{"id":17,"date":"2021-09-24T10:45:30","date_gmt":"2021-09-24T14:45:30","guid":{"rendered":"https:\/\/www.ramapo.edu\/dmc\/?page_id=17"},"modified":"2025-11-26T10:18:38","modified_gmt":"2025-11-26T15:18:38","slug":"curriculum","status":"publish","type":"page","link":"https:\/\/www.ramapo.edu\/dmc\/curriculum\/","title":{"rendered":"Curriculum"},"content":{"rendered":"<p>The Center for Data, Mathematical, and Computational Sciences (DMC) at 69色情视频 offers an integrated ecosystem of programs designed around shared foundations and flexible pathways. Students can pursue a BS in Computer Science, Data Science, Cybersecurity, or Mathematics, with all four programs sharing core coursework in programming, data structures, and mathematical reasoning. This common foundation enables students to explore diverse electives\u2014from Machine Learning and Agentic Software Architectures to Cryptography and Big Data Programming\u2014while building expertise in their chosen discipline.<\/p>\n<p>The real value emerges through our accelerated 4+1 pathways, where undergraduates can complete both a bachelor&#8217;s and master&#8217;s degree in just five years. Students from any of the four BS programs can transition into the MS in Data Science, MS in Computer Science, or MS in Applied Mathematics, taking up to three graduate courses during their senior year at undergraduate tuition rates. This saves approximately 30% on graduate education while building advanced competencies in artificial intelligence, statistical modeling, and systems architecture.<\/p>\n<div style=\"min-height: 70px;background-color: #313436\">\n<div style=\"float: left;width: 15px;height: 70px;background-color: #a42228\"><\/div>\n<div style=\"padding: 1em;padding-left: 30px\">\n                    <a style=\"color: white;font-size: larger\" href=\"https:\/\/www.ramapo.edu\/dmc\/ai-in-the-dmc-curriculum\/\">Learn how we&#8217;re adapting for AI<\/a>\n                <\/div>\n<\/p><\/div>\n<h2>Undergraduate Programs<\/h2>\n<p>All of the undergraduate programs below can lead to a graduate degree in any of the disciplines the Center supports. If you are just starting out in your college career, take a look!\u00a0 Students enrolled in these majors might want to consider the <a href=\"https:\/\/www.ramapo.edu\/dmc\/4plus1\/\">4+1 BS to MS Degree option<\/a>.<\/p>\n<div class=\"row\">\n<div class=\"col-sm-12 col-md-12 col-lg-6\">\n<div style=\"min-height: 70px;background-color: #313436\">\n<div style=\"float: left;width: 15px;height: 70px;background-color: #449c62\"><\/div>\n<div style=\"padding: 1em;padding-left: 30px\">\n                    <a style=\"color: white;font-size: larger\" href=\"https:\/\/www.ramapo.edu\/majors-minors\/majors\/bioinformatics\/\">Bioinformatics<\/a>\n                <\/div>\n<\/p><\/div>\n<div style=\"min-height: 70px;background-color: #313436;margin-top: 20px\">\n<div style=\"float: left;width: 15px;height: 70px;background-color: #cba052\"><\/div>\n<div style=\"padding: 1em;padding-left: 30px\"><a style=\"color: white;font-size: larger\" href=\"https:\/\/www.ramapo.edu\/majors-minors\/majors\/mathematics\/\">Mathematics<\/a><\/div>\n<\/p><\/div>\n<div style=\"min-height: 70px;background-color: #313436;margin-top: 20px\">\n<div style=\"float: left;width: 15px;height: 70px;background-color: #05336e\"><\/div>\n<div style=\"padding: 1em;padding-left: 30px\"><a style=\"color: white;font-size: larger\" href=\"https:\/\/www.ramapo.edu\/majors-minors\/majors\/bachelor-of-science-in-cybersecurity\/\">Cybersecurity<\/a>\n                <\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"col-sm-12 col-md-12 col-lg-6\">\n<div style=\"min-height: 70px;background-color: #313436\">\n<div style=\"float: left;width: 15px;height: 70px;background-color: #a42228\"><\/div>\n<div style=\"padding: 1em;padding-left: 30px\"><a style=\"color: white;font-size: larger\" href=\"https:\/\/www.ramapo.edu\/majors-minors\/majors\/data-science\/\">Data Science<\/a><\/div>\n<\/p><\/div>\n<div style=\"min-height: 70px;background-color: #313436;margin-top: 20px\">\n<div style=\"float: left;width: 15px;height: 70px;background-color: #d7d2cb\"><\/div>\n<div style=\"padding: 1em;padding-left: 30px\"><a style=\"color: white;font-size: larger\" href=\"https:\/\/www.ramapo.edu\/majors-minors\/majors\/computer-science\/\">Computer Science<\/a><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"divider\"><img decoding=\"async\" src=\"\/wp-content\/themes\/rcnjrd\/images\/icons\/ramapo-arch-icom_rule.png\" alt=\"Ramapo\" \/><\/div>\n<h2>Graduate Programs<\/h2>\n<p>The Center supports three Master of Science degrees, all of which share resources and faculty. Students in all three programs take many similar courses. See below for specific degree requirements.<br \/>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>MS in Data Science (30 credits)<\/div><div class=\"c_content\"><\/p>\n<h4>Program Goals<\/h4>\n<ul>\n<li><strong>Goal 1:  Data Acquisition and Management<\/strong>:  Students must understand the technical and ethical aspects of collecting data and storing data.<\/li>\n<li>\n<strong>Goal 2:  Data Analysis<\/strong>:  Students must have the mathematics and computing skills to effectively analyze collected data.\n<\/li>\n<li>\n<strong>Goal 3:  Data Presentation<\/strong>:  Students must be able to communicate their analysis and create effective visualizations of complex data sets to support their analysis.\n<\/li>\n<li>\n<strong>Goal 4:  Data-Driven Decision Making<\/strong>:  Students must be able to use their analysis to drive domain-specific decisions.\n<\/li>\n<li>\n<strong>Goal 5:  Effectively integrate all facets of Data Science to meet domain-specific project objectives:<\/strong>  Students will be able to integrate the skills described above in fieldwork\/thesis projects.\n<\/li>\n<\/ul>\n<h4>Program Learning Outcomes<\/h4>\n<ul>\n<li><strong>Outcome 1:<\/strong>  Demonstrate advanced skills in data acquisition and management.<\/li>\n<li><strong>Outcome 2:<\/strong>  Demonstrate advanced skills in data analysis techniques using mathematics and statistical principles.<\/li>\n<li><strong>Outcome 3:<\/strong>  Demonstrate advanced skills in data presentation, communication, and visualization.<\/li>\n<li><strong>Outcome 4:<\/strong>  Demonstrate the ability to integrate skills in a multi-faceted data science project.<\/li>\n<li><strong>Outcome 5:<\/strong>  Demonstrate the ability to make data-driven decisions.<\/li>\n<\/ul>\n<p>Take a look at how our courses match up with the the <a href=\"https:\/\/www.ramapo.edu\/dmc\/2025\/08\/29\/learn-how-ramapos-msds-aligns-with-the-nationally-recognized-adsa-core-data-science-competencies\/\">Academic Data Science Alliance industry endorsed list of core competencies<\/a>!<\/p>\n<h4>Required Courses (21 credits)<\/h4>\n<ul>\n<li><a href=\"#data601\">DATA 601 &#8211; Introduction to Data Science<\/a> &#8211; Introduces students to topics covering <i>all<\/i> the program goals and outcomesbri &#8211; data acquition, analysis, presentation, and integration.  The course has a seminar component, bringing in speakers from industry to introduce students to real world data science use cases. <\/li>\n<li><a href=\"#data620\">DATA 620 &#8211; Ethics in Data and Computing<\/a> &#8211; Focuses on data integrative skills and data driven decisions, within the context of ethics, law, and policy surrounding technology.