{"product_id":"intro-to-data-mining","title":"Data Analytics for Decision Makers","description":"\u003cp\u003e\u003cstrong\u003eCourse Description\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003eIn this course, you will learn data mining concepts. These concepts include pattern recognition, visualization, and artificial intelligence. Additionally, you will have a few “hands-on” opportunities using tools to assist in these effects.\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003eThis is an asynchronous, self-paced online course with no live instructor. The schedule is flexible, allowing learners to complete coursework and assignments on their own time.\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003e\u003cstrong\u003eNote: Learners will be awarded a Purdue course completion certificate upon submitting the four modules' knowledge check with an 80% completion score. \u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003e\u003cstrong\u003eLearning Outcomes\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cul style=\"list-style-type:disc;\"\u003e\n\u003cli\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003eExamine foundational concepts in data mining.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003eDifferentiate between descriptive and predictive elements of data mining.\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003eContrast the strengths and weaknesses of supervised and unsupervised methods.\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cdiv style=\"background-color:white;margin-left:.25in;margin-right:-6.0pt;\"\u003e\u003cp style=\"background-color:white;\"\u003e \u003c\/p\u003e\u003c\/div\u003e\u003cp\u003e\u003cspan style=\"background-color:white;color:#1F1F1F;\"\u003e\u003cstrong\u003eCourse Schedule and Duration\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"background-color:white;color:#373A3C;\"\u003eBelow is the course schedule. It is recommended that you work through one module per week. The entire course is estimated to take 15 hours to complete. Each module is estimated to take 15-18 hours to complete. The Purdue certificate is delivered within the Awards tool in the Brightspace course. Schedule and assignments are subject to change. Any changes will be posted in Brightspace.\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp style=\"background-color:white;\"\u003e\u003cspan style=\"background-color:white;color:#373A3C;\"\u003e\u003cstrong\u003eTarget Audience\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\u003cp style=\"background-color:white;\"\u003e\u003cspan style=\"background-color:white;color:#373A3C;\"\u003eSales engineers\/business development executives, college students, and anyone interested in learning more about data mining.\u003c\/span\u003e\u003c\/p\u003e\u003cfigure class=\"table\" style=\"width:481.6pt;\"\u003e\u003ctable style=\"-webkit-text-stroke-width:0px;background-color:rgb(255, 255, 255);border-collapse:collapse;box-sizing:border-box;color:rgb(65, 65, 65);empty-cells:show;font-family:sans-serif;font-size:14px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;max-width:100%;orphans:2;text-align:left;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-transform:none;white-space:normal;widows:2;word-spacing:0px;\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\u003ctbody style=\"box-sizing:border-box;\"\u003e\n\u003ctr style=\"box-sizing:border-box;height:13.05pt;\"\u003e\n\u003ctd style=\"border:1pt solid rgb(0, 0, 0);box-sizing:border-box;height:13.05pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e\u003cstrong style=\"box-sizing:border-box;\"\u003eModule\u003c\/strong\u003e\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top:1pt solid rgb(0, 0, 0);box-sizing:border-box;height:13.05pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e\u003cstrong style=\"box-sizing:border-box;\"\u003eTopic \u0026amp; Readings\u003c\/strong\u003e\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top:1pt solid rgb(0, 0, 0);box-sizing:border-box;height:13.05pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.6pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e\u003cstrong style=\"box-sizing:border-box;\"\u003eAssignments\u003c\/strong\u003e\u003c\/p\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:12.1pt;\"\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left:1pt solid rgb(0, 0, 0);border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:12.1pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" rowspan=\"3\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e1 – Foundations of Data Mining\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:12.1pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 1: Data Concepts 1\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:12.1pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.6pt;\" rowspan=\"3\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003eModule 1 Quiz – 10 points\u003c\/p\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:8.35pt;\"\u003e\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:8.35pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 2: Data Concepts 2\u003c\/p\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:8.3pt;\"\u003e\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:8.3pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 3: Data Quality\u003c\/p\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:16.15pt;\"\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left:1pt solid rgb(0, 0, 0);border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:16.15pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003e \u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:16.15pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003e \u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:16.15pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.6pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e \u003c\/p\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:10.45pt;\"\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left:1pt solid rgb(0, 0, 0);border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:10.45pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" rowspan=\"3\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e2 – Components of Data Mining\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:10.45pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 4: Pattern Recognition\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:10.45pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.6pt;\" rowspan=\"3\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003eModule 2 Quiz – 10 points\u003c\/p\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:8.35pt;\"\u003e\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:8.35pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 5: Visualization\u003c\/p\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:8.35pt;\"\u003e\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:8.35pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 6: Large-scale Data\u003c\/p\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:14.35pt;\"\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left:1pt solid rgb(0, 0, 0);border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:14.35pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003e \u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:14.35pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003e \u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:14.35pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.6pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e \u003c\/p\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:9.85pt;\"\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left:1pt solid rgb(0, 0, 0);border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:9.85pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" rowspan=\"3\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003e3 – Methods for Data Mining\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:9.85pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 7: Supervised Machine Learning\u003c\/p\u003e\u003c\/td\u003e\n\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:9.85pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.6pt;\" rowspan=\"3\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;text-align:center;\"\u003eModule 3 Quiz – 10 points\u003c\/p\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:4.05pt;\"\u003e\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:4.05pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 8: Unsupervised Machine Learning\u003c\/p\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr style=\"box-sizing:border-box;height:8.35pt;\"\u003e\u003ctd style=\"border-bottom:1pt solid rgb(0, 0, 0);border-image:initial;border-left-style:none;border-right:1pt solid rgb(0, 0, 0);border-top-style:none;box-sizing:border-box;height:8.35pt;min-width:5px;padding:0px 5.4pt;user-select:text;vertical-align:top;width:160.5pt;\" width=\"214\"\u003e\u003cp style=\"box-sizing:border-box;line-height:normal;margin-top:0px;\"\u003eTopic 9: Deep Learning\u003c\/p\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/figure\u003e","brand":"Purdue University","offers":[{"title":"Default Title","offer_id":43106835103811,"sku":"18233","price":129.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0567\/3402\/3747\/files\/800x800_Purdue_IntroToDataMining.png?v=1776700947","url":"https:\/\/store-dev2.semi.org\/products\/intro-to-data-mining","provider":"SEMI Dev 2","version":"1.0","type":"link"}