  {"id":502,"date":"2022-04-07T12:56:37","date_gmt":"2022-04-07T17:56:37","guid":{"rendered":"https:\/\/uwm.edu\/data-science\/?post_type=program&#038;p=502"},"modified":"2025-11-24T15:17:59","modified_gmt":"2025-11-24T21:17:59","slug":"data-science-ms","status":"publish","type":"program","link":"https:\/\/uwm.edu\/data-science\/academics\/data-science-ms\/","title":{"rendered":"Data Science and Applied Artificial Intelligence"},"content":{"rendered":"\n\n<div id=\"primary\" class=\"content-area\">\n\t<div id=\"main\" class=\"site-main\">\n\t\t\n\n<section id=\"program-intro\" class=\"uwm-u-pt-80 uwm-u-pb-40\">\n    <div class=\"uwm-l-container--base\">\n        \n\n<div class=\"uwm-l-row\">\n<div id=\"intro-main\" class=\"uwm-l-col\">\n<header class=\"page-header\">\n    <h1 class=\"entry-title heading-separator\">\n        Data Science and Applied Artificial Intelligence                    <div class=\"program-degree size-h3 uwm-u-mt-20\">MS<\/div>\n            <\/h1>\n<\/header>\n  \n\n\n\n\n<p class=\"lead-in\">Data science and AI are advancing virtually every aspect of our world. Data science degree programs at UW-Milwaukee prepare graduates to drive new data-centric advancements with the potential to improve daily life for everyone. Our master\u2019s in Data Science and Applied Artificial Intelligence paves the way to the future you\u2019re dreaming about.<\/p>\n\n\n\n<p class=\"size-p-lg\">A master\u2019s in Data Science and Applied Artificial Intelligence from UW-Milwaukee opens doors to amazing jobs just waiting to be filled. Data science and AI jobs are projected to grow by 35% (much faster than the average for all occupations) by 2032.<sup>1<\/sup> And nationwide, close to a quarter of U.S. job postings require data science and AI skills.<sup>2<\/sup> Additionally, recent research reveals that 30% of employers seek data scientists with a master\u2019s degree.<sup>3&nbsp; <\/sup>With a master\u2019s in Data Science and Applied Artificial Intelligence from UW-Milwaukee, you may just find yourself with a choice of dream jobs.<\/p>\n\n\n\n<p class=\"size-p-sm\"><sup>1<\/sup>U.S. Bureau of Labor Statistics&nbsp;<\/p>\n\n\n\n<p class=\"size-p-sm\"><sup>2<\/sup>Burning Glass Institute and ExcelinEd 2024&nbsp;<\/p>\n\n\n\n<p class=\"size-p-sm\"><sup>3<\/sup>\u201cThe Data Scientist Job Market in 2024 [Research on 1,000 Job Postings],\u201d\u202f<a href=\"https:\/\/365datascience.com\/career-advice\/data-scientist-job-market\/\" target=\"_blank\" rel=\"noreferrer noopener\">365datascience.com\/career-advice\/data-scientist-job-market\/<\/a>, Aug. 6, 2024.&nbsp;<\/p>\n\n\n    <div id=\"program-details-jump\">\n        <a class=\"uwm-c-button uwm-c-button--s\" href=\"#program-details\" rel=\"\">Jump to Program Details <i class=\"uwm-u-pl-10 fa-solid fa-arrow-down\"><\/i><\/a>\n    <\/div>\n\n    \n\n\n    <div class=\"uwm-l-row uwm-u-mt-50\">\n                    <div class=\"uwm-l-col\">\n                <h2 class=\"size-h6 uwm-u-my-10 uwm-u-pt-10 uwm-u-border--top\">Program Type<\/h2>\n                <p class=\"size-h4 uwm-u-mt-0\">\n                    <strong>Master&#8217;s<\/strong>\n                <\/p>\n            <\/div>\n        \n                    <div class=\"uwm-l-col uwm-l-offset--2\">\n                <h2 class=\"size-h6 uwm-u-my-10 uwm-u-pt-10 uwm-u-border--top\">Program Format<\/h2>\n                <p class=\"size-h4 uwm-u-mt-0\">\n                    <strong>On Campus, Online<\/strong>\n                <\/p>\n            <\/div>\n            <\/div>\n\n<\/div>\n\n\n\n<div id=\"intro-sidebar\" class=\"uwm-l-col uwm-l-col--4 uwm-l-offset--1\">\n        <div class=\"uwm-mini-rfi uwm-p-card uwm-p-card--gold uwm-p-card--shadow\">\n            <div class=\"uwm-p-card--body\">\n                <div class=\"uwm-p-card--content\">\n                    <div class=\"uwm-p-card--title\">Request Information<\/div>\n                    <div id='form_1709ab10-4705-4b06-9a9a-43e31327c124'>Loading&#8230;<\/div>                <\/div>\n            <\/div>\n        <\/div>\n        \n\n<div class=\"uwm-u-flex uwm-u-flex--wrap uwm-u-flex--left uwm-u-mt-40\">\n<a class=\"uwm-c-button uwm-c-button--gold uwm-u-mr-20 uwm-u-mb-20\" href=\"https:\/\/uwm.edu\/applygrad\/\" rel=\"\">Apply Now<\/a>\n\n\n\n<a class=\"uwm-c-button uwm-c-button--gold uwm-u-mr-20 uwm-u-mb-20\" href=\"https:\/\/uwm.edu\/cost-aid\" rel=\"\">Cost<\/a>\n<\/div>\n\n<script async='async' src='https:\/\/admissions.uwm.edu\/register\/?id=1709ab10-4705-4b06-9a9a-43e31327c124&amp;output=embed&amp;div=form_1709ab10-4705-4b06-9a9a-43e31327c124'>\/**\/<\/script>\n<\/div>\n<\/div>\n\n    <\/div>\n<\/section>\n\n\n\n\n\t    <section id=\"program-highlights\" class=\"uwm-u-py-80 uwm-bg-gray-dark\">\n        <div class=\"uwm-l-container--base\">\n        \n\n<div id=\"program-highlights-content-stats\" class=\"uwm-l-row\">\n<div class=\"uwm-l-col\">\n\n<h2 class=\"wp-block-heading heading-separator-short\" id=\"h-master-s-in-data-science-and-applied-artificial-intelligence-at-uw-milwaukee\">Master\u2019s in Data Science and Applied Artificial Intelligence at UW-Milwaukee<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-a-unique-degree-built-on-your-needs-preferences-and-chosen-field-nbsp\">A unique degree built on your needs, preferences and chosen field&nbsp;<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Start the program with ANY academic background, take courses in a wide variety of disciplines\/fields (business, computer science, engineering, etc.), pursue graduate-level data science and AI courses and choose your own path to success.&nbsp;<\/li>\n\n\n\n<li>Enroll in the data science and applied artificial intelligence master\u2019s with a bachelor\u2019s in the field you\u2019re passionate about and become a data scientist in that field.&nbsp;<\/li>\n\n\n\n<li>Complete the program with dual credentials that set you apart from your peers and help enhance your professional and personal achievements.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>If you\u2019re currently a working professional, you can enroll in 51ÁÔÆæ\u2019s data science and applied artificial intelligence master\u2019s  program to stack additional credentials and help advance your career.&nbsp;<\/p>\n\n<\/div>\n\n\n\n<div class=\"uwm-l-col uwm-l-col--6 uwm-l-offset--1\">\n<div class=\"uwm-p-gridblock uwm-p-gridblock--3-border uwm-p-gridblock--3\"><div class=\"uwm-p-gridblock--block uwm-p-block--stat uwm-p-block--default \"><div class=\"uwm-p-block--content-wrap\"><span><strong>30<\/strong>credits total<\/span><\/div><\/div>\n\n<div class=\"uwm-p-gridblock--block uwm-p-block--stat uwm-p-block--default \"><div class=\"uwm-p-block--content-wrap\"><span><strong>18<\/strong>core credits<\/span><\/div><\/div>\n\n<div class=\"uwm-p-gridblock--block uwm-p-block--stat uwm-p-block--default \"><div class=\"uwm-p-block--content-wrap\"><span><strong>12<\/strong>electives to specialize in your area of interest<\/span><\/div><\/div><\/div>\n<\/div>\n<\/div>\n\n        <\/div>\n    <\/section>\n\n\n\n\n<section id=\"program-details\" class=\"uwm-u-py-80\">\n    <div class=\"uwm-l-container--base\">\n        \n\n<div class=\"uwm-l-row\">\n<div id=\"details-main\" class=\"uwm-l-col\">\n\n<div class=\"uwm-p-tabs\" data-tabs-prefix-class=\"uwm-p-tabs\"><ul class=\"uwm-p-tablist\" data-hx=\"h2\"><li class=\"uwm-p-tablist--item\"><a href=\"#tab-yrq7-program-details\" class=\"uwm-p-tablist--link\">Program Details<\/a><\/li><li class=\"uwm-p-tablist--item\"><a href=\"#tab-yrq7-curriculum\" class=\"uwm-p-tablist--link\">Curriculum<\/a><\/li><li class=\"uwm-p-tablist--item\"><a href=\"#tab-yrq7-internship-thesis-exam\" class=\"uwm-p-tablist--link\">Internship\/Thesis\/Exam<\/a><\/li><li class=\"uwm-p-tablist--item\"><a href=\"#tab-yrq7-time-to-degree\" class=\"uwm-p-tablist--link\">Time to Degree <\/a><\/li><li class=\"uwm-p-tablist--item\"><a href=\"#tab-yrq7-partnership\" class=\"uwm-p-tablist--link\">Partnership<\/a><\/li><\/ul><div class=\"uwm-p-tabcontent\">\n<div id=\"tab-yrq7-program-details\" class=\"uwm-p-tabcontent--pane\">\n<p class=\"lead-in\">In the Data Science master\u2019s program at UW-Milwaukee, you\u2019ll learn skills and insights to help you land an in-demand, data-centric role in your chosen field.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Explore current program requirements and courses, access the official program application and discover more great reasons to choose a data science master\u2019s and 51ÁÔÆæ in our <a href=\"https:\/\/catalog.uwm.edu\/graduate-school\/data-science-ms\/\" target=\"_blank\" rel=\"noreferrer noopener\">academic catalog<\/a>.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-core-areas-of-study-nbsp\">Core areas of study&nbsp;<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Developing insights from data, for applications&nbsp;<\/li>\n\n\n\n<li>Organizing and maintaining large data sets&nbsp;<\/li>\n\n\n\n<li>Learning methods like AI and machine learning to extract insight from data&nbsp;<\/li>\n\n\n\n<li>Building knowledge and skills for using probabilistic methods to analyze uncertainty in data and develop insights&nbsp;<\/li>\n\n\n\n<li>Using advanced programming for data collection and data science&nbsp;<\/li>\n\n\n\n<li>Understanding the importance of, and skills for, the ethical use of data&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>For more information about the data science master\u2019s program please attend one of our upcoming\u202f<a href=\"https:\/\/uwm.edu\/data-science\/academics\/data-science-ms\/information-sessions\/\" target=\"_blank\" rel=\"noreferrer noopener\">information sessions<\/a>.