[{"command":"settings","settings":{"basePath":"\/","pathPrefix":"es\/","ajaxPageState":{"theme":"andes","theme_token":"uVAOJy0Lay7AgL3QZpgndrdyVlxUruI-8CevxerhOSE"},"googleCSE":{"cx":" 018231938674625582243:gwfydvaxzyg","language":"","resultsWidth":600,"domain":"www.google.com","showWaterMark":0},"CToolsModal":{"modalSize":{"type":"scale","width":".9","height":".9","addWidth":0,"addHeight":0,"contentRight":25,"contentBottom":75},"modalOptions":{"opacity":".55","background-color":"#FFF"},"animationSpeed":"fast","modalTheme":"CToolsModalDialog","throbberTheme":"CToolsModalThrobber"},"panopoly_magic":{"pane_add_preview_mode":"single"}},"merge":true},{"command":"insert","method":"replaceWith","selector":"section.content","data":"\u003Csection id=\u0027curriculum-3\u0027class=\u0027content\u0027\u003E\u003Ch1 id=\u0027node-5460\u0027 class=\u0027content-item\u0027\u003ECurriculum\u003C\/h1\u003E\u003Cp\u003EMaster program in Data Analytics Intelligence (MIAD) of UniAndes \u2013 Coursera consists of 36 academic credits (30 mandatory credits and 6 electives credits), divided into 4 MasterTrack. The curriculum can be completed in 2 years (24 months). Each MasterTrack has 2 cycles of 8 weeks. In each cycle, students will take 2 courses simultaneously. The curriculum has the following courses divided by MasterTrack:\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EMasterTrack 1: Analytics Fundamentals (8 credits)\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 1: Decision analysis \/\/ Computational analytics lab.\u003C\/p\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 2: Data modeling and ETL \/\/ Statistical analysis models\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EMasterTrack 2: Basic Analytical Skills (10 credits)\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 1: Data Visualization and Storytelling \/\/ Introduction to Machine Learning\u003C\/p\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 2: Machine Learning and Natural Language Processing (NLP) \/\/ Optimization\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EMasterTrack 3: Advanced Analytics Skills (9 credits)\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 1: Students can choose between: System Dynamics or Simulation \/\/ Unsupervised Learning\u003C\/p\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 2: Deployment of Analytical Solutions \/\/ Analytics Project Management\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EMasterTrack 4: Advanced Analytics Applications and Techniques (9 credits)\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 1: Elective 1 \/\/ Elective 2\u003C\/p\u003E\r\n\r\n\u003Cp class=\u0022rteindent2\u0022\u003ECycle 2: Elective 3 \/\/ Master Project\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe student will be guided by professors and tutors with wide experience in data science.\u003C\/p\u003E\r\n\u003C\/section\u003E","settings":null}]