{"id":7175,"date":"2023-02-02T12:02:02","date_gmt":"2023-02-02T04:02:02","guid":{"rendered":"http:\/\/www.ukm.my\/siswazahfst\/?page_id=7175"},"modified":"2025-07-17T17:24:00","modified_gmt":"2025-07-17T09:24:00","slug":"data-science-and-analytics-v2","status":"publish","type":"page","link":"https:\/\/www.ukm.my\/siswazahfst\/data-science-and-analytics-v2\/","title":{"rendered":"Data Science And Analytics &#8211; v2"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"7175\" class=\"elementor elementor-7175\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-238a0d55 elementor-section-full_width elementor-section-height-min-height elementor-section-height-default elementor-section-items-middle\" data-id=\"238a0d55\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4e7c9eab\" data-id=\"4e7c9eab\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-37b4ed8 elementor-widget elementor-widget-heading\" data-id=\"37b4ed8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">MASTER OF SCIENCE (DATA SCIENCE AND ANALYTICS)\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-28c77c7b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"28c77c7b\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-20c463af\" data-id=\"20c463af\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5e5f58ee elementor-widget elementor-widget-spacer\" data-id=\"5e5f58ee\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1d77e2e5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1d77e2e5\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-7170ca92\" data-id=\"7170ca92\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6f13f070 elementor-widget elementor-widget-text-editor\" data-id=\"6f13f070\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\"><div id=\"elementor-tab-content-1591\" class=\"elementor-tab-content elementor-clearfix elementor-active\" role=\"tabpanel\" data-tab=\"1\" aria-labelledby=\"elementor-tab-title-1591\"><p><span style=\"color: #000000; font-size: 14pt;\">Data science is a multidisciplinary field of study that involves scientific methods, processes and systems in extracting both explicit and implicit information from\u00a0a variety\u00a0of\u00a0data structures. It combines the knowledge of mathematics and statistics,\u00a0 programming and data analytics. This master programme offers a variety of courses with emphasis on data analytics. Students are free to choose from three different learning modules: Data Computing,\u00a0Data Analytic, and\u00a0Finance &amp; Business\u00a0Analytic to match their interests and career\u00a0paths. The aim of the\u00a0programme is\u00a0to produce knowledgeable, ethical and competitive graduates who can contribute to the nations.<\/span><\/p><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-3ba7f635\" data-id=\"3ba7f635\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2bc42ee7 ekit-equal-height-enable elementor-widget elementor-widget-elementskit-image-box\" data-id=\"2bc42ee7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"elementskit-image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"ekit-wid-con\" >\n            <div class=\"elementskit-info-image-box ekit-image-box text-center style-modern\" >\n\n                \n                <div class=\"elementskit-box-header image-box-img-center\">\n\n                    <img decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/www.ukm.my\/siswazahfst\/wp-content\/uploads\/2023\/02\/dr-aftar-e1677916102856-150x150.jpeg\" class=\"attachment-thumbnail size-thumbnail wp-image-7425\" alt=\"\" \/>\n                <\/div>\n                \n                <div class=\"elementskit-box-body ekit-image-box-body\">\n                    <div class=\"elementskit-box-content ekit-image-box-body-inner\">\n                                                <h3 class=\"elementskit-info-box-title\">\n\n                         Assoc. Prof. Dr. Mohd Aftar Abu Bakar\n                        \n                    <\/h3>\n                                                            <div class=\"elementskit-box-style-content\">\n                        Programme Coordinator<br>\naftar@ukm.edu.my                    <\/div>\n                                    <\/div>\n\n                            <\/div>\n            <\/div>\n    <\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9a6707c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9a6707c\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9a90a3b\" data-id=\"9a90a3b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7ed38ca elementor-align-center elementor-widget elementor-widget-button\" data-id=\"7ed38ca\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/join.ukm.my\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Apply Now<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-120752f0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"120752f0\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-55dc1d33\" data-id=\"55dc1d33\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6ba9754d elementor-widget-divider--view-line_icon elementor-view-default elementor-widget-divider--element-align-center elementor-widget elementor-widget-divider\" data-id=\"6ba9754d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon elementor-divider__element\">\n\t\t\t\t\t<i aria-hidden=\"true\" class=\"fas fa-star\"><\/i><\/div>\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-56149c50 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"56149c50\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1827d250\" data-id=\"1827d250\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5a61897e elementor-tabs-alignment-stretch elementor-tabs-view-horizontal