{"id":23883,"date":"2026-10-19T14:31:16","date_gmt":"2026-10-19T18:31:16","guid":{"rendered":"https:\/\/dcp.ufl.edu\/urp\/?page_id=23883"},"modified":"2026-10-19T15:41:35","modified_gmt":"2026-10-19T19:41:35","slug":"msua-program-requirements","status":"publish","type":"page","link":"https:\/\/dcp.ufl.edu\/urp\/msua-program-requirements\/","title":{"rendered":"MSUA Program Requirements"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<!-- MSUA Program Requirements: paste into a WordPress \"Custom HTML\" block -->\n<style>\n.msua{--ink:#101010;--muted:#4c4c4c;--line:#dedede;--stripe:#f4f3f0;font-family:Arial,Helvetica,sans-serif;color:var(--ink);font-size:16px;line-height:1.58;max-width:1080px;margin:0 auto}\n.msua *,.msua *::before,.msua *::after{box-sizing:border-box}\n.msua a{color:#005288;text-underline-offset:3px}\n.msua a:focus-visible,.msua .table-scroll:focus-visible{outline:3px solid #005288;outline-offset:4px}\n.msua .eyebrow{display:flex;justify-content:space-between;gap:18px;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:1.3px;padding-bottom:15px;border-bottom:2px solid #171717}\n.msua .draft{white-space:nowrap}\n.msua .msua-title{font-size:35px;line-height:1.16;letter-spacing:-.7px;margin:30px 0 14px;padding:0}\n.msua h3{font-size:25px;line-height:1.25;margin:45px 0 16px;padding:0}\n.msua h4{font-size:19px;line-height:1.35;margin:28px 0 12px;padding:0}\n.msua p{margin:10px 0 16px}\n.msua .lead{font-size:18px;max-width:940px}\n.msua .jump{display:flex;flex-wrap:wrap;gap:9px 24px;margin:24px 0 26px;padding-bottom:18px;border-bottom:1px solid var(--line);font-size:14px}\n.msua .mobile-hint{display:none}\n.msua .table-scroll{width:100%;overflow-x:auto;margin:12px 0 18px}\n.msua table{width:100%;border-collapse:collapse;table-layout:fixed;text-align:left;font-size:15px;margin:0;border:0}\n.msua th,.msua td{padding:15px 16px;vertical-align:top;overflow-wrap:anywhere;border:0;text-align:left}\n.msua thead th{white-space:nowrap;border-bottom:1px solid #b9b9b9;font-weight:700;font-size:14px;background:transparent}\n.msua tbody th{font-weight:400;background:transparent}\n.msua tbody tr{background:transparent}\n.msua tbody tr:nth-child(even){background:var(--stripe)}\n.msua .overview tbody th{font-weight:700}\n.msua .overview th:nth-child(1){width:31%}\n.msua .overview th:nth-child(2){width:13%}\n.msua .courses th:nth-child(1){width:13%}\n.msua .courses th:nth-child(2){width:54%}\n.msua .courses th:nth-child(3){width:11%}\n.msua .courses tbody th{white-space:nowrap}\n.msua .plan th:nth-child(1){width:24%}\n.msua .plan th:nth-child(2){width:66%}\n.msua .plan th:nth-child(3){width:10%}\n.msua .plan tbody th{font-weight:700}\n.msua .smallcourses th:nth-child(1){width:20%}\n.msua .smallcourses th:nth-child(2){width:65%}\n.msua .total{border-top:1px solid #b9b9b9;font-weight:700}\n.msua .total th{font-weight:700}\n.msua .note{color:var(--muted);font-size:13px;line-height:1.55}\n.msua .notes{padding:0;margin:15px 0}\n.msua .notes p{margin:7px 0;font-size:14px}\n.msua .rule{font-size:15px}\n.msua section{scroll-margin-top:80px}\n@media(max-width:650px){\n.msua .mobile-hint{display:block;font-size:12px;color:#555;margin:12px 0 0}\n.msua .msua-title{font-size:29px}\n.msua h3{font-size:23px}\n.msua .eyebrow{font-size:10px;letter-spacing:.8px}\n.msua .lead{font-size:16px}\n.msua th,.msua td{padding:11px 12px}\n.msua table{min-width:610px}\n.msua .overview{min-width:520px}\n.msua .plan{min-width:590px}\n.msua .smallcourses{min-width:490px}\n.msua .jump{gap:9px 16px}\n}\n@media print{\n.msua{max-width:none;font-size:10pt}\n.msua .jump{display:none}\n.msua .table-scroll{overflow:visible}\n.msua table{font-size:9pt}\n.msua th,.msua td{padding:8px}\n.msua thead{display:table-header-group}\n.msua tr{break-inside:avoid}\n.msua h3,.msua h4{break-after:avoid}\n}\n<\/style>\n\n<div class=\"msua\">\n<div class=\"eyebrow\"><span>University of Florida \u00b7 Urban Analytics<\/span><span class=\"draft\"><\/span><\/div>\n<header><h2 class=\"msua-title\">MSUA Program Requirements<\/h2><p class=\"lead\">The Master of Science in Urban Analytics (MSUA) requires the completion of 30 credits. The curriculum includes required core courses &#8211; including a 6-credit Master Research Project &#8211; and electives in urban and regional planning and machine learning.