{"id":3984,"date":"2022-06-14T08:40:37","date_gmt":"2022-06-14T08:40:37","guid":{"rendered":"http:\/\/embt-college.org\/embt1\/?post_type=product&#038;p=3984"},"modified":"2022-08-08T20:44:33","modified_gmt":"2022-08-08T20:44:33","slug":"certified-data-scientist-1","status":"publish","type":"product","link":"https:\/\/embt-college.org\/embt1\/product\/certified-data-scientist-1\/","title":{"rendered":"Certified Data Scientist 1"},"content":{"rendered":"<p>[vc_row css=&#8221;.vc_custom_1499953847063{padding-top: 11px !important;}&#8221;][vc_column][vc_column_text]Covers Sections 1 &amp; 2 of the ICDSP Certification training 1. Accessing and Manipulating Data 1.1 Preparing data for analysis and modelling 1.2 Developing optimal data structures 1.3 Transforming data into usable datasets 1.4 Exploratory data analysis including identification of anomalies and outliers 1.5 Handling missing data &#8211; imputation and other methods 2. Data Mining and Modelling: Supervised and Unsupervised Learning Systems 2.1 Classification methods 2.2 Regression methods \u2013 linear, logistic and non-linear regression models 2.3 Time-series forecasting methods 2.4 Ensemble methods e.g. Boosted Decision Trees and Forests 2.5 Association-based data mining schemes 2.6 Unsupervised learning through clustering segmentation[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Covers Sections 1 &amp; 2 of the ICDSP Certification training<\/p>\n<p>1. Accessing and Manipulating Data<\/p>\n<p style=\"padding-left: 40px;\">1.1 Preparing data for analysis and modelling<\/p>\n<p style=\"padding-left: 40px;\">1.2 Developing optimal data structures<\/p>\n<p style=\"padding-left: 40px;\">1.3 Transforming data into usable datasets<\/p>\n<p style=\"padding-left: 40px;\">1.4 Exploratory data analysis including identification of anomalies and outliers<\/p>\n<p style=\"padding-left: 40px;\">1.5 Handling missing data &#8211; imputation and other methods<\/p>\n<hr \/>\n<p>2. Data Mining and Modelling: Supervised and Unsupervised Learning Systems<\/p>\n<p style=\"padding-left: 40px;\">2.1 Classification methods<\/p>\n<p style=\"padding-left: 40px;\">2.2 Regression methods \u2013 linear, logistic and non-linear regression models<\/p>\n<p style=\"padding-left: 40px;\">2.3 Time-series forecasting methods<\/p>\n<p style=\"padding-left: 40px;\">2.4 Ensemble methods e.g. Boosted Decision Trees and Forests<\/p>\n<p style=\"padding-left: 40px;\">2.5 Association-based data mining schemes<\/p>\n<p style=\"padding-left: 40px;\">2.6 Unsupervised learning through clustering segmentation<\/p>\n<p>The online course features twice weekly synchronous online sessions (2 hours per session), over an 8-week period (32 hours total course contact duration).<\/p>\n","protected":false},"featured_media":3985,"template":"","meta":{"spay_email":""},"product_cat":[152],"product_tag":[149,150,158,151,148],"class_list":["post-3984","product","type-product","status-publish","has-post-thumbnail","hentry","product_cat-online-training-certification","product_tag-certification","product_tag-embt","product_tag-icds","product_tag-idesa","product_tag-it"],"_links":{"self":[{"href":"https:\/\/embt-college.org\/embt1\/wp-json\/wp\/v2\/product\/3984","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/embt-college.org\/embt1\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/embt-college.org\/embt1\/wp-json\/wp\/v2\/types\/product"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/embt-college.org\/embt1\/wp-json\/wp\/v2\/media\/3985"}],"wp:attachment":[{"href":"https:\/\/embt-college.org\/embt1\/wp-json\/wp\/v2\/media?parent=3984"}],"wp:term":[{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/embt-college.org\/embt1\/wp-json\/wp\/v2\/product_cat?post=3984"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/embt-college.org\/embt1\/wp-json\/wp\/v2\/product_tag?post=3984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}