elementor-widget elementor-widget-tabs\" data-id=\"5a61897e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-tabs\">\n\t\t\t<div class=\"elementor-tabs-wrapper\" role=\"tablist\" >\n\t\t\t\t\t\t\t\t\t<div id=\"elementor-tab-title-1511\" class=\"elementor-tab-title elementor-tab-desktop-title\" aria-selected=\"true\" data-tab=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"elementor-tab-content-1511\" aria-expanded=\"false\">Programme Structure<\/div>\n\t\t\t\t\t\t\t\t\t<div id=\"elementor-tab-title-1512\" class=\"elementor-tab-title elementor-tab-desktop-title\" aria-selected=\"false\" data-tab=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1512\" aria-expanded=\"false\">Course Synopsis<\/div>\n\t\t\t\t\t\t\t\t\t<div id=\"elementor-tab-title-1513\" class=\"elementor-tab-title elementor-tab-desktop-title\" aria-selected=\"false\" data-tab=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1513\" aria-expanded=\"false\">Entry Requirement<\/div>\n\t\t\t\t\t\t\t\t\t<div id=\"elementor-tab-title-1514\" class=\"elementor-tab-title elementor-tab-desktop-title\" aria-selected=\"false\" data-tab=\"4\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1514\" aria-expanded=\"false\">Career Prospect<\/div>\n\t\t\t\t\t\t\t\t\t<div id=\"elementor-tab-title-1515\" class=\"elementor-tab-title elementor-tab-desktop-title\" aria-selected=\"false\" data-tab=\"5\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1515\" aria-expanded=\"false\">Tuition Fees<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t<div class=\"elementor-tabs-content-wrapper\" role=\"tablist\" aria-orientation=\"vertical\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-tab-title elementor-tab-mobile-title\" aria-selected=\"true\" data-tab=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"elementor-tab-content-1511\" aria-expanded=\"false\">Programme Structure<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-1511\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"tabpanel\" aria-labelledby=\"elementor-tab-title-1511\" tabindex=\"0\" hidden=\"false\"><p><span style=\"font-family: georgia, palatino, serif;\"><strong>Study Duration<br \/><\/strong>Minimum 3 semesters (full time) \/ 5 semesters (part time)<br \/>Maximum 4 semesters (full time) \/ 8 semesters (part time)\u00a0<br \/>*<em>all lectures during weekdays and office hours (including part time)<\/em><\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>Intake<br \/><\/strong>Intake \u2013 every October<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">*subjected to 每日大赛app academic calendar<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>Data Computing Module<\/strong><\/span><\/p><table style=\"border-collapse: collapse; width: 100%; height: 397px;\"><tbody><tr style=\"height: 24px;\"><td style=\"width: 19.9656%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\"><strong>Semester<\/strong><\/span><\/td><td style=\"width: 46.4716%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\"><strong>Core Course<\/strong><\/span><\/td><td style=\"width: 33.2186%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\"><strong>Elective Course<\/strong><\/span><\/td><\/tr><tr style=\"height: 163px;\"><td style=\"width: 19.9656%; height: 163px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">I<\/span><\/td><td style=\"width: 46.4716%; height: 163px; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6014<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Science<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6214<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Mathematical Statistics with Computing<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6414<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Mining<\/span><\/p><\/td><td style=\"width: 33.2186%; height: 363px; border-style: ridge; border-color: #f5f0f0;\" rowspan=\"3\"><p><span style=\"font-family: georgia, palatino, serif;\">Choose four (4):<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6124<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Visualization and Communication<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6324<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Management<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6114<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Unstructured Data Analytics<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQS6444<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Time Series Modelling and Forecasting<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQM6154<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Network Science<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6334<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Multicriteria Decision Making<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6524<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Statistical Methods for Computational Biology<\/span><\/p><\/td><\/tr><tr style=\"height: 138px;\"><td style=\"width: 19.9656%; height: 138px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">II<\/span><\/td><td style=\"width: 46.4716%; height: 138px; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6024<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Machine Learning<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQP6014<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Research Methodology and Industrial Seminar<\/span><\/p><\/td><\/tr><tr style=\"height: 62px;\"><td style=\"width: 19.9656%; height: 62px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">III<\/span><\/td><td style=\"width: 46.4716%; height: 62px; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6889<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Capstone