargas-solar.com\/data-centric-smart-everything\/hands-on\/getting-started-with-the-data-science-ecosystem\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Pandas memento (opens in a new tab)\">Pandas memento<\/a>] [<a href=\"https:\/\/github.com\/javieraespinosa\/ml-studios\/blob\/master\/ch02_Toolbox.ipynb\">ch02_Toolbox.ipynb<\/a>]<\/li><li>Understanding the Gender Divide at Work [<a href=\"https:\/\/github.com\/javieraespinosa\/ml-studios\/blob\/master\/ch03_Descriptive_Statistics.ipynb\">ch03_Descriptive_Statistics.ipynb<\/a>].<\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">ToDo<\/h4>\n\n\n\n<ol class=\"wp-block-list\"><li>Create a project on azure notebooks devoted to work for this lecture with the material available on github.<\/li><li>Have a look at the notebook that implements the analysis for Understanding the Gender Divide at Work<\/li><li>Going through the notebook while executing it, have a look at the use of descriptive analytics concepts for exploring data.<ul><li>Explain the use of statistical measures for understanding data collections.<\/li><li>Explain the importance of computing data distributions for the attributes of the data sets.<\/li><li>Explain why is it important to observe outliers in the analysis. How do you technically observe outliers?<\/li><li>Which is the strategy to measure the relative risk of early promotion? How did you processed the data to evaluate this risk?<\/li><\/ul><\/li><\/ol>\n\n\n\n<h4 class=\"wp-block-heading\">To Hand In<\/h4>\n\n\n\n<ol class=\"wp-block-list\"><li>Use the notebook that you analyse and add at the end of the document using MarkDown a section where you explain the use of descriptive analytics in data exploration<\/li><li>Create a figure of the pipeline you extracted and add it to your notebook<\/li><li>Send the modified notebook to genoveva.vargas@gmail.com<\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Objective Understand the different tools available in a WIDE for developing data science projects. Recall basic concepts of Descriptive Statistics and apply them to data exploration tasks. Material Azure Notebooks account (https:\/\/notebooks.azure.com) Follow the&nbsp;here&nbsp;instructions for starting&nbsp;Microsoft Azure Notebooks&nbsp;and get started for executing the memento and hands-on exercise. Basic knowledge of Pandas in Python. If not [&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-51","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/pages\/51","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=51"}],"version-history":[{"count":14,"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/pages\/51\/revisions"}],"predecessor-version":[{"id":110,"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/pages\/51\/revisions\/110"}],"wp:attachment":[{"href":"http:\/\/vargas-solar.com\/data-ml-studios\/wp-json\/wp\/v2\/media?parent=51"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}