<\/p><\/header>\n<nav aria-label=\"Curriculum sections\" class=\"jump\"><a href=\"#msua-overview\">Requirements overview<\/a><a href=\"#msua-core\">Core courses<\/a><a href=\"#msua-electives\">Electives<\/a><a href=\"#msua-plans\">Model semester plan<\/a><\/nav>\n\n<section id=\"msua-overview\"><h3>Program Requirements at a Glance<\/h3>\n<p class=\"mobile-hint\">Swipe horizontally to view all table columns.<\/p><div aria-label=\"Program requirements overview\" class=\"table-scroll\" role=\"region\" tabindex=\"0\"><table class=\"overview\"><thead><tr><th scope=\"col\">Requirement<\/th><th scope=\"col\">Credits<\/th><th scope=\"col\">Details<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Required Core Courses<\/th><td>18<\/td><td>Urban data structures, urban and regional planning, introductory and intermediate urban analytics, and a 6-credit Master Research Project.<\/td><\/tr><tr><th scope=\"row\">Elective Group 1: Urban and Regional Planning<\/th><td>6 or 9<\/td><td>Courses in planning information systems, geospatial modeling, spatial analysis, and transportation and land use modeling. A proposed AI and Cities course is also listed.<\/td><\/tr><tr><th scope=\"row\">Elective Group 2: Machine Learning (ML)<\/th><td>6 or 9<\/td><td>Courses in machine learning, pattern recognition, intelligent systems, and related applications.<\/td><\/tr><tr class=\"total\"><th scope=\"row\">Total Required Credits<\/th><td>30<\/td><td>The required elective allocation depends on the student\u2019s academic background; see the group-specific rules below.<\/td><\/tr><\/tbody><\/table><\/div>\n<p class=\"note\">The five listed required core courses total 18 credits before any applicable course waivers.<\/p><\/section>\n\n<section id=\"msua-core\"><h3>Required Core Courses<\/h3><p><strong>18 credits<\/strong>.<\/p>\n<p class=\"mobile-hint\">Swipe horizontally to view all table columns.<\/p><div aria-label=\"Required core courses\" class=\"table-scroll\" role=\"region\" tabindex=\"0\"><table class=\"courses\"><thead><tr><th scope=\"col\">Course<\/th><th scope=\"col\">Title<\/th><th scope=\"col\">Credits<\/th><th scope=\"col\">Term<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">URP 6295<\/th><td>Urban Data Structures Basics\u00b9<\/td><td>3<\/td><td>Fall<\/td><\/tr><tr><th scope=\"row\">URP 6007<\/th><td>Survey in Urban and Regional Planning\u00b2<\/td><td>3<\/td><td>Fall<\/td><\/tr><tr><th scope=\"row\">URP 6223<\/th><td>Introduction to Urban Analytics\u00b3<\/td><td>3<\/td><td>Fall<\/td><\/tr><tr><th scope=\"row\">URP 6224<\/th><td>Intermediate Urban Analytics<\/td><td>3<\/td><td>Spring<\/td><\/tr><tr><th scope=\"row\">URP 6979<\/th><td>Master Research Project\u2074<\/td><td>6<\/td><td>Fall and Spring\u2074<\/td><\/tr><\/tbody><\/table><\/div>\n<div class=\"notes\"><p><strong>1. URP 6295:<\/strong> Waived for students with a computer science or similar background.<\/p><p><strong>2. URP 6007:<\/strong> Waived for students with a planning or similar background.<\/p><p><strong>3. URP 6223:<\/strong> May be replaced by URP 6931 \u2013 Artificial Intelligence in the Built Environment with permission from the program coordinator.<\/p><p><strong>4. URP 6979:<\/strong> Ideally completed as 3 credits in a fall semester and 3 credits in a spring semester.<\/p><\/div><\/section>\n\n<section id=\"msua-electives\"><h3>Elective Course Groups<\/h3><h4>Group 1: Urban and Regional Planning &#8211; 6 or 9 credits<\/h4>\n<p class=\"rule\">9 credits are required for students without an urban planning background; 6 credits are required for students with an urban planning or similar background.<\/p>\n<p class=\"mobile-hint\">Swipe horizontally to view all table columns.