<\/li>\n<li><a href=\"#cmps530\">CMPS 530 &#8211; Python for Data Science<\/a> &#8211; Provides students the programming experience necessary for further exploration of data science through Python.  Students learn data acquisition, presentation, and integration skills through programming projects.<\/li>\n<li><a href=\"#cmps664\">CMPS 664 &#8211; Big Data and Database Design<\/a> &#8211; Students learn the foundational concepts of data storage, schema design, and distributed data concepts to support data acquisition and integrative skills.<\/li>\n<li><a href=\"#math570\">MATH 570 &#8211; Applied Statistics<\/a>Provides students the necessary foundation in statistics to further explore data analysis, setting the stage for the advanced mathematical modeling course taken later in the program.  Students also learn methods of using statistics to better present and contextualize data.<\/li>\n<li><a href=\"#math680\">MATH 680 \u2013 Advanced Mathematical Modeling<\/a> Students refine their abilities to perform data analysis, presentation, and integration to drive decision making. Student projects demonstrate mastery in each of the program learning outcomes.<\/li>\n<li><a href=\"#data745\">DATA 745 &#8211; Data Science Thesis Proposal <\/a>and <a href=\"#data750\">DATA 750 &#8211; Data Science Thesis<\/a> Serves as the final demonstration of mastery of the program goals and learning outcomes, encapsulated in an independent research project requiring students to demonstrate impact and proficiency in data-driven decision making.<\/li>\n<\/ul>\n<div><a id=msds-electives><\/a><\/div>\n<h4>Category 1 Electives (Pick 2, total of 6 credits)<\/h4>\n<p>Category 1 Electives allow students to explore more specific areas of data science focused on technical skills.  These electives reinforce different goals and learning outcomes, however most focus analysis, presentation, and integrative skills.<\/p>\n<ul>\n<li><a href=\"#cmps620\">CMPS 620 &#8211; Machine Learning<\/a><\/li>\n<li><a href=\"#cmps645\">CMPS 645 &#8211; Analysis of Algorithms<\/a><\/li>\n<li><a href=\"#data670\">DATA 670 &#8211; Data Visualization<\/a><\/li>\n<li><a href=\"#data687\">DATA 687 &#8211; Time Series Data<\/a><\/li>\n<li><a href=\"#math540\">MATH 540 &#8211; Cryptography<\/a><\/li>\n<li><a href=\"#math562\">MATH 562 &#8211; Applied Linear Algebra<\/a><\/li>\n<li><a href=\"#math645\">MATH 645 &#8211; Numerical Analysis<\/a><\/li>\n<li><a href=\"#math654\">MATH 654 &#8211; Applied Probability<\/a><\/li>\n<li><a href=\"#math685\">MATH 685 &#8211; Introduction to Experimental Design<\/a><\/li>\n<\/ul>\n<h4>Category 2 Electives (Pick 1, total of 3 credits)<\/h4>\n<p>The Category 2 elective allows students to dive deeper into the broader context of data, particularly within business and industry.  The courses listed have a less technical focus and instead aim more at data driven decision making and presentation skills.  Students are permitted to replace this with a third Category 1 elective in most cases should they choose.<\/p>\n<ul>\n<li><a id=\"69e161a6cbd69\" href=\"https:\/\/catalog.ramapo.edu\/courses\/BADM501\" target=\"_blank\">BADM 501 - DATA ANALYTICS<\/a><\/li>\n<li><a id=\"69e161a6cc0c1\" href=\"https:\/\/catalog.ramapo.edu\/courses\/MBAD610\" target=\"_blank\">MBAD 610 - BECOMING A 21ST CENTURY LEADER<\/a><\/li>\n<li><a id=\"69e161a6cc63d\" href=\"https:\/\/catalog.ramapo.edu\/courses\/MBAD612\" target=\"_blank\">MBAD 612 - LEADING CHANGE IN AN  UNCERTAIN WORLD<\/a><\/li>\n<li><a id=\"69e161a6ccd80\" href=\"https:\/\/catalog.ramapo.edu\/courses\/MBAD615\" target=\"_blank\">MBAD 615 - BUSINESS ANALYTICS<\/a><\/li>\n<li><a href=\"#data730\">DATA 730 &#8211; Fieldwork Experience<\/a><\/li>\n<\/ul>\n<h3>Looking for a smaller commitment?<\/h3>\n<p>We have 3 graduate certificate programs available in Data Science.  You can take three courses an earn a certificate in either Data Analyst, Data Modeler, or Machine Learning Engineer.  The courses you take will count towards a future MS in Data Science if you decide to take that on later!<\/p>\n<p><a href=\"https:\/\/www.ramapo.edu\/dmc\/graduate-certificates-in-data-science\/\">Learn more about our Graduate Certificates in Data Science<\/a><br \/>\n<\/div><\/div><\/p>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>MS in Applied Mathematics (30 credits)<\/div><div class=\"c_content\">\n<h4>Required Courses (15 credits)<\/h4>\n<ul>\n<li><a href=\"#math562\">MATH 562 &#8211; Applied Linear Algebra<\/a><\/li>\n<li><a href=\"#math562\">MATH 654 &#8211; Applied Probability<\/a><\/li>\n<li><a href=\"#math680\">MATH 680 &#8211; Advanced Mathematical Modeling<\/a><\/li>\n<li><a href=\"#data620\">DATA 620 &#8211; <\/a><a href=\"#data620\">Ethics in Data and Computing<\/a><\/li>\n<li><a href=\"#math745\">MATH 745 &#8211; Applied Mathematics Thesis Proposal <\/a> and <a href=\"#math750\">MATH 750 &#8211; Applied Mathematics Thesis<\/a><\/li>\n<\/ul>\n<div><a id=msam-electives><\/a><\/div>\n<h4>Category 1 Electives (Pick 2, total of 6 credits)<\/h4>\n<ul>\n<li><a href=\"#math540\">MATH 540 &#8211; Cryptography<\/a><\/li>\n<li><a href=\"#math570\">MATH 570 &#8211; Applied Statistics<\/a><\/li>\n<li><a href=\"#math645\">MATH 645 &#8211; Numerical Analysis<\/a><\/li>\n<li><a href=\"#cmps645\">CMPS 645 &#8211; Analysis of Algorithms<\/a><\/li>\n<li><a href=\"#math685\">MATH 685 &#8211; Introduction to Experimental Design<\/a><\/li>\n<\/ul>\n<h4>Category 2 Electives (Pick 3, total of 9 credits)<\/h4>\n<ul>\n<li><a href=\"#cmps530\">CMPS 530 &#8211; Python for Data Science<\/a><\/li>\n<li><a href=\"#cmps531\">CMPS 531 &#8211; Data Structures and Algorithms<\/a><\/li>\n<li><a href=\"#cmps547\">CMPS 547 &#8211; Foundations of Computer Science<\/a><\/li>\n<li><a href=\"#cmps620\">CMPS 620 &#8211; Machine Learning<\/a><\/li>\n<li><a href=\"#cmps620\">CMPS 664 &#8211; Big Data and Database Design<\/a><\/li>\n<li><a href=\"#data601\">DATA 601 &#8211; Introduction to Data Science<\/a><\/li>\n<li><a href=\"#data670\">DATA 670 &#8211; Data Visualization<\/a><\/li>\n<li><a href=\"#data687\">DATA 687 &#8211; Time Series Data<\/a><\/li>\n<li><a href=\"#math730\">MATH 730 &#8211; Fieldwork Experience<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>MS in Computer Science (30 credits)<\/div><div class=\"c_content\">\n<h4>Program Goals<\/h4>\n<ul>\n<li><strong>Goal 1:  Software Development<\/strong>. Apply computing theory and programming principles to analyze, design, implement, and evaluate computer-based systems, processes, components, and programs to meet desired needs.<\/li>\n<li><strong>Goal 2:  Problem Solving<\/strong>: Engage effectively and creatively in problem solving, including exploring multiple approaches, and assessing potential solutions.<\/li>\n<li><strong>Goal 3:  Mathematical Reasoning<\/strong>:   Reason in mathematical arguments at an advanced level, including posing problems precisely, articulating assumptions and limitations of the approach, and reasoning logically to conclusions.<\/li>\n<li><strong>Goal 3: Presentation and Communication<\/strong>:  Students must be able to communicate their analysis, model, and implementation strategies and create effective visualizations to support their analysis.<\/li>\n<li><strong>Goal 4: Integrated Skills<\/strong>:  Students will be able to integrate the skills described above in fieldwork\/thesis projects.