Project<\/span><\/p><\/td><\/tr><tr style=\"height: 10px;\"><td style=\"width: 19.9656%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">Total Credit<\/span><\/td><td style=\"width: 46.4716%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">29<\/span><\/p><\/td><td style=\"width: 33.2186%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">16<\/span><\/p><\/td><\/tr><\/tbody><\/table><p><span style=\"font-family: georgia, palatino, serif;\"><strong>Data Analytic Module<\/strong><\/span><\/p><table style=\"border-collapse: collapse; width: 100%; height: 397px;\"><tbody><tr style=\"height: 24px;\"><td style=\"width: 19.9656%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\"><strong>Semester<\/strong><\/span><\/td><td style=\"width: 46.4716%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\"><strong>Core Course<\/strong><\/span><\/td><td style=\"width: 33.2186%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\"><strong>Elective Course<\/strong><\/span><\/td><\/tr><tr style=\"height: 163px;\"><td style=\"width: 19.9656%; height: 163px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">I<\/span><\/td><td style=\"width: 46.4716%; height: 163px; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6014<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Science<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6214<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Mathematical Statistics with Computing<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6414<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Mining<\/span><\/p><\/td><td style=\"width: 33.2186%; height: 363px; border-style: ridge; border-color: #f5f0f0;\" rowspan=\"3\"><p><span style=\"font-family: georgia, palatino, serif;\">Choose four (4):<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6124<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Visualization and Communication<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQS6284<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Multivariate Analysis<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6114<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Unstructured Data Analytics<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6134<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Business Analytics<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQS6444<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Time Series Modelling and Forecasting<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQS6234<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Bayesian Inference<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQM6154<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Network Science<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6334<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Multicriteria Decision Making<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6524<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Statistical Methods for Computational Biology<\/span><\/p><\/td><\/tr><tr style=\"height: 138px;\"><td style=\"width: 19.9656%; height: 138px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">II<\/span><\/td><td style=\"width: 46.4716%;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6024<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Machine Learning<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQP6014<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Research Methodology and Industrial Seminar<\/span><\/p><\/td><\/tr><tr style=\"height: 62px;\"><td style=\"width: 19.9656%; height: 62px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">III<\/span><\/td><td style=\"width: 46.4716%; height: 62px; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6889<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Capstone Project<\/span><\/p><\/td><\/tr><tr style=\"height: 10px;\"><td style=\"width: 19.9656%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">Total Credit<\/span><\/td><td style=\"width: 46.4716%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">29<\/span><\/p><\/td><td style=\"width: 33.2186%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">16<\/span><\/p><\/td><\/tr><\/tbody><\/table><p><span style=\"font-family: georgia, palatino, serif;\"><strong>Finance and Business Analytic Module\u00a0<\/strong><\/span><\/p><table style=\"border-collapse: collapse; width: 100%; height: 397px;\"><tbody><tr style=\"height: 24px;\"><td style=\"width: 19.9656%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">Semester<\/span><\/td><td style=\"width: 46.4716%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">Core Course<\/span><\/td><td style=\"width: 33.2186%; height: 24px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">Elective Course<\/span><\/td><\/tr><tr style=\"height: 163px;\"><td style=\"width: 19.9656%; height: 163px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">I<\/span><\/td><td style=\"width: 46.4716%; height: 163px; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6014<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Science<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6214<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Mathematical Statistics with Computing<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQD6414<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Data Mining<\/span><\/p><\/td><td style=\"width: 33.2186%; height: 363px; border-style: ridge; border-color: #f5f0f0;\" rowspan=\"3\"><p><span style=\"font-family: georgia, palatino, serif;\">Choose four (4):<\/span><\/p><p>STQD6124<br \/>Data Visualization and