<\/p><div aria-label=\"Urban and regional planning electives\" class=\"table-scroll\" role=\"region\" tabindex=\"0\"><table class=\"courses\"><thead><tr><th scope=\"col\">Course<\/th><th scope=\"col\">Title<\/th><th scope=\"col\">Credits<\/th><th scope=\"col\">Term<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">URP 6270<\/th><td>Introduction to Planning Information Systems<\/td><td>3<\/td><td>Fall<\/td><\/tr><tr><th scope=\"row\">URP 6271<\/th><td>Automation for Geospatial Modeling and Analysis<\/td><td>3<\/td><td>To be determined<\/td><\/tr><tr><th scope=\"row\">URP 6272<\/th><td>Urban Spatial Analysis<\/td><td>3<\/td><td>Spring<\/td><\/tr><tr><th scope=\"row\">URP 6821<\/th><td>Transportation and Land Use Modeling<\/td><td>3<\/td><td>Spring<\/td><\/tr><tr><th scope=\"row\">URP 6726<\/th><td>AI and Cities: How Machines See, Read, and Shape Urban Futures<\/td><td>3<\/td><td>Fall<\/td><\/tr><\/tbody><\/table><\/div>\n\n<h4>Group 2: Machine Learning (ML) &#8211; 6 or 9 credits<\/h4>\n<p class=\"rule\">9 credits are required for students without a computer science background; 6 credits are required for students with a computer science background.<\/p>\n<p class=\"mobile-hint\">Swipe horizontally to view all table columns.<\/p><div aria-label=\"Machine learning electives\" class=\"table-scroll\" role=\"region\" tabindex=\"0\"><table class=\"courses\"><thead><tr><th scope=\"col\">Course<\/th><th scope=\"col\">Title<\/th><th scope=\"col\">Credits<\/th><th scope=\"col\">Term<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">EEL 5840<\/th><td>Fundamentals of Machine Learning<\/td><td>3<\/td><td>Fall and Spring<\/td><\/tr><tr><th scope=\"row\">EEL 6825<\/th><td>Pattern Recognition and Intelligent Systems<\/td><td>3<\/td><td>Fall<\/td><\/tr><tr><th scope=\"row\">ARC 6922<\/th><td>Machine Learning for Architects<\/td><td>3<\/td><td>Fall<\/td><\/tr><tr><th scope=\"row\">CAP 6610<\/th><td>Machine Learning<\/td><td>3<\/td><td>Spring<\/td><\/tr><tr><th scope=\"row\">ABE 6933<\/th><td>Applied Statistical Machine Learning<\/td><td>3<\/td><td>Fall<\/td><\/tr><\/tbody><\/table><\/div>\n\n<h4>Additional Course Policies<\/h4><p>Students may count up to two 3000- or 4000-level courses (6 credits maximum) from departments other than URP toward graduate credit. The source recommends the following for students without a computer science background:<\/p>\n<p class=\"mobile-hint\">Swipe horizontally to view all table columns.<\/p><div aria-label=\"Recommended additional courses\" class=\"table-scroll\" role=\"region\" tabindex=\"0\"><table class=\"smallcourses\"><thead><tr><th scope=\"col\">Course<\/th><th scope=\"col\">Title<\/th><th scope=\"col\">Credits<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">COP 3502C<\/th><td>Programming Fundamentals 1<\/td><td>4<\/td><\/tr><tr><th scope=\"row\">EEL 3850<\/th><td>Data Science for ECE<\/td><td>4<\/td><\/tr><\/tbody><\/table><\/div><p class=\"note\">COP 3502C and EEL 3850 are 4 credits each. Consult the program coordinator about how either course fits within the 6-credit maximum described above.<\/p>\n<p>Alternative elective courses require approval from the program coordinator.<\/p><\/section>\n\n<section id=\"msua-plans\"><h3>Model Semester Plan<\/h3><p>The following plans reproduce the course sequence shown in the source. \u201cGroup 1\u201d refers to urban and regional planning electives; \u201cGroup 2\u201d refers to machine learning electives.<\/p>\n<h4>Students Beginning in Fall<\/h4>\n<p class=\"mobile-hint\">Swipe horizontally to view all table columns.<\/p><div aria-label=\"Model plan for fall-entry students\" class=\"table-scroll\" role=\"region\" tabindex=\"0\"><table class=\"plan\"><thead><tr><th scope=\"col\">Semester<\/th><th scope=\"col\">Courses<\/th><th scope=\"col\">Credits<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">First semester: Fall<\/th><td>URP 6295 or URP 6007 &#8211; leveling course (3 credits)<br>URP 6223 (3 credits)<br>Elective Group 1 (3 credits)<br>Elective Group 2 (3 credits)<\/td><td>12<\/td><\/tr><tr><th scope=\"row\">Second semester: Spring<\/th><td>URP 6224 (3 credits)<br>URP 6979 (3 credits)<br>Elective Group 1 (3 credits)<br>Elective Group 2 (3 credits)<\/td><td>12<\/td><\/tr><tr><th scope=\"row\">Third semester: Fall<\/th><td>URP 6979 (3 credits)<br>Elective Group 1 or 2 (3 credits)<\/td><td>6<\/td><\/tr><tr class=\"total\"><th scope=\"row\">Total<\/th><td><\/td><td>30<\/td><\/tr><\/tbody><\/table><\/div>\n<h4>Students Beginning in Spring<\/h4>\n<p class=\"mobile-hint\">Swipe horizontally to view all table columns.