<\/li>\n<li><strong>Goal 5: Ethics in Work<\/strong>. Recognize ethical and responsible conduct and learn to apply them in practice<\/li>\n<\/ul>\n<h4>Learning Outcomes<\/h4>\n<ul>\n<li><strong>Outcome 1:<\/strong>  Demonstrate advanced computational and programming skills<\/li>\n<li><strong>Outcome 2: <\/strong> Demonstrate advanced problem solving skills.<\/li>\n<li><strong>Outcome 3:<\/strong>  Demonstrate advanced mathematical reasoning skills.<\/li>\n<li><strong>Outcome 4:<\/strong>  Demonstrate advanced skills in data presentation, communication, and visualization.<\/li>\n<li><strong>Outcome 5:<\/strong>  Demonstrate the ability to integrate skills in a multi-faceted technical project.<\/li>\n<li><strong>Outcome 6:<\/strong>  Demonstrate ethical awareness, the ability to do ethical reflection, and the ability to apply ethical principles in decision-making<\/li>\n<\/ul>\n<h4>Required Courses (12 credits)<\/h4>\n<ul>\n<li><a href=\"#cmps547\">CMPS 547 &#8211; Foundations of Computer Science<\/a> &#8211; Provides students with the theoretical foundations for advanced computer science work, specifically focused on computational and programming skills through the lens of real-world <b>software development<\/b>.<\/li>\n<li><a href=\"#cmps531\">CMPS 531 &#8211; Data Structures and Algorithms<\/a> &#8211; Enhances a students skillset in computational skills, problem solving, and mathematical reasoning though advanced algorithm development and analysis.<\/li>\n<li><a href=\"#data620\">DATA 620 &#8211; <\/a><a href=\"#data620\">Ethics in Data and Computing<\/a>Focuses on the ethics in work program outcome by exploring the ethics, policy, and laws surrounding technology in modern life.<\/li>\n<li><a href=\"#cmps745\">CMPS 745 &#8211; Computer Science Thesis Proposal<\/a> and <a href=\"#cmps750\">CMPS 750 &#8211; Computer Science Thesis<\/a>Ties together required and elective coursework to demonstrate mastery in presentation, communication, and visualization skills, the ability to integrate skills in a multi-faceted technical project, and the ability to apply and reflect upon ethical principles in decision making.<\/li>\n<\/ul>\n<div><a id=mscs-electives><\/a><\/div>\n<h4>Category 1 Electives (Pick 4, total of 12 credits)<\/h4>\n<p>Category 1 electives create opportunity for students to deeply study very specific sub-disciplines in Computer Science.  In each, students are reinforcing program goals of programming skills and problem solving, and gain experience in presentation, communication, and integrative skills through class projects.<\/p>\n<ul>\n<li><a href=\"#cmps530\">CMPS 530 &#8211; Python for Data Science<\/a><\/li>\n<li><a href=\"#cmps611\">CMPS 611 &#8211; Operating System Design<\/a><\/li>\n<li><a href=\"#cmps631\">CMPS 631 &#8211; Computer Architecture<\/a><\/li>\n<li><a href=\"#cmps620\">CMPS 620 &#8211; Machine Learning<\/a><\/li>\n<li><a href=\"#cmps664\">CMPS 664 &#8211; Big Data and Database Design<\/a><\/li>\n<li><a href=\"#cmps688\">CMPS 688 &#8211; Networks<\/a><\/li>\n<li><a href=\"#data670\">DATA 670 &#8211; Data Visualization<\/a><\/li>\n<li><a href=\"#data687\">DATA 687 &#8211; Time Series Data<\/a><\/li>\n<li><a href=\"#cmps730\">CMPS 730 &#8211; Fieldwork Experience<\/a><\/li>\n<\/ul>\n<h4>Category 2 Electives (Pick 2, total of 6 credits)<\/h4>\n<p>Category 2 electives focus on mathematical reasoning, along with problem solving and computational thinking.  Students will often use programming and computation in these classes to solve mathematical and algorithmic problems.  The courses all provide students with experience in <i>analysis<\/i>.<\/p>\n<ul>\n<li><a href=\"#cmps646\">CMPS 645 &#8211; Analysis of Algorithms<\/a><\/li>\n<li><a href=\"#math540\">MATH 540 &#8211; Cryptography<\/a><\/li>\n<li><a href=\"#math562\">MATH 562 &#8211; Applied Linear Algebra<\/a><\/li>\n<li><a href=\"#math570\">MATH 570 &#8211; Applied Statistics<\/a><\/li>\n<li><a href=\"#math645\">MATH 645 &#8211; Numerical Analysis<\/a><\/li>\n<li><a href=\"#math654\">MATH 654 &#8211; Applied Probability<\/a><\/li>\n<li><a href=\"#math680\">MATH 680 \u2013 Advanced Mathematical Modeling<\/a><\/li>\n<li><a href=\"#math685\">MATH 685 &#8211; Introduction to Experimental Design<\/a><\/li>\n<\/ul>\n<\/div><\/div>\n<div class=\"divider\"><img decoding=\"async\" src=\"\/wp-content\/themes\/rcnjrd\/images\/icons\/ramapo-arch-icom_rule.png\" alt=\"Ramapo\" \/><\/div>\n<h2>Graduate Course Schedule<\/h2>\n<p>Below are <i>tentative<\/i> schedules for the next few academic years. Depending on your program (MSDS, MSCS, MSAM), you can see whether each course is a requirements or an elective, and plan out your time at Ramapo. Please do keep in mind that the further these schedule go out into the future, the more they are subject to change &#8211; so always consult your Program Director and review each semester&#8217;s schedule carefully when registration begins!<\/p>\n<p>Please see the catalog below for course descriptions.<\/p>\n<p><b>R<\/b> &#8211; Required Course<br \/>\n<b>E<\/b> &#8211; Elective Course<\/p>\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 530<\/td>\n<td>Python for Data Science<\/td>\n<td><b>Every Fall semester<\/b><br \/>Online (async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 531<\/td>\n<td>Data Structures and Algorithms<\/td>\n<td><b>Every Spring semester<\/b><br \/>Thursday 8:00pm-9:15pm<\/td>\n<td><\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 547<\/td>\n<td>Foundations of Computer Science<\/td>\n<td><b>Every Fall semester<\/b><br \/>Thursday 6:05pm-7:20pm<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 601<\/td>\n<td>Introduction to Data Science<\/td>\n<td><b>Every Fall semester<\/b><br \/>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td><\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 611<\/td>\n<td>Operating Systems<\/td>\n<td><b>Fall 2026<\/b><br \/>Online (Async)<\/td>\n<td><\/td>\n<td>E<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>CMPS 620<\/td>\n<td>Machine Learning<\/td>\n<td><b>Summer 2027, 2029<\/b><br \/>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 631<\/td>\n<td>Computer Architecture<\/td>\n<td><b>Fall 2028<\/b><br \/>Online (Async)<\/td>\n<td><\/td>\n<td>E<\/td>\n<td><\/td>\n<\/tr>\n<tr>\n<td>CMPS 664<\/td>\n<td>Big Data and Database Design<\/td>\n<td><b>Every Spring semester<\/b><br \/>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 680<\/td>\n<td>Advanced Mathematical Modeling<\/td>\n<td><b>Every Fall semester<\/b><br \/>Thursday 8:00pm-9:15pm<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>DATA 620<\/td>\n<td>Ethics in Data and Computing<\/td>\n<td><b>Every Spring semester<\/b><br \/>Thursday 6:05pm-7:20pm (Virtual)<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>DATA 670<\/td>\n<td>Data Visualization<\/td>\n<td><b>Spring 2028, 2030<\/b><br \/>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 687<\/td>\n<td>Time Series Data<\/td>\n<td><b>Summer 2028<\/b> <br \/>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 690<\/td>\n<td>Topics: Data Communication<\/td>\n<td><b>Spring 2026<\/b><br \/>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 540<\/td>\n<td>Cryptography<\/td>\n<td><b>Summer 2026, 2030<\/b><br \/>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 562<\/td>\n<td>Applied Linear Algebra<\/td>\n<td><b>Fall 2027, 