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-student-learning-outcomes-and-program-objectives\"><strong>Student Learning Outcomes and Program Objectives<\/strong><\/h2>\n\n\n\n<p>The core objective of the M.S. in Data Science is to prepare students to pursue a data science oriented career path in the discipline that they are passionate about. The program is designed to allow students to progress through the areas mentioned above. Graduates of the M.S. in Data Science will be prepared to apply the concepts of data science inter-disciplinarily to problems in a variety of fields and industries and appreciate and abide by ethical uses of data and insights from the use of data science. Students will:<\/p>\n\n\n\n<ol style=\"list-style-type:1\" class=\"wp-block-list\">\n<li>Develop insights from data, for applications,<\/li>\n\n\n\n<li>Learn how to work with large data sets,<\/li>\n\n\n\n<li>Gain experience in advanced computer programming for data science<\/li>\n\n\n\n<li>Become skilled in specific areas of data science such as artificial intelligence and machine learning<\/li>\n\n\n\n<li>Understand how to deal with uncertainty which is an inherent characteristic of data science and<\/li>\n<\/ol>\n\n\n\n<p>Recognize and internalize the importance of ethical use of data and data science.<\/p>\n<\/div>\n\n\n\n<div id=\"tab-yrq7-curriculum\" class=\"uwm-p-tabcontent--pane\">\n<h2 class=\"wp-block-heading\" id=\"h-program-requirements-and-curriculum\"><strong>Program Requirements and Curriculum<\/strong><\/h2>\n\n\n\n<p>For admission to the M.S. in Data Science program, students must meet the general requirements of admission to a graduate program at UW-Milwaukee. As stated by the Graduate School, these requirements include: (1) a baccalaureate degree, or its equivalent as determined by the UW-MILWAUKEE Center for International Education, from a regionally accredited institution, completed before the first term of enrollment in the Graduate School, (2) proficiency in the English language, and (3) a minimum cumulative undergraduate grade point average (GPA) of 2.75 on a 4.0 scale, or an equivalent measure on a grading system that does not use a 4.0 scale. Students applying to the program are expected to have proficiency, demonstrated through coursework, exams or a portfolio, in the following areas: Linear Algebra (3 credits), Multivariable Calculus (4 credits), Statistics (3 credits), and Computer Literacy (6 credits). Those without these proficiencies may be admitted if they have 6 credits or fewer of the proficiency requirements remaining to be completed. Coursework taken to meet the&nbsp; proficiency requirements does not count towards the degree requirements.<\/p>\n\n\n\n<p>The table below illustrates the program curriculum for the proposed program. The program requirements are comprised of 30 credits, of which there are 18 credits across the six core areas, 12 credits of general electives in the seventh area for Specialized Skills in Data Science for Specific Applications and Fields of which 3 credits may be fulfilled with a capstone course. Enrollment in an internship or thesis is subject to the approval of the Program Director and the signature of a faculty member willing to guide the thesis or internship. Every student\u2019s program of electives must be approved by the program director. Subject to approval, students may be able to count as electives some courses in the \u201ccore\u201d categories not applied to the core requirements. Students wishing to apply other courses not listed above towards the electives requirements must have each course approved by the program director.