<\/p><div aria-label=\"Model plan for spring-entry students\" class=\"table-scroll\" role=\"region\" tabindex=\"0\"><table class=\"plan\"><thead><tr><th scope=\"col\">Semester<\/th><th scope=\"col\">Courses<\/th><th scope=\"col\">Credits<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">First semester: Spring<\/th><td>Elective Group 1 (6 credits)<br>Elective Group 2 (6 credits)<\/td><td>12<\/td><\/tr><tr><th scope=\"row\">Second semester: Fall<\/th><td>URP 6295 or URP 6007 &#8211; leveling course (3 credits)<br>URP 6223 (3 credits)<br>Elective Group 1 or 2 (3 credits)<br>URP 6979 (3 credits)<\/td><td>12<\/td><\/tr><tr><th scope=\"row\">Third semester: Spring<\/th><td>URP 6224 (3 credits)<br>URP 6979 (3 credits)<\/td><td>6<\/td><\/tr><tr class=\"total\"><th scope=\"row\">Total<\/th><td><\/td><td>30<\/td><\/tr><\/tbody><\/table><\/div>\n<\/section>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>University of Florida \u00b7 Urban Analytics MSUA Program Requirements The Master of Science in Urban Analytics (MSUA) requires the completion of 30 credits. The curriculum includes required core courses &#8211; including a 6-credit Master Research Project &#8211; and electives in urban and regional planning and machine learning. Requirements overviewCore coursesElectivesModel semester plan Program Requirements at [&hellip;]<\/p>\n","protected":false},"author":223,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-23883","page","type-page","status-publish","hentry"],"featured_image_urls_v2":{"full":"","thumbnail":"","medium":"","medium_large":"","large":"","1536x1536":"","2048x2048":""},"post_excerpt_stackable_v2":"<p>University of Florida \u00b7 Urban Analytics MSUA Program RequirementsThe Master of Science in Urban Analytics (MSUA) requires the completion of 30 credits. The curriculum includes required core courses &#8211; including a 6-credit Master Research Project &#8211; and electives in urban and regional planning and machine learning. Requirements overviewCore coursesElectivesModel semester plan Program Requirements at a Glance Swipe horizontally to view all table columns.RequirementCreditsDetailsRequired Core Courses18Urban data structures, urban and regional planning, introductory and intermediate urban analytics, and a 6-credit Master Research Project.Elective Group 1: Urban and Regional Planning6 or 9Courses in planning information systems, geospatial modeling, spatial analysis, and transportation&hellip;<\/p>\n","category_list_v2":"","author_info_v2":{"name":"ryanhelterhoffurp","url":"https:\/\/dcp.ufl.edu\/urp\/author\/ryanhelterhoffurp\/"},"comments_num_v2":"0 comments","uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"ryanhelterhoffurp","author_link":"https:\/\/dcp.ufl.edu\/urp\/author\/ryanhelterhoffurp\/"},"uagb_comment_info":0,"uagb_excerpt":"University of Florida \u00b7 Urban Analytics MSUA Program Requirements The Master of Science in Urban Analytics (MSUA) requires the completion of 30 credits. The curriculum includes required core courses &#8211; including a 6-credit Master Research Project &#8211; and electives in urban and regional planning and machine learning. Requirements overviewCore coursesElectivesModel semester plan Program Requirements at&hellip;","_links":{"self":[{"href":"https:\/\/dcp.ufl.edu\/urp\/wp-json\/wp\/v2\/pages\/23883","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dcp.ufl.edu\/urp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dcp.ufl.edu\/urp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dcp.ufl.edu\/urp\/wp-json\/wp\/v2\/users\/223"}],"replies":[{"embeddable":true,"href":"https:\/\/dcp.ufl.edu\/urp\/wp-json\/wp\/v2\/comments?post=23883"}],"version-history":[{"count":0,"href":"https:\/\/dcp.ufl.edu\/urp\/wp-json\/wp\/v2\/pages\/23883\/revisions"}],"wp:attachment":[{"href":"https:\/\/dcp.ufl.edu\/urp\/wp-json\/wp\/v2\/media?parent=23883"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}