Communication<\/p><p>STQD6134<br \/>Business Analytics<\/p><p>TQD6114<br \/>Unstructured Data Analytics<\/p><p>STQS6444<br \/>Time Series Modelling and Forecasting<\/p><p>STQD6334<br \/>Multicriteria Decision Making<\/p><p>STQA6014<br \/>Investment Analysis and Portfolio Management<\/p><p>STQA6034<br \/>Issues in Risk Management and Insurance<\/p><\/td><\/tr><tr style=\"height: 138px;\"><td style=\"width: 19.9656%; height: 138px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">II<\/span><\/td><td style=\"width: 46.4716%; height: 138px;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6024<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Machine Learning<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\">STQP6014<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Research Methodology and Industrial Seminar<\/span><\/p><\/td><\/tr><tr style=\"height: 62px;\"><td style=\"width: 19.9656%; height: 62px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">III<\/span><\/td><td style=\"width: 46.4716%; height: 62px; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">STQD6889<\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Capstone Project<\/span><\/p><\/td><\/tr><tr style=\"height: 10px;\"><td style=\"width: 19.9656%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><span style=\"font-family: georgia, palatino, serif;\">Total Credit<\/span><\/td><td style=\"width: 46.4716%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">29<\/span><\/p><\/td><td style=\"width: 33.2186%; height: 10px; text-align: center; border-style: ridge; border-color: #f5f0f0;\"><p><span style=\"font-family: georgia, palatino, serif;\">16<\/span><\/p><\/td><\/tr><\/tbody><\/table><\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-tab-title elementor-tab-mobile-title\" aria-selected=\"false\" data-tab=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1512\" aria-expanded=\"false\">Course Synopsis<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-1512\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"tabpanel\" aria-labelledby=\"elementor-tab-title-1512\" tabindex=\"0\" hidden=\"hidden\"><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQA6014 Investment Analysis and Portfolio Management<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">The focus of this course is on the investment decision making. It presents the applications of various investment instruments and its role in risk management. The concept of risks and returns are covered comprehensively. Efficient diversification is discussed with the emphasis on the construction of efficient portfolio. The different kinds of investment instruments are assessed and weighted. Share valuation methods and portfolio theories such as the Markowitz theory, the Single Index model, the Capital Asset Pricing Model are discussed. The fundamental and technical analyses are also explained. The behavioral finance theory such as the Efficient Market Hypothesis is included. Students will participate in learning activities consisting of article journal discussion and project presentations.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQA6034 Issues in Risk Management and Insurance<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course has one main objectives; the first is to provide students with a broad perspective of risk management that emphasize traditional risk management and insurance while introducing other types of risk management, while the second is to equip students with the tools needed for the analysis of mathematical models that describe the loss process. The major topics that will be covered are risk management (objectives, measurement, diversification and retention), hedging, corporate risk management, enterprise risk management, estimation methods (for complete and incomplete data) and model selection. The students will also be trained to use R and Excell software for computing relevant mathematical analysis. At the end of semester, students are required to make a presentation on an article from an agreed journal so that they will appreciate the applicability of concepts and methodologies covered in this course.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6014 Data Science<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course aims to expose students to the basic principles of data science and Python programming. Students will be introduced with the concept of big data and the various types of data related to it. This course would also covers the algorithms, processes, methods and analyses used in the field of data science with examples and discussions using Python. Other topics covered are the current data technologies available for storing and archiving data.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6024 Machine Learning<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course aims to expose students on concepts, techniques and algorithms in machine learning. Machine learning revolves around the development of a computer system, which is able to self-learning and improving through experience and recorded data. This course is among main technologies in Big Data and its applications in various fields. Among common topics covered are neural neonerk, decision tree and support vector machines. Among advanced topics covered are ensemble and unsupervised learning also reinforcement and evolutionary learning.