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-core-coursework-18-credits\">Core Coursework (18 credits)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-developing-insights-from-data-for-application-choose-one-of-the-following\">Developing insights from data for application (choose one of the following)<\/h3>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course<\/th><th>Credits<\/th><\/tr><\/thead><tbody><tr><td>ATM SCI 600&nbsp;&nbsp;&nbsp;&nbsp; Data Analytics<\/td><td>3<\/td><\/tr><tr><td>INFOST 687&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data Analysis for Data Science<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 425(G)  Introduction to Data Mining<\/td><td>3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-organizing-and-maintaining-large-data-sets-choose-one-of-the-following\">Organizing and maintaining large data sets (choose one of the following)<\/h3>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course<\/th><th>Credits<\/th><\/tr><\/thead><tbody><tr><td>INFOST 785&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Database Management systems for information professionals<\/td><td>3<\/td><\/tr><tr><td>INFOST 714&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Metadata<\/td><td>3<\/td><\/tr><tr><td>INFOST 780&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; XML for Libraries<\/td><td>3<\/td><\/tr><tr><td>INFOST 783&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Information Storage and Retrieval<\/td><td>3<\/td><\/tr><tr><td>INFOST 691&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data Management and Curation<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 557&nbsp;&nbsp; Database Systems<\/td><td>3<\/td><\/tr><tr><td>PH 718&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Data Management and Visualization in R&nbsp;<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 749&nbsp;&nbsp; Data and Information Management<\/td><td>3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-ai-and-machine-learning-to-extract-insight-from-data-choose-one-of-the-following\">AI, and Machine Learning to extract insight from Data (choose one of the following)<\/h3>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course<\/th><th>Credits<\/th><\/tr><\/thead><tbody><tr><td>INFOST 582&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Introduction to Data Science<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 795 &nbsp; Seminar-in-Management: Ideas &amp; Applications of Data Science In<br>&nbsp; Different Fields<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 422G   Introduction to Artificial Intelligence<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 710&nbsp;&nbsp; Artificial Intelligence<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 411G   Machine Learning and Applications<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 711&nbsp;&nbsp; Introduction to Machine Learning<\/td><td>3<\/td><\/tr><tr><td>MATH 702&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Industrial MATH 2<\/td><td>3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-probabilistic-methods-to-analyze-uncertainty-in-data-choose-one-of-the-following\">Probabilistic methods to analyze uncertainty in data (choose one of the following)<\/h3>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course<\/th><th>Credits<\/th><\/tr><\/thead><tbody><tr><td>ATM SCI 500&nbsp;&nbsp;&nbsp;&nbsp; Statistical Methods in Atmospheric Sciences<\/td><td>3<\/td><\/tr><tr><td>ATM SCI 700&nbsp;&nbsp;&nbsp;&nbsp; Statistical Methods in Atmospheric Sciences II: Signal Detection<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 754 &nbsp; Statistical Analysis<\/td><td>3<\/td><\/tr><tr><td>BUSMGMT 709 Predictive Analytics for Managers<\/td><td>3<\/td><\/tr><tr><td>BUS ADM&nbsp;713 &nbsp; Business Forecasting Methods<\/td><td>3<\/td><\/tr><tr><td>BUS ADM&nbsp;714&nbsp;&nbsp; Multivariate Techniques in Mgmt Research<\/td><td>3<\/td><\/tr><tr><td>IND ENG 575&nbsp;&nbsp;&nbsp; Design of Experiments<\/td><td>3<\/td><\/tr><tr><td>IND ENG 765&nbsp;&nbsp;&nbsp; Operations Research Methods<\/td><td>3<\/td><\/tr><tr><td>SOCIOL 461G&nbsp;&nbsp; Social Data Analysis Using Regression<\/td><td>3<\/td><\/tr><tr><td>SOCIOL 760&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Advanced Statistical Methods in Sociology<\/td><td>3<\/td><\/tr><tr><td>SOCIOL 982&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Advanced Quantitative Analysis<\/td><td>3<\/td><\/tr><tr><td>PSYCH 510G&nbsp;&nbsp;&nbsp;&nbsp; Advanced Psychological Statistics<\/td><td>3<\/td><\/tr><tr><td>PSYCH 610G&nbsp;&nbsp;&nbsp;&nbsp; Experimental Design<\/td><td>3<\/td><\/tr><tr><td>POL SCI 390G&nbsp;&nbsp; Political Data Analysis<\/td><td>3<\/td><\/tr><tr><td>POL SCI 701&nbsp;&nbsp;&nbsp;&nbsp; Techniques of Political Science Research<\/td><td>3<\/td><\/tr><tr><td>POL SCI 702&nbsp;&nbsp;&nbsp;&nbsp; Advanced Techniques of Political Science Research<\/td><td>3<\/td><\/tr><tr><td>ECON 411G&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Economic