2029<\/b>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>MATH 570<\/td>\n<td>Applied Statistics<\/td>\n<td><b>Every Summer<\/b><br \/>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>MATH 654<\/td>\n<td>Applied Probability<\/td>\n<td><b>Spring 2027, 2029<\/b><br \/>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Fall 2025<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 530<\/td>\n<td>Python for Data Science<\/td>\n<td>Online (async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 547<\/td>\n<td>Foundations of Computer Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 601<\/td>\n<td>Introduction to Data Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td><\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 680<\/td>\n<td>Advanced Mathematical Modeling<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Spring 2026<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<td>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 531<\/td>\n<td>Data Structures and Algorithms<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td><\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 664<\/td>\n<td>Big Data and Database Design<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 620<\/td>\n<td>Ethics in Data and Computing<\/td>\n<td>Thursday 6:05pm-7:20pm (Virtual)<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>DATA 690<\/td>\n<td>Topics: Data Communication<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Summer 2026<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DATA 670<\/td>\n<td>Data Visualization<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<tr>\n<td>MATH 570<\/td>\n<td>Applied Statistics<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Fall 2026<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 547<\/td>\n<td>Foundations of Computer Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 601<\/td>\n<td>Introduction to Data Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td><\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 680<\/td>\n<td>Advanced Mathematical Modeling<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 530<\/td>\n<td>Python for Data Science<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 611<\/td>\n<td>Operating Systems<\/td>\n<td>Online (Async)<\/td>\n<td><\/td>\n<td>E<\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Spring 2027<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DATA 620<\/td>\n<td>Ethics in Data and Computing<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 531<\/td>\n<td>Data Structures and Algorithms<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td><\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 664<\/td>\n<td>Big Data and Database Design<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 654<\/td>\n<td>Applied Probability<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Summer 2027<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 620<\/td>\n<td>Machine Learning<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 570<\/td>\n<td>Applied Statistics<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Fall 2027<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 547<\/td>\n<td>Foundations of Computer Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 601<\/td>\n<td>Introduction to Data Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td><\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 680<\/td>\n<td>Advanced Mathematical Modeling<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 530<\/td>\n<td>Python for Data Science<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 562<\/td>\n<td>Applied Linear Algebra<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Spring 2028<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DATA 620<\/td>\n<td>Ethics in Data and Computing<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 531<\/td>\n<td>Data Structures and Algorithms<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td><\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 664<\/td>\n<td>Big Data and Database Design<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 670<\/td>\n<td>Data Visualization<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Summer 2028<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DATA 687<\/td>\n<td>Time Series Data<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 570<\/td>\n<td>Applied Statistics<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Fall 2028<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 547<\/td>\n<td>Foundations of Computer Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 601<\/td>\n<td>Introduction to Data Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td><\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 680<\/td>\n<td>Advanced Mathematical Modeling<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 530<\/td>\n<td>Python for Data Science<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 631<\/td>\n<td>Computer Architecture<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Spring 2029<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DATA 620<\/td>\n<td>Ethics in Data and Computing<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 531<\/td>\n<td>Data Structures and Algorithms<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td><\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 664<\/td>\n<td>Big Data and Database Design<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 654<\/td>\n<td>Applied Probability<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Summer 2029<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 620<\/td>\n<td>Machine Learning<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 570<\/td>\n<td>Applied Statistics<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Fall 2029<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 547<\/td>\n<td>Foundations of Computer Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 601<\/td>\n<td>Introduction to Data Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td><\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 680<\/td>\n<td>Advanced Mathematical Modeling<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 530<\/td>\n<td>Python for Data Science<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 631<\/td>\n<td>Computer Architecture<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td><\/td>\n<td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Spring 2029<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DATA 620<\/td>\n<td>Ethics in Data and Computing<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 531<\/td>\n<td>Data Structures and Algorithms<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td><\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 664<\/td>\n<td>Big Data and Database Design<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 654<\/td>\n<td>Applied Probability<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Summer 2029<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 620<\/td>\n<td>Machine Learning<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 570<\/td>\n<td>Applied Statistics<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Fall 2029<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 547<\/td>\n<td>Foundations of Computer Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 601<\/td>\n<td>Introduction to Data