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6114 Unstructured Data Analytics<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">The aim of this course is to introduce students to basic and current methods used to compile, summarize and analyze unstructured and semi-structured data. Unstructured data includes texts, images and audios. Focus are given to algorithms and techniques for mining, exploring and analyzing unstructured data using suitable packages. Students are also exposed to sources for unstructured data. Related applications of unstructured data such as sentiment analysis, document clustering and information extraction are also discussed.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6124 Data Visualization and Communication<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course introduces students to the basic principles of data visualization and communication. Students are exposed to the principle of designing visualizations, human perception, colour theory and effective data storytelling. Suitable graphs and charts to convey information clearly are taught. Students will be trained to use visualization softwares such as R, ggplot, MatplotLib, D3 and others. Some specific graphical techniques will be introduces such as visualizing multivariate, time series, spatial, texts, hierarchical and neonerk data.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6134 Business Analytics<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course aims to expose students on the techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. It is divided into customer, operation and people analytics. Customer analytics focuses on how data is used to describe, explain, and predict customer behavior. Meanwhile, operation analytics focuses on how the data can be used to profitably match supply with demand in various business settings. This also covers on how to model future demand uncertainties, how to predict the outcomes of competing policy choices and how to choose the best course of action in the face of risk. Finally, people analytics is a data \u2013 driven approach to managing people at work.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6214 Mathematical Statistics with Computing<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course aims to expose students to the fundamentals of mathematical statistics including descriptive statistics, graphical displays, sampling distributions, hypothesis testing and other methods in data analysis. This course also reflects the integral role of R in computing statistical problems. Basic simulation concepts are discussed with examples. Students will learn how to generate data, analyze data using statistical methods and interpret the results obtained.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6324 Data Management<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course aims to provide the fundamental and state of the art on the technologies used in data management big data solutions. Students will be introduced to data model, databases, querying and big data processing. It covers data security, data centre and the development of big data solutions such as the Hadoop ecosystem, including MapReduce and HDFS. Apache Spark will also be introduced, including Spark\u2019s architecture, data distribution and parallelisation of tasks. Students will have a better understanding on how to optimise the information in the big data using Spark\u2019s memory caching, as well as using the more advanced operations available in Spark.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6334 Multicriteria Decision Making<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">The purpose of this course is to introduce the concepts and techniques in solving Multi-criteria Decision Making problems. The methods to be used to solve the problems depend on the type of problems. Topics included are decision making without probabilities, decision making with probabilities, decision making with sample information, decision making under uncertainties, Analytic Hierarchy Process, TOPSIS, VIKOR, PROMETHEE and ELECTRE.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6414 Data Mining<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course explains in detail about the process of exploration in the database (KDD) and data mining. This course discusses the process of data preparation which includes data cleaning, integration, transformation, reduction and discretization. This course covers the the general concept of data mining process on various types of data stream, sequence, time series, text, spatial and web-data.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6524 Statistical Methods for Computational Biology<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">The aim of this course is to give exposure on statistical methods and computation in biology and bioinformatics. Focus is given on the understanding of basic statistical concepts and inferential statistics as well as their use in solving biological problems. This course covers topics such as introduction to genetic data, gene expression data, DNA sequential data, Protein and RNA, sequential analysis, phylogenetic, gene expression analysis and micro array data analysis. Statistical methods that will be covered are inferential statistics methods, hypothesis testings, multivariate, statistical modelling, experimental design, robust statistical techniques, Bayesian and Markov Chain Monte Carlo.