Forecasting Methods<\/td><td>3<\/td><\/tr><tr><td>ECON 413G&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Statistics for Economists<\/td><td>3<\/td><\/tr><tr><td>ECON 513G&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Introduction to Econometrics<\/td><td>3<\/td><\/tr><tr><td>GEOG 747&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Spatial Analysis<\/td><td>3<\/td><\/tr><tr><td>PH 711&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Intermediate Biostatistics<\/td><td>3<\/td><\/tr><tr><td>PH 818&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &#8220;Statistical Computing (&#8220;&#8221;This course will cover the theory and &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; application of common algorithms used in statistical computing.)&#8221;<\/td><td>3<\/td><\/tr><tr><td>GEOG 827&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Qualitative Research<\/td><td>3<\/td><\/tr><tr><td>COMPST&nbsp;701&nbsp;&nbsp;&nbsp; Mathematical &amp; Computing Fundamentals for IT Professionals<\/td><td>3<\/td><\/tr><tr><td>MTHSTAT 361G Intro Prob\/Stats I<\/td><td>3<\/td><\/tr><tr><td>MTHSTAT 362G Intro Prob\/Stats II<\/td><td>3<\/td><\/tr><tr><td>MTHSTAT 763 &nbsp; Regression<\/td><td>3<\/td><\/tr><tr><td>MTHSTAT 764 &nbsp; Time Series Analysis<\/td><td>3<\/td><\/tr><tr><td>MATH 571G&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Probability Models<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 720&nbsp;&nbsp; Computational models for decision making<\/td><td>3<\/td><\/tr><tr><td>MTHSTAT 761&nbsp; Mathematical Statistics I<\/td><td>3<\/td><\/tr><tr><td>MTHSTAT 762&nbsp; Mathematical Statistics II<\/td><td>3<\/td><\/tr><tr><td>ED PSY 724&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Educational Statistical Methods II<\/td><td>3<\/td><\/tr><tr><td>ED PSY 820&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Multiple Regression and Other General Linear Models<\/td><td>3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-advanced-programming-for-data-collection-and-data-science-choose-one-of-the-following\">Advanced Programming for Data Collection and Data Science (choose one of the following)<\/h3>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course<\/th><th>Credits<\/th><\/tr><\/thead><tbody><tr><td>BUSMGMT&nbsp;744 R Programming for Business Analytics<\/td><td>3<\/td><\/tr><tr><td>COMPST 702&nbsp;&nbsp;&nbsp; Software Development in Python<\/td><td>3<\/td><\/tr><tr><td>GEOG 748 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ArcGIS Programming with Python<\/td><td>3<\/td><\/tr><tr><td>URBPLAN 794&nbsp; Internet Geographic Information Systems<\/td><td>3<\/td><\/tr><tr><td>COMPST 751&nbsp;&nbsp;&nbsp; Data Structures and Algorithms<\/td><td>3<\/td><\/tr><tr><td>MTHSTAT 766&nbsp; Computational Statistics<\/td><td>3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-ethics-choose-one-of-the-following\">Ethics (choose one of the following)<\/h3>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course<\/th><th>Credits<\/th><\/tr><\/thead><tbody><tr><td>INFOST 660&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Information Policy<\/td><td>3<\/td><\/tr><tr><td>INFOST 661&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Information Ethics<\/td><td>3<\/td><\/tr><tr><td>INFOST 583&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Survey of Information Security<\/td><td>3<\/td><\/tr><tr><td>INFOST 784&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Information Security Management<\/td><td>3<\/td><\/tr><tr><td>INFOST 761&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Information Privacy<\/td><td>3<\/td><\/tr><tr><td>INFOST 465G&nbsp;&nbsp;&nbsp; Legal aspects of info products &amp; services (G)<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 743&nbsp;&nbsp; Information Privacy, Security, and Continuity<\/td><td>3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-specialized-skills-in-data-science-for-specific-application-in-the-field-12-credits\">Specialized skills in data science for specific application in the field (12 credits)<\/h2>\n\n\n\n<p>Choose from the following:<\/p>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course<\/th><th>Credits<\/th><\/tr><\/thead><tbody><tr><td>INFOST 691&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Artificial Intelligence and Disruptive Technologies<\/td><td>3<\/td><\/tr><tr><td>BUS ADM&nbsp;741 &nbsp; Web Mining and Analytics<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 812&nbsp;&nbsp; Machine Learning for Business.