Science<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td><\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 680<\/td>\n<td>Advanced Mathematical Modeling<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 530<\/td>\n<td>Python for Data Science<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 562<\/td>\n<td>Applied Linear Algebra<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Spring 2030<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>DATA 620<\/td>\n<td>Ethics in Data and Computing<\/td>\n<td>Thursday 6:05pm-7:20pm<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<td>R<\/td>\n<\/tr>\n<tr>\n<td>CMPS 531<\/td>\n<td>Data Structures and Algorithms<\/td>\n<td>Thursday 8:00pm-9:15pm<\/td>\n<td><\/td>\n<td>R<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>CMPS 664<\/td>\n<td>Big Data and Database Design<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>DATA 670<\/td>\n<td>Data Visualization<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Summer 2030<\/div><div class=\"c_content\">\n<table class=\"table table-hover table-striped table-bordered\">\n<thead>\n<tr>\n<th>Course<\/th>\n<th>Title<\/th>\n<th>Day\/Time<\/th>\n<th>MSDS<\/th>\n<th>MSCS<\/th>\n<th>MSAM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>CMPS 620<\/td>\n<td>Machine Learning<\/td>\n<td>Online (Async)<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<td>E<\/td>\n<\/tr>\n<tr>\n<td>MATH 570<\/td>\n<td>Applied Statistics<\/td>\n<td>Online (Async)<\/td>\n<td>R<\/td>\n<td>E<\/td>\n<td>R<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div><\/div>\n<div class=\"divider\"><img decoding=\"async\" src=\"\/wp-content\/themes\/rcnjrd\/images\/icons\/ramapo-arch-icom_rule.png\" alt=\"Ramapo\" \/><\/div>\n<h2>Graduate Course Catalog<\/h2>\n<a id=cmps530><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps530\"><\/a>CMPS 530 &#8211; Python for Data Science<\/h4>\n<p style=\"margin-top: 5px\">This course introduces students to the fundamental programming concepts and skills utilized by Data Scientists \u00bb in particular parallel computing, input\/output, and visualization &#8211; all through the Python programming language and associated libraries (i.e. numpy, pandas, etc). The course is suitable for students with a basic knowledge of programming, and prepares students to take more advanced computing courses in databases, big data analytics, machine learning, and other DATA and CMPS electives.<\/p>\n<a id=cmps531><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps531\"><\/a>CMPS 531 &#8211; Data Structures and Algorithms<\/h4>\n<p style=\"margin-top: 5px\">The first part covers data structures to efficiently store, organize, modify, and access data. Topics include: arrays, stacks, queues, linked lists, trees, hash tables, priority queues, and graphs. The second part covers the design and analysis of algorithms for solving computer science problems. Topics include: algorithm analysis, exhaustive search algorithms, divide-and-conquer algorithms, greedy algorithms, and dynamic programming algorithms.<\/p>\n<a id=cmps547><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps547\"><\/a>CMPS 547 &#8211; Foundations of Computer Science<\/h4>\n<p style=\"margin-top: 5px\">This course provides a foundational overview of programming language design, including compiled languages as well as higher level scripting languages. The course introduces students to concepts such as grammars, binding, scope, flow control, and data abstraction &#8211; through the lense of multiple languages. Students will also gain experience programming across language interfaces.<\/p>\n<a id=cmps611><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps611\"><\/a>CMPS 611 &#8211; Operating System Design<\/h4>\n<p style=\"margin-top: 5px\">An advanced study of the design, use, and analysis of operating systems. The course will include a study of the supportive computer architecture, memory management, process management, multi-core application design, device control, and evaluation of modern operating system design.<\/p>\n<a id=cmps620><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps620\"><\/a>CMPS 620 &#8211; Machine Learning<\/h4>\n<p style=\"margin-top: 5px\">This course provides students with the mathematical underpinnings of machine learning along with the software development skills to develop models and integrate machine learning into practical applications. Topics include foundational techniques such as linear and logistic regression and extend to deep learning and other state of the art techniques. Students will also obtain an understanding of both the capabilities and limitations of machine learning, and its responsible applications.<\/p>\n<a id=cmps631><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps631\"><\/a>CMPS 631 &#8211; Computer Architecture<\/h4>\n<p style=\"margin-top: 5px\">Topics in processor design and architecture, addressing modes, design and management of memory hierarchies, and virtual memory, multiprocessing, multitasking, process communications, principles of pipeline processing, classification of parallel architectures and data flow architectures. This course will provide the insight necessary to develop more effective software. The key goal of the course is to provide students a deep background in how digital microprocessors work on a fundamental level. Students will learn how architecture, instructions, and data flow in modern CPUs are implemented in hardware, and how these map to higher level programming languages and the operating system, which are covered in other courses within the curriculum. An emphasis on simulation and design will expose students to the types of problem solving and programming skills required at the hardware level of a computer system.<\/p>\n<a id=cmps645><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps645\"><\/a>CMPS 645 &#8211; Analysis of Algorithms<\/h4>\n<p style=\"margin-top: 5px\">This course provides a study of computer algorithms at an advanced level. The emphasis is on the design of efficient algorithms and data structures, proofs of their correctness, and analysis of their complexity. A number of algorithmic concepts and techniques are covered, including recursion, incremental design, divide-and-conquer, greedy algorithms, amortized analysis, and dynamic programming. Advanced data structures such as AVL trees, red-black trees, and B trees are discussed. Applications of algorithms will come from a variety of fields, such as data compression, encoding digital images, feature extraction as used in machine learning, efficient database access, and bioinformatics. The topics of NP-hard and NP-complete problems will be examined.<\/p>\n<a id=cmps664><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps664\"><\/a>CMPS 664 &#8211; Big Data and Database Design<\/h4>\n<p style=\"margin-top: 5px\">The study of the design and implementation of both relational and distributed databases. The course will include parallel programming models for large-scale data processing on a large number of computing systems, \ufb01le systems to store large data sets across a network of machines, tools to create and manage clusters of large number of processing units. Methods to maintain data consistency during large-scale l\/O operation on disk are also covered.