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQD6889 Capstone Project<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">Capstone project provides experiential learning opportunity and gives students space to produce a product which is evaluated by potential employers. The project is obtained from real world problems and executed in collaboration with industry, government or private agencies, or academics. Students will use knowledge and skills which they have obtained throughout their study to help solve real problems. During the course of the project, students will be involved with the whole process of identifying and defining problems, giving solutions and limitations, perform analysis, reporting and presenting results and giving suggestions<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQM6154 Network Science<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course introduces mathematical theories in neonerk science. Neonerk science is a multidiscipline field which investigate problems that can be understood through neonerk approach. Among the aims of neonerk science are to find cross-neonerk equations and increase understanding of systems which are represented by neonerks through data analysis. The use of neonerk science can be found in mathematics, social neonerks, biological systems and transportations<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQP6014 Research Methodology and Industrial Seminar<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">The aim of this course is to give a background and method to perform scientific research in Data Dcience field. Research ethics, research principes, research designs and the role of researhers are discussed. Research methodologies, sampling and data collection as well as critical literature review are exposed to the students. Students will also be exposed to current issues and recent research in Data Science through a series of Data Science Seminar by inviting researchers and main industry practitioners in this field.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQS6234 Bayesian Inference<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course introduces to the students on Bayesian\u2019s theories. Bayesian inference for normal distributions is also discussed. Other than that, Bayesian inference for distributions other than normal, for example Binomial and Poisson is also explained. Other topics include hierarchical Bayesian model, empirical Bayesian, hypothesis testing, correlation, regression and analysis of variance.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQS6284 Multivariate Analysis<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">This course intends to introduce statistical mehods for multivariate data. Students are emphasized on the comprehension of the concepts and theories in multivariate analysis. Among topics covered in this course are matrix algebra, multivariate normal distribution, hypothesis testing for multivariate data, principal component analysia, factor analysis, discriminant analysis and cluster analysis.<\/span><\/p><p><span style=\"font-family: georgia, palatino, serif;\"><strong>STQS6444 Time Series Modelling and Forecasting<\/strong><\/span><br \/><span style=\"font-family: georgia, palatino, serif;\">The objectives of this course are estimating simple regression models, explaining the techniques for modeling trend and volatility in time series data, explaining the cointegrating relation between one or more time series, and at the same time highlighting several major issues in time series analysis that are related to stationarity, trend, volatility, and cointegration. In particular, for modeling trend and volatility, the focus will be on the ARCH-GARCH models. As for cointegration, the error-correction mechanism and the Johansen approach will be discussed. At the end of the semester, the students will be required to write one short report on the application of statistical testing methods and model analyses that are covered during the semester.<\/span><\/p><\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-tab-title elementor-tab-mobile-title\" aria-selected=\"false\" data-tab=\"3\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1513\" aria-expanded=\"false\">Entry Requirement<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-1513\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"tabpanel\" aria-labelledby=\"elementor-tab-title-1513\" tabindex=\"0\" hidden=\"hidden\"><ul><li><span style=\"font-family: georgia, palatino, serif; font-size: 12pt;\">Bachelor\u2019s Degree in relevant field with minimum CGPA 2.50 or equivalent, from any institutions of higher learning\u00a0 recognized by the 每日大赛app Senate;\u00a0<strong>or<\/strong><\/span><\/li><li><span style=\"font-family: georgia, palatino, serif; font-size: 12pt;\">Bachelor\u2019s Degree in relevant field with minimum CGPA 2.00 \u2013 2.49 or equivalent, with minimum of 5 years working experience or research project in relevant field. Proof of the working experience of a foreign candidate should be acknowledge by the embassy of the respective country;\u00a0<strong>or<\/strong><\/span><\/li><li><span style=\"font-family: georgia, palatino, serif; font-size: 12pt;\">Fulfill Accreditation of Prior Experiential Learning (APEL A) for local candidates only:<\/span><ul><li><span style=\"font-family: georgia, palatino, serif; font-size: 12pt;\">should be more than 30 years of age in the year of application; and<\/span><\/li><li><span style=\"font-family: georgia, palatino, serif; font-size: 12pt;\">possess at least STPM or diploma in relevant field or other equivalent qualification recognized by the Government of Malaysia and approved by the 每日大赛app Senate; and<\/span><\/li><li><span style=\"font-family: georgia, palatino, serif; font-size: 12pt;\">possess\u00a0a certificate of MQA APEL with MQF Level 7<\/span><\/li><\/ul><\/li><li>An international student shall obtain minimum results of either<ul><li>TOEFL iBT score 46 <strong>or<\/strong><\/li><li>IELTS band 5.5 <strong>or<\/strong><\/li><li>PTE score 51 <strong>or <\/strong><\/li><li>CEFR band B2 <strong>or<\/strong><\/li><li>MUET