<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 813 &nbsp; Social Media Analytics for Business<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 817&nbsp;&nbsp; Connected Systems for Business<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 742&nbsp;&nbsp; Big Data in Business<\/td><td>3<\/td><\/tr><tr><td>BUS ADM 745&nbsp;&nbsp; Artificial Intelligence for Business<\/td><td>3<\/td><\/tr><tr><td>BUS ADM&nbsp;763 &nbsp; Marketing Analytics<\/td><td>3<\/td><\/tr><tr><td>BUS ADM&nbsp;769 &nbsp; Database Marketing<\/td><td>3<\/td><\/tr><tr><td>BUS ADM&nbsp;816 &nbsp; Business Intelligence Technologies &amp; Solutions<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 712&nbsp;&nbsp; Image Processing<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 423G     Natural Language Processing<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 723&nbsp;&nbsp; Natural Language Processing<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 744&nbsp;&nbsp; Text Retrieval<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 469G Security<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 535G Analysis of Algorithms<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 704&nbsp;&nbsp; Analysis of Algorithms<\/td><td>3<\/td><\/tr><tr><td>COMPSCI 759&nbsp;&nbsp; Data Security<\/td><td>3<\/td><\/tr><tr><td>Comp Sci 725&nbsp;&nbsp; Robot Motion Planning<\/td><td>3<\/td><\/tr><tr><td>Comp Sci 755&nbsp;&nbsp; Information and Coding Theory<\/td><td>3<\/td><\/tr><tr><td>SOCIOL. 750&nbsp;&nbsp;&nbsp;&nbsp; Research Methods in Sociology<\/td><td>3<\/td><\/tr><tr><td>SOCIOL. 752&nbsp;&nbsp;&nbsp;&nbsp; Fundamentals of Survey Methodology<\/td><td>3<\/td><\/tr><tr><td>SOCIOL 952&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Social Network Analysis<\/td><td>3<\/td><\/tr><tr><td>POL SCI392G&nbsp;&nbsp;&nbsp; Survey Research<\/td><td>3<\/td><\/tr><tr><td>GEOG 704&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Remote Sensing: Environmental and Land Use Analysis<\/td><td>3<\/td><\/tr><tr><td>GEOG 705&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Cartography<\/td><td>3<\/td><\/tr><tr><td>GEOG 716&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Watershed Analysis and Modeling<\/td><td>3<\/td><\/tr><tr><td>GEOG 726&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Geographic Information Science<\/td><td>3<\/td><\/tr><tr><td>GEOG 804&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Advanced Remote Sensing<\/td><td>3<\/td><\/tr><tr><td>GEOG 826&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Intermediate Geographic Information Science<\/td><td>3<\/td><\/tr><tr><td>GEOG 834&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GIS and Society<\/td><td>3<\/td><\/tr><tr><td>GEOG 904&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Remote Sensing and Urban Analysis<\/td><td>3<\/td><\/tr><tr><td>GEOG 926&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Advanced Geographic Information Science: Geographic Modeling<\/td><td>3<\/td><\/tr><tr><td>GEOG 960 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Seminar: Geographic Techniques<\/td><td>3<\/td><\/tr><tr><td>GEOG 999&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Independent Work (with appropriate topic)<\/td><td>3<\/td><\/tr><tr><td>URBPLAN 692&nbsp; Special Topics in Urban Planning: Transportation Planning and GIS<\/td><td>3<\/td><\/tr><tr><td>PH 812&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Statistical Learning and Data Mining<\/td><td>3<\/td><\/tr><tr><td>URBPLAN 791&nbsp; Introduction to Urban Geographic Information Systems for Planning<\/td><td>3<\/td><\/tr><tr><td>URBPLAN 792&nbsp; Using Urban Geographic Information Systems for Planning<\/td><td>3<\/td><\/tr><tr><td>URBPLAN 999&nbsp; Independent Study<\/td><td>3<\/td><\/tr><tr><td>ANTHRO 380&nbsp;&nbsp;&nbsp; Anthropological Applications of GIS<\/td><td>3<\/td><\/tr><tr><td>ANTHRO 562&nbsp;&nbsp;&nbsp; Techniques and Problems in Archaeology<\/td><td>3<\/td><\/tr><tr><td>ANTHO 768&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Topics in Advanced Research Design in Anthropology<\/td><td>3<\/td><\/tr><tr><td>CRM JST 520G&nbsp; Analysis Oriented Technology: Spatial Data Analysis; Crime Mapping; ArcGIS<\/td><td>3<\/td><\/tr><tr><td>CRM JST 713&nbsp;&nbsp;&nbsp;&nbsp; Measuring Crime &amp; Analyzing Crime Data<\/td><td>3<\/td><\/tr><tr><td>CRM JST 716&nbsp;&nbsp;&nbsp;&nbsp; Advanced Analytic Techniques for Crime Analysts<\/td><td>3<\/td><\/tr><tr><td>CRM JST 910&nbsp;&nbsp;&nbsp;&nbsp; Methods and Practice Capstone for Crime Analysts<\/td><td>3<\/td><\/tr><tr><td>ART 