<\/p>\n<a id=cmps688><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps688\"><\/a>CMPS 688 &#8211; Networks<\/h4>\n<p style=\"margin-top: 5px\">The design and implementation of network applications will be presented in this course. TCP\/IP using Berkeley Sockets will provide the network interface.<\/p>\n<a id=cmps730><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps730\"><\/a>CMPS 730 &#8211; Fieldwork Experience<\/h4>\n<p style=\"margin-top: 5px\">This is a projects-based course, developed in conjunction with industry sponsors and faculty. Students work closely with faculty and sponsors over one or more semesters on a Computer Science project. The program merges aspects of a co-op or internship and faculty-mentored independent study or thesis. Students will be required to \ufb01le progress reports with the faculty project mentor, along with a \ufb01nal presentation submitted to the project stakeholders and Computer Science convening group.<\/p>\n<p>Opportunities to enroll in this course are subject to the availability of projects sponsored by the College, industry or government representatives, or faculty. Availability will be announced prior to each semester, and students may enroll in the course to ful\ufb01ll one of their Category 1 electives. Enrollment will be granted at the discretion of participating faculty and the Computer Science program director.<\/p>\n<a id=cmps745><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps745\"><\/a>CMPS 745 &#8211; Computer Science Thesis Proposal<\/h4>\n<p>All M.S. students will be required to complete a Master\u2019s Thesis under the advisement of a faculty<br \/>\nmember. Thesis projects represent independent research, developed and implemented by the student.<br \/>\nPrior to registration for Master\u2019s Thesis, students must complete this Master\u2019s Thesis Proposal course,<br \/>\nwhere they will learn how to identify quality thesis topics, conduct research, and develop their Thesis<br \/>\nproposal. This course is Pass\/Fail, with a passing grade subject to the student obtaining approval of a<br \/>\nformal thesis proposal, along with signatures from their Thesis advisors and readers. Completion of this<br \/>\ncourse will allow students to register for Master\u2019s Thesis in a subsequent semester<\/p>\n<a id=cmps750><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#cmps750\"><\/a>CMPS 750 &#8211; Computer Science Thesis<\/h4>\n<p style=\"margin-top: 5px\">All M.S. students will be required to complete a Master\u2019s Thesis under the advisement of a faculty member. This requirement is distinct from any Fieldwork Experiences the students participate in &#8211; however students participating in Fieldwork Experiences for more than one semester may have their thesis requirement waived.<\/p>\n<p>Thesis projects represent independent research, developed and implemented by the student. Thesis projects require a written thesis to be submitted to the faculty advisor, along with a panel of Computer Science faculty. The project also requires an oral presentation, made to the faculty advisor and panel. Grading is P\/F for this course.<\/p>\n<p>Enrollment requires students to meet with the Computer Science Graduate Program Director, obtain approval of a project idea, and select a faculty advisor prior to registration. Registration for Spring semester requires project approval and advisor selection prior to the end of October, registration for Fall semester requires project approval and advisor selection prior to the end of April.<\/p>\n<a id=data601><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#data601\"><\/a>DATA 601 &#8211; Introduction to Data Science<\/h4>\n<p style=\"margin-top: 5px\">This course serves as the foundation for all DATA graduate level coursework. It will cover programming, data analysis, data visualization, ethics and security\/privacy concerns surrounding data and other topics students are expected to master in the program. The course will also feature a Seminar component designed to acclimate students to working with industry Sponsors and to hear \ufb01rst hand from people working in Data Science.<\/p>\n<a id=data620><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#data620\"><\/a>DATA 620 &#8211; Ethics in Data and Computing<\/h4>\n<p style=\"margin-top: 5px\">This course is focused on ethical concerns around the acquisition, preparation, storage, and usage of data, and the applications of computing. The course may include topics such as: fairness in machine learning, technical bias, data protection (including privacy-preserving mechanisms), regulatory frameworks, transparency and interpretability, and proper use vs. misuse of collected data. In discussing these topics, students will consider varying stakeholder perspectives and weigh the inherent trade-offs faced by decision makers.<\/p>\n<a id=data670><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#data670\"><\/a>DATA 670 &#8211; Data Visualization<\/h4>\n<p style=\"margin-top: 5px\">The course provides students with the fundamental concepts and tools needed to understand the emerging role of data visualization in organizations. Students will learn techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course will also provide a tool based exposure for visualization and communication of the visual analysis. This course is cross listed with MBAD670.<\/p>\n<a id=data687><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#data687\"><\/a>DATA 687 &#8211; Time Series Data<\/h4>\n<p style=\"margin-top: 5px\">This course will cover sequential and time series data analysis techniques. Students will apply mathematical models aimed at describing time series processes and using these models to create visualizations, predictions, and insights into the data. Practical applications of these concepts will be supported though lab sessions utilizing common programming languages is the field, such as R or Python.<\/p>\n<a id=data730><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#data730\"><\/a>DATA 730 &#8211; Fieldwork Experience<\/h4>\n<p style=\"margin-top: 5px\">This is a projects-based course, developed in conjunction with industry sponsors and faculty. Students work closely with faculty and sponsors over one or more semesters on a domain-speci\ufb01c Data Science project. The program merges aspects of a co-op or internship and faculty-mentored independent study or thesis Students will be required to \ufb01le progress reports with the faculty project mentor, along with a \ufb01nal presentation submitted to the project stakeholders and Data Science convening group. Opportunities to enroll in this course are subject to the availability of projects sponsored by the College. industry or government representatives, or faculty. Availability will be announced prior to each semester, and students may enroll in the course to ful\ufb01ll one of their Data Science electives. Enrollment will be granted at the discretion of participating faculty and the Data Science program director.<\/p>\n<a id=data745><\/a>\n<h4>DATA 745 &#8211; Data Science Thesis Proposal<\/h4>\n<p>All M.S. students will be required to complete a Master\u2019s Thesis under the advisement of a faculty<br \/>\nmember. Thesis projects represent independent research, developed and implemented by the student.