band 4 <strong>or<\/strong><\/li><li>Higher Education English Test (HEET) score 7 <strong>or<\/strong><\/li><li>An international student who comes from a country where English is the official language, or\u00a0who has obtained academic qualifications from any institution of higher learning that uses English as the medium of instruction may be exempted from the requirement for HEET \/ TOEFL \/ IELTS)<\/li><\/ul><\/li><\/ul><\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-tab-title elementor-tab-mobile-title\" aria-selected=\"false\" data-tab=\"4\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1514\" aria-expanded=\"false\">Career Prospect<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-1514\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"tabpanel\" aria-labelledby=\"elementor-tab-title-1514\" tabindex=\"0\" hidden=\"hidden\"><ol><li><span style=\"font-family: georgia, palatino, serif;\">Data Scientist<\/span><\/li><li><span style=\"font-family: georgia, palatino, serif;\">Data Analyst<\/span><\/li><li><span style=\"font-family: georgia, palatino, serif;\">Statistician<\/span><\/li><li><span style=\"font-family: georgia, palatino, serif;\">Data Engineer<\/span><\/li><li><span style=\"font-family: georgia, palatino, serif;\">Machine Learning Engineer<\/span><\/li><\/ol><\/div>\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-tab-title elementor-tab-mobile-title\" aria-selected=\"false\" data-tab=\"5\" role=\"tab\" tabindex=\"-1\" aria-controls=\"elementor-tab-content-1515\" aria-expanded=\"false\">Tuition Fees<\/div>\n\t\t\t\t\t<div id=\"elementor-tab-content-1515\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"5\" role=\"tabpanel\" aria-labelledby=\"elementor-tab-title-1515\" tabindex=\"0\" hidden=\"hidden\"><div class=\"header\"><h2 class=\"title\">Local<\/h2><\/div><div class=\"eael-pricing-tag\"><span class=\"price-tag\"><span class=\"original-price\"><span class=\"price-currency\">Full Time<\/span><\/span><\/span><\/div><div class=\"eael-pricing-tag\"><span class=\"price-tag\"><span class=\"original-price\"><span class=\"price-currency\">RM<\/span>9,155.00<\/span><\/span>\u00a0<span class=\"price-period\">\/ study (minimum 3 semesters)<\/span><\/div><div class=\"body\"><ul><li class=\"elementor-repeater-item-1cd676f\">RM 1,010.00 &#8211; Registration Fee (1st semester only)<\/li><li class=\"elementor-repeater-item-45f0684\">RM 420.00 &#8211; Service &amp; Activity Fee (every semester)<\/li><li class=\"elementor-repeater-item-39c6388\">RM 153.00 &#8211; Tuition Fee (per credit)<\/li><li class=\"elementor-repeater-item-5a69660\">RM 1,430.00 + (RM 153.00 x no. of credit) &#8211; total fee for 1st semester<\/li><li class=\"elementor-repeater-item-4bdcb9a\">RM 420.00 + (RM 153.00 x no. of credit) &#8211; total fee for subsequent semester<\/li><\/ul><div class=\"body\"><div class=\"eael-pricing-tag\"><span class=\"price-tag\"><span class=\"original-price\"><span class=\"price-currency\">Part Time<\/span><\/span><\/span><\/div><div class=\"eael-pricing-tag\"><span class=\"price-tag\"><span class=\"original-price\"><span class=\"price-currency\">RM8,770<\/span>.00<\/span><\/span>\u00a0<span class=\"price-period\">\/ study (minimum 5 semesters)<\/span><\/div><div class=\"body\"><ul><li class=\"elementor-repeater-item-1cd676f\">RM 1,085.00 \u2013 Registration Fee (1st semester only)<\/li><li class=\"elementor-repeater-item-45f0684\">RM 200.00 \u2013 Service &amp; Activity Fee (every semester)<\/li><li class=\"elementor-repeater-item-39c6388\">RM 153.00 \u2013 Tuition Fee (per credit)<\/li><li class=\"elementor-repeater-item-5a69660\">RM 1,085.00 + (RM 153.00 x no. of credit) \u2013 total fee for 1st semester<\/li><li class=\"elementor-repeater-item-4bdcb9a\">RM 200.00 + (RM 153.00 x no. of credit) \u2013 total fee for subsequent semester<\/li><\/ul><\/div><\/div><div class=\"header\"><h2 class=\"title\">International<\/h2><\/div><div class=\"eael-pricing-tag\"><span class=\"price-tag\"><span class=\"original-price\"><span class=\"price-currency\">RM<\/span>37,620.00<\/span><\/span>\u00a0<span class=\"price-period\">\/ study (minimum 3 semesters)<\/span><\/div><div class=\"body\"><ul><li class=\"elementor-repeater-item-1cd676f\">RM 1,410.00 &#8211; Registration Fee (1st semester only)<\/li><li class=\"elementor-repeater-item-39c6388\">RM 12,070.00 &#8211; total fee\u00a0<\/li><li class=\"elementor-repeater-item-4bdcb9a\">RM 13,480.00 &#8211; total fee for 1st semester<\/li><li>RM 12,070.00 &#8211; total fee for subsequent semester<\/li><\/ul><\/div><\/div><\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>MASTER OF SCIENCE (DATA SCIENCE AND ANALYTICS) Data science is a multidisciplinary field of study that involves scientific methods, processes and systems in extracting both<a class=\"ut-readmore\" href=\"https:\/\/www.ukm.my\/siswazahfst\/data-science-and-analytics-v2\/\"> &#8230;<\/a><\/p>\n","protected":false},"author":101012,"featured_media":0,"parent":0,"menu_order":209,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":""},"class_list":["post-7175","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/pages\/7175","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/users\/101012"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/comments?post=7175"}],"version-history":[{"count":103,"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/pages\/7175\/revisions"}],"predecessor-version":[{"id":14974,"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/pages\/7175\/revisions\/14974"}],"wp:attachment":[{"href":"https:\/\/www.ukm.my\/siswazahfst\/wp-json\/wp\/v2\/media?parent=7175"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}