526G&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Research in Universal Design and Fabrication<\/td><td>3<\/td><\/tr><tr><td>ART 316 G&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Interactive and Multimedia Art<\/td><td>3<\/td><\/tr><tr><td>ART 317 G&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3D Imaging I<\/td><td>3<\/td><\/tr><tr><td>ART 427 U\/G&nbsp;&nbsp;&nbsp; Special Topics Course<\/td><td>3<\/td><\/tr><tr><td>ART 313 U\/G&nbsp;&nbsp;&nbsp; Interactive and Multimedia Art and Programming for Artists<\/td><td>3<\/td><\/tr><tr><td>ED PSY 720&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Techniques of Educational and Psychological Measurement<\/td><td>3<\/td><\/tr><tr><td>MATH 701&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Industrial MATH 1<\/td><td>3<\/td><\/tr><tr><td>ED PSY 821&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Psychometric Theory and Practice<\/td><td>3<\/td><\/tr><tr><td>ED PSY 822&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Modern Test Theory<\/td><td>3<\/td><\/tr><tr><td>ED PSY 823&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Structural Equation Modeling<\/td><td>3<\/td><\/tr><tr><td>ED PSY 824&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Advanced Experimental Design and Analysis<\/td><td>3<\/td><\/tr><tr><td>ED PSY 825&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Multivariate Methods<\/td><td>3<\/td><\/tr><tr><td>ED PSY 826&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Analysis of Cross-Classified Categorical Data<\/td><td>3<\/td><\/tr><tr><td>ED PSY 827&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Survey Research Methods in Education<\/td><td>3<\/td><\/tr><tr><td>ED PSY 832&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Theory of Hierarchical Linear Modeling<\/td><td>3<\/td><\/tr><tr><td>BIO SCI 469 &nbsp;&nbsp;&nbsp;&nbsp; Genomic Data Analysis<\/td><td>3<\/td><\/tr><tr><td>BIO SCI 502 &nbsp;&nbsp;&nbsp;&nbsp; Introduction to Programming and Modeling in Ecology and Evolution<\/td><td>3<\/td><\/tr><tr><td>BIO SCI 572 &nbsp;&nbsp;&nbsp;&nbsp; Functional Genomics<\/td><td>3<\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n\n\n\n<div id=\"tab-yrq7-internship-thesis-exam\" class=\"uwm-p-tabcontent--pane\">\n<h2 class=\"wp-block-heading\" id=\"h-optional-internship-thesis-capstone\">Optional: Internship \/ Thesis \/ Capstone<\/h2>\n\n\n\n<p>Of the required 12 elective credits, up to three credits may be awarded for a thesis or internship. Students who choose this option must complete a relevant thesis or internship that is approved by the program director. Students who choose to complete a thesis must work with a thesis advisor and have the thesis approved by the advisor and the program director. Students who choose to pursue an internship must also obtain approval from the program director. Students may select from courses such as those listed below or enroll for thesis\/internship credits with their thesis advisor (in the advisor\u2019s department).<\/p>\n\n\n\n<figure class=\"uwm-c-table uwm-c-table--stripe\"><table><thead><tr><th>Course Number<\/th><th>Course Name<\/th><\/tr><\/thead><tbody><tr><td>INFOST 790&nbsp;<\/td><td>Project Design, Implementation, and Evaluation<\/td><\/tr><tr><td>GEOG 798&nbsp;&nbsp;<\/td><td>GIS\/Cartography Internship<\/td><\/tr><tr><td>URBPLAN 793<\/td><td>Applied Projects in Urban Geographic Information Systems<\/td><\/tr><tr><td>URBPLAN 991<\/td><td>Legislative\/Administrative Agency Internship<\/td><\/tr><tr><td>MATH 790&nbsp;<\/td><td>Master&#8217;s Thesis<\/td><\/tr><tr><td>COMPSCI 990 &nbsp;<\/td><td> Master\u2019s Thesis<\/td><\/tr><tr><td>COMPSCI 995 <\/td><td>Master&#8217;s Capstone Project<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-qualifying-exam\">Qualifying Exam<\/h2>\n\n\n\n<p>Students who do not choose to pursue the optional capstone course\/thesis\/internship option are required to pass a qualifying exam. During this exam, students are given a data set and a research problem to be addressed with the data, using data science techniques. Students must submit a final report in which they use the provided data set to address the research question and demonstrate that they have developed a sufficient level of expertise to work as a data scientist. This is a take-home exam and students will have seven days to complete it.<\/p>\n<\/div>\n\n\n\n<div id=\"tab-yrq7-time-to-degree\" class=\"uwm-p-tabcontent--pane\">\n<p>The program requires completion of 30 credits. A full-time student would typically complete the requirements in two years or four semesters. A part-time student enrolling in one course per semester would require 10 semesters to complete the program.