<br \/>\nPrior to registration for Master\u2019s Thesis, students must complete this Master\u2019s Thesis Proposal course,<br \/>\nwhere they will learn how to identify quality thesis topics, conduct research, and develop their Thesis<br \/>\nproposal. This course is Pass\/Fail, with a passing grade subject to the student obtaining approval of a<br \/>\nformal thesis proposal, along with signatures from their Thesis advisors and readers. Completion of this<br \/>\ncourse will allow students to register for Master\u2019s Thesis in a subsequent semester<\/p>\n<a id=data750><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#data750\"><\/a>DATA 750 &#8211; Data Science Thesis<\/h4>\n<p style=\"margin-top: 5px\">All M.S. students will be required to complete a Master\u2019s Thesis under the advisement of a faculty member. This requirement is distinct from any Fieldwork Experiences the students participate in &#8211; however students participating in Fieldwork Experiences for more than one semester may have their thesis requirement waived.<\/p>\n<p>Thesis projects represent independent research, developed and implemented by the student. Thesis projects require a written thesis to be submitted to the faculty advisor, along with a panel of Data Science faculty. The project also requires an oral presentation, made to the faculty advisor and panel. Grading is P\/F for this course.<\/p>\n<p>Enrollment requires students meet with the Data Science Graduate Program Directory, obtain approval of a project idea, and select a faculty advisor prior to registration. Registration for Spring semester requires project approval and advisor selection prior to the end of October, registration for Fall semester requires project approval and advisor selection prior to the end of April.<\/p>\n<a id=math540><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math540\"><\/a>MATH 540 &#8211; Cryptography<\/h4>\n<p style=\"margin-top: 5px\">Cryptography is the field of mathematics that looks at methods to establish safe means of communication, especially in the situation where an adversary is attempting to discover or subvert that communication. How can we guarantee the security of the messages we send, so that only the intended recipients of the message are able to read them?<\/p>\n<p>In this course we look at a variety of protocols that allow for provably secure communication. The student in this class will learn about public-key cryptography, block ciphers, elliptic curve cryptography, Weil pairing, lattice-based encryption, and zero knowledge proofs. These methods constitute current practice in the field of cryptography. We will also consider the threat and challenges to secure encryption from quantum computers.<\/p>\n<a id=math562><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math562\"><\/a>MATH 562 &#8211; Applied Linear Algebra<\/h4>\n<p style=\"margin-top: 5px\">This course is a foundational course for the study of Linear Algebraic structures used in a variety of scientific and computational applications, such as data fitting, clustering, feature engineering, image processing, machine learning, optimization, and dynamical systems. In order to achieve this purpose, this course will cover topics in linear algebra including vector and matrix operations, linear transformations, linear independence, norms, decomposition, and least squares.<\/p>\n<a id=math570><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math570\"><\/a>MATH 570 &#8211; Applied Statistics<\/h4>\n<p style=\"margin-top: 5px\">This course gives an introduction to statistical methods used in data science with an emphasis on applications. Topics may include foundations of probability, univariate and multivariate random variables and distributions, special distributions, Central Limit Theorem, one- and two-sample methods, point estimation, interval estimation, hypothesis testing, regression analysis, Bayesian analysis, data analysis and model building.<\/p>\n<a id=math645><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math645\"><\/a>MATH 645 &#8211; Numerical Analysis<\/h4>\n<p style=\"margin-top: 5px\">Numerical analysis is an essential tool for Data Science, Computer Science, and Applied Mathematics. It makes it possible to solve problems which are difficult or impossible for conventional mathematics. The topics in this course include: the solution of equations and systems of equations using Newton&#8217;s method and other iteration schemes, interpolation, method of least squares (linear and nonlinear regression) for curve fitting, numerical integration, numerical solution of systems of linear equations, solution of minimax problems, Monte Carlo methods and the numerical solution of ordinary differential equations. Error analysis will be performed on each method.<\/p>\n<a id=math654><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math654\"><\/a>MATH 654 &#8211; Applied Probability<\/h4>\n<p style=\"margin-top: 5px\">This course will cover the fundamental concepts in probability theory and their applications. Probability theory will introduce concepts for single random variables and then extend into multiple random variables, including distributions and classifications of random variables. Applications will focus on real-world problems utilizing data analysis and uncertainty. Computer simulations will be an embedded component throughout the course.<\/p>\n<a id=math680><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math680\"><\/a>MATH 680 \u2013 Advanced Mathematical Modeling<\/h4>\n<p style=\"margin-top: 5px\">This course requires students to develop, use, and assess models to solve real-world problems using the mathematical modeling process. Models developed in a variety of disciplines, including linear programming, network science, decision theory, machine learning, are studied and used to solve problems in other disciplines.<\/p>\n<a id=math685><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math685\"><\/a>MATH 685 &#8211; Introduction to Experimental Design<\/h4>\n<p style=\"margin-top: 5px\">This course will cover the principles of experimental design including the collection, analysis and interpretation of data acquired from designed experiments. The identification and implementation of a variety of design structures will be studied, along with the statistical model and methods of<br \/>\nanalyzing data for each of these designs. The assessment of whether or not model assumptions are sufficiently met will be emphasized, along with how to clearly summarize and present the results of the analysis of a designed experiment.<\/p>\n<a id=math730><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math730\"><\/a>MATH 730 &#8211; Fieldwork Experience<\/h4>\n<p style=\"margin-top: 5px\">This is a projects-based course, developed in conjunction with industry sponsors and faculty. Students work closely with faculty and sponsors over one or more semesters on an Applied Mathematics project. The program merges aspects of a co-op or internship and faculty-mentored independent study or thesis. Students will be required to \ufb01le progress reports with the faculty project mentor, along with a \ufb01nal presentation submitted to the project stakeholders and Mathematics convening group.