<\/p>\n<\/div>\n\n\n\n<div id=\"tab-yrq7-partnership\" class=\"uwm-p-tabcontent--pane\">\n<h2 class=\"wp-block-heading\" id=\"h-partnership-with-the-northwestern-mutual-data-science-institute-nbsp\">Partnership with the Northwestern Mutual Data Science Institute&nbsp;<\/h2>\n\n\n\n<p>UW-Milwaukee is a founding partner of the Northwestern Mutual Data Science Institute (<a href=\"https:\/\/innv.northwesternmutual.com\/northwestern-mutual-data-science-institute\/\" target=\"_blank\" rel=\"noreferrer noopener\">NMDSI<\/a>), a $75 million initiative created to develop degree programs that graduate professionals skilled in data science and AI.&nbsp;&nbsp;<\/p>\n\n\n\n<p>This unique partnership means that as a 51ÁÔÆæ Data Science master\u2019s program student, you\u2019ll:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Benefit from NMDSI mentoring opportunities, research support and student funding opportunities to enhance your academic and professional achievements.&nbsp;<\/li>\n\n\n\n<li>Collaborate with expert faculty leaders in programs\/fields including computer science, statistics, marketing, health, public health and education.&nbsp;<\/li>\n\n\n\n<li>Work directly with employers to solve real-world problems via data science and AI.&nbsp;<\/li>\n<\/ul>\n<\/div>\n<\/div><\/div>\n\n<\/div>\n\n\n\n<div id=\"details-sidebar\" class=\"uwm-l-col uwm-l-col--4 uwm-l-offset--1\">\n            <nav aria-label=\"Section\" class=\"uwm-p-navigation-list uwm-p-navigation-list--gold-border uwm-u-mb-40\">\n                <div class=\"uwm-p-navigation-list--title\">\n                    <a href=\"https:\/\/uwm.edu\/data-science\/academics\/data-science-ms\/\">Data Science and Applied Artificial Intelligence<\/a>                <\/div>\n                <ul>\n                                            \n                        <li class=\"\">\n\t\t\t\t\t\t\t<a href=\"https:\/\/uwm.edu\/data-science\/academics\/data-science-ms\/information-sessions\/\" >Master&#039;s of Data Science Information Sessions<\/a>                        <\/li>\n                                        <\/ul>\n            <\/nav>\n        \n\n\n\n<div class=\"uwm-l-buttons--full\">\n<a class=\"uwm-c-button uwm-c-button--full uwm-u-mb-20\" href=\"https:\/\/catalog.uwm.edu\/graduate-school\/data-science-applied-artificial-intelligence-ms\/\" rel=\"\">Academic Catalog<\/a>\n<\/div>\n\n\n\n\n    <div class=\"uwm-p-card uwm-p-card--color-head uwm-u-mt-40\">\n        <div class=\"uwm-p-card--title\">Important Dates<\/div>\n        <div class=\"uwm-p-card--body\">\n            <div class=\"uwm-p-card--content\">\n                <strong>Domestic students priority application deadline:\u00a0<\/strong><br><em>Spring semester:<\/em> Nov. 15<br><em>Fall semester: <\/em><br>Dec. 15 &#8211; Priority deadline to be considered for funding. <br>April 15 &#8211; Recommended deadline for all other applications.<br><br><strong>International students priority deadline<\/strong>\u00a0<br><em>Spring semester:<\/em> July 15\u00a0<br><em>Fall semester: <\/em>Dec. 15 &#8211; Priority deadline to be considered for funding.\u00a0<br><br>Applications for all students and all semesters continue to be accepted after the priority deadlines (rolling admission).\u00a0                <\/div>\n        <\/div>\n    <\/div>\n\n\n\n\n<\/div>\n<\/div>\n\n    <\/div>\n<\/section>\n\n\n\t<\/div>\n<\/div>\n\n\n","protected":false},"author":9839,"parent":0,"template":"","meta":{"_acf_changed":false,"_uwm_academics_program_template":"page","_uwm_academics_program_navigation":[{"id":1180,"label":"Master's of Data Science Information Sessions","url":"https:\/\/uwm.edu\/data-science\/academics\/data-science-ms\/information-sessions\/","target":"","rel":"","custom":false,"className":""}],"uwm_academics_program_card_image_id":541,"uwm_academics_program_banner_image_id":0,"uwm_academics_program_sort_title":"","uwm_academics_program_external_url":"","uwm_academics_program_jump_details_btn":true,"_uwm_academics_program_exclude_from_programs_archive_page":false,"_uwm_academics_program_exclude_from_programs_block":false,"_uwm_academics_program_exclude_from_department_page":false,"footnotes":"","uwm_wg_additional_authors":[]},"program_group":[],"program_tag":[],"program_type":[27],"program_format":[35,36],"program_department":[],"class_list":["post-502","program","type-program","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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