<\/p>\n<p>Opportunities to enroll in this course are subject to the availability of projects sponsored by the College, industry or government representatives, or faculty. Availability will be announced prior to each semester, and students may enroll in the course to ful\ufb01ll one of their Category 2 electives. Enrollment will be granted at the discretion of participating faculty and the Applied Mathematics program director.<\/p>\n<a id=math745><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math745\"><\/a>MATH 745 &#8211; Applied Mathematics Thesis Proposal<\/h4>\n<p>All M.S. students will be required to complete a Master\u2019s Thesis under the advisement of a faculty<br \/>\nmember. Thesis projects represent independent research, developed and implemented by the student.<br \/>\nPrior to registration for Master\u2019s Thesis, students must complete this Master\u2019s Thesis Proposal course,<br \/>\nwhere they will learn how to identify quality thesis topics, conduct research, and develop their Thesis<br \/>\nproposal. This course is Pass\/Fail, with a passing grade subject to the student obtaining approval of a<br \/>\nformal thesis proposal, along with signatures from their Thesis advisors and readers. Completion of this<br \/>\ncourse will allow students to register for Master\u2019s Thesis in a subsequent semester<\/p>\n<a id=math750><\/a>\n<h4 style=\"margin-top: 10px;margin-bottom: 5px\"><a name=\"#math750\"><\/a>MATH 750 &#8211; Applied Mathematics Thesis<\/h4>\n<p style=\"margin-top: 5px\">All M.S. students will be required to complete a Master\u2019s Thesis under the advisement of a faculty member. This requirement is distinct from any Fieldwork Experiences the students participate in &#8211; however students participating in Fieldwork Experiences for more than one semester may have their thesis requirement waived.<\/p>\n<p>Thesis projects represent independent research, developed and implemented by the student. Thesis projects require a written thesis to be submitted to the faculty advisor, along with a panel of Mathematics faculty. The project also requires an oral presentation, made to the faculty advisor and panel. Grading is P\/F for this course.<\/p>\n<p>Enrollment requires students to meet with the Applied Mathematics Graduate Program Director, obtain approval of a project idea, and select a faculty advisor prior to registration. Registration for Spring semester requires project approval and advisor selection prior to the end of October, registration for Fall semester requires project approval and advisor selection prior to the end of April.<\/p>\n<div class=\"divider\"><img decoding=\"async\" src=\"\/wp-content\/themes\/rcnjrd\/images\/icons\/ramapo-arch-icom_rule.png\" alt=\"Ramapo\" \/><\/div>\n<h2>Graduate Certificate Programs<\/h2>\n<p>The Center supports three Graduate Certificate Programs, all of which utilize the graduate coursework offered as part of the MS in Data Science degree. Students completing all three will have two remaining courses to complete their MSDS &#8211; DATA 620 Ethics in Data and Computing and their MSDS Thesis (DATA 750).<\/p>\n<p>Each certificate is a three-course sequence, with two required courses and one elective. The courses are conveniently offered hybrid with one evening per week on campus to make them ideal for working professionals.<\/p>\n<p>See below for specific certificate requirements.<\/p>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Certificate #1: Data Analyst (9 credits)<\/div><div class=\"c_content\"><br \/>\nThe Analyst certificate is geared towards students most interested in learning data\/business analytics skills &#8211; the interpretation and presentation of data.<\/p>\n<h4>Required Courses (6 credits)<\/h4>\n<ul>\n<li><a href=\"#data601\">DATA 601 &#8211; Introduction to Data Science<\/a><\/li>\n<li><a href=\"#data670\">DATA 670 &#8211; Data Visualization<\/a><\/li>\n<\/ul>\n<div><a id=cert1_electives><\/a><\/div>\n<h4>Select 1 Elective (3 credits)<\/h4>\n<ul>\n<li><a id=\"69e161a6cde63\" href=\"https:\/\/catalog.ramapo.edu\/courses\/BADM501\" target=\"_blank\">BADM 501 - DATA ANALYTICS<\/a><\/li>\n<li><a id=\"69e161a6ce0ee\" href=\"https:\/\/catalog.ramapo.edu\/courses\/MBAD615\" target=\"_blank\">MBAD 615 - BUSINESS ANALYTICS<\/a><\/li>\n<\/ul>\n<p><\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Certificate #2: Data Modeler (9 credits)<\/div><div class=\"c_content\"><br \/>\nThe Modeler certificate provides skills in statistics and underlying methods of synthesizing raw data into actionable information.<\/p>\n<h4>Required Courses (6 credits)<\/h4>\n<ul>\n<li><a href=\"#math570\">MATH 570 &#8211; Applied Statistics<\/a><\/li>\n<li><a href=\"#math680\">MATH 680 &#8211; Advanced Mathematical Modeling<\/a><\/li>\n<\/ul>\n<div><a id=cert2_electives><\/a><\/div>\n<h4>Select 1 Elective (3 credits)<\/h4>\n<ul>\n<li><a href=\"#cmps620\">CMPS 620 &#8211; Machine Learning<\/a><\/li>\n<li><a href=\"#math562\">MATH 562 &#8211; Applied Linear Algebra<\/a><\/li>\n<li><a href=\"#data670\">DATA 670 &#8211; Data Visualization<\/a><\/li>\n<li><a href=\"#data687\">DATA 687 &#8211; Time Series Data<\/a><\/li>\n<\/ul>\n<p><\/div><\/div>\n<div class=\"collapsableContent\" tabindex=\"0\"><div class=\"collapsableTitle\"><span class=\"fa-stack\"><i class=\"fa fa-circle fa-stack-2x\"><\/i><i class=\"fa fa-chevron-down fa-stack-1x fa-inverse\"><\/i><i class=\"fa fa-chevron-up fa-stack-1x fa-inverse\"><\/i><\/span>Certificate #3: Machine Learning Engineer (9 credits)<\/div><div class=\"c_content\"><br \/>\nThe Machine Learning Engineer certificate is for students seeking to develop the software, programming skills, and mathematical underpinnings to work with machine learning and big data.<\/p>\n<h4>Required Courses (6 credits)<\/h4>\n<ul>\n<li><a href=\"#cmps530\">CMPS 530 &#8211; Python for Data Science<\/a><\/li>\n<li><a href=\"#cmps620\">CMPS 620 &#8211; Machine Learning<\/a><\/li>\n<\/ul>\n<div><a id=cert3_electives><\/a><\/div>\n<h4>Select 1 Elective (3 credits)<\/h4>\n<ul>\n<li><a href=\"#cmps664\">CMPS 664 &#8211; Big Data and Database Design<\/a><\/li>\n<li><a href=\"#math562\">MATH 562 &#8211; Applied Linear Algebra<\/a><\/li>\n<li><a href=\"#data670\">DATA 670 &#8211; Data Visualization<\/a><\/li>\n<li><a href=\"#data687\">DATA 687 &#8211; Time Series Data<\/a><\/li>\n<\/ul>\n<p><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The Center for Data, Mathematical, and Computational Sciences (DMC) at 69色情视频 offers an integrated ecosystem of programs designed around shared foundations and flexible pathways. Students can pursue a BS in Computer Science, Data Science, Cybersecurity, or Mathematics, with all four programs sharing core coursework in programming, data structures, and mathematical reasoning. This common foundation [&hellip;]<\/p>\n","protected":false},"author":214,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-17","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.5 (Yoast SEO v27.1.1) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Curriculum - Center for Data, Mathematical and Computational Sciences || 69色情视频 of New Jersey<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ramapo.edu\/dmc\/curriculum\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Curriculum\" \/>\n<meta property=\"og:description\" content=\"The Center for Data, Mathematical, and Computational Sciences (DMC) at 69色情视频 offers an integrated ecosystem of programs designed around shared foundations and flexible pathways. 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