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"id": 249165,
"description_type": {
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"name": "Source Catalog Curriculum"
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"description": "<h1>Track Prerequisites</h1><p>For admission, an undergraduate degree in Computer Science, Statistics, Computer Engineering, or Information Technology is desirable but not required. Applicants without a strong undergraduate background in Computer Science or Statistics must demonstrate an understanding of the material covered in the following undergraduate courses, by either taking these courses or by convincing the program that the student’s work experience or courses at other institutions have covered this material:</p> <ul> <li>COP 3330 Object-Oriented Programming</li> <li>COP 3503C Computer Science II</li> <li>COP 4710 Database Systems</li> <li>STA 2023 Statistical Methods I</li> <li>Programming experience or STA 4164 (Statistical Methods III)</li> </ul> <p>The program’s director, assisted by the program’s faculty, will evaluate student applications. At the discretion of the director, prospective students with sufficient industry experience in computer programming will be deemed to have programming experience and the director will decide which of the prerequisites the student may need to make up as a non-degree seeking student (at UCF or elsewhere).</p><h1>Degree Requirements</h1><div><section><header data-test=\"grouping-0-header\"><div><h2 data-testid=\"grouping-label\"><span>Required Courses</span></h2></div><div><span>30</span><span>Total Credits</span></div><div><div><button aria-label=\"Collapse\"><i></i></button></div></div></header><div><div><ul><li data-test=\"ruleView-A\"><div data-test=\"ruleView-A-result\">Complete the following: <div><ul style=\"margin-top:5px;margin-bottom:5px\"><li><span><a href=\"#/courses/view/60ca8154a8d2fb1d302d8585\" target=\"_blank\">STA5206</a> <!-- -->-<!-- --> <!-- -->Statistical Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a636b6b62568940006e\" target=\"_blank\">CNT5805</a> <!-- -->-<!-- --> <!-- -->Network Science<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8154a8d2fb7d132d8586\" target=\"_blank\">STA5703</a> <!-- -->-<!-- --> <!-- -->Data Mining Methodology I<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a2b5a1583b5779e6c28\" target=\"_blank\">CAP5610</a> <!-- -->-<!-- --> <!-- -->Machine Learning<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8158a38edf5c413ecb2a\" target=\"_blank\">STA6704</a> <!-- -->-<!-- --> <!-- -->Data Mining Methodology II<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca80b65a1583f9bd9e730b\" target=\"_blank\">PHI6679</a> <!-- -->-<!-- --> <!-- -->Digital Ethics<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a2b00f8275c0e140ce2\" target=\"_blank\">CAP6614</a> <!-- -->-<!-- --> <!-- -->Current Topics in Machine Learning<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a2b5a158365449e6c2c\" target=\"_blank\">CAP6640</a> <!-- -->-<!-- --> <!-- -->Computer Understanding of Natural Language<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a2ba8d2fbaff52d8046\" target=\"_blank\">CAP5636</a> <!-- -->-<!-- --> <!-- -->Advanced Artificial Intelligence<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a30714b5f6964521f73\" target=\"_blank\">CAP6942</a> <!-- -->-<!-- --> <!-- -->Project in Data Analytics<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li></ul></div></div></li></ul></div></div></section><h3>Grand Total Credits:<!-- --> <strong>30</strong></h3></div><h1>Financial Information</h1><p>Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies <a href=\"https://funding.graduate.ucf.edu/\" target=\"_blank\">Funding website</a>, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.</p> <p><strong>UCF Student Financial Assistance</strong><br />Millican Hall 120<br />Telephone: 407-823-2827<br />Appointment Line: 407-823-5285<br />Fax: 407-823-5241<br /><a href=\"mailto:finaid@ucf.edu\">finaid@ucf.edu</a><br /><a href=\"http://finaid.ucf.edu/\" target=\"_blank\">Website</a></p><h1>Fellowship Information</h1><p>Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see <a href=\"https://graduate.ucf.edu/fellowships/\" target=\"_blank\">UCF Graduate Fellowships</a>, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.</p> <p><strong>Grad Fellowships</strong><br />Telephone: 407-823-0127<br /><a href=\"mailto:gradfellowship@ucf.edu\">gradfellowship@ucf.edu</a><br /><a href=\"https://funding.graduate.ucf.edu/\" target=\"_blank\">Website</a></p><p>The MSDA-AI track will be an intense, 5 (or 4) semester long program intended for individuals with STEM heavy majors and some quantitate work experience. The educational goal of the program is to bridge a student’s undergraduate degree with artificial intelligence applications in a practical context, providing fundamental knowledge and in demand skills for the technical marketplace.</p> <p><strong>PROGRAM HIGHLIGHTS</strong></p> <ul> <li>Less than 2 years, intensive course schedule. Program can be completed in four or five consecutive academic terms: Fall, Spring & Summer</li> <li>Coursework covering the fundamentals of data science, analytics, machine learning, deep learning, and artificial intelligence.</li> <li>Applied, integrated real-world capstone project</li> </ul> <p><strong>SAMPLE COURSE SCHEDULE</strong></p> <p><strong>Semester 1 (Fall)</strong></p> <p>STA 5206 Statistical Analysis</p> <p>CNT 5805 Network Science</p> <p><strong>Semester 2 (Spring)</strong></p> <p>STA 5703 Data Mining Methodology I</p> <p>CAP 5610 Machine Learning</p> <p><strong>Semester 3 (Summer)</strong></p> <p>STA 6704 Data Mining Methodology II</p> <p>PHI 6679 Digital Ethics</p> <p><strong>Semester 4 (Fall)</strong></p> <p>CAP 6614 Current Topics in Machine Learning </p> <p>CAP 6640 Computer Understanding of Natural Language</p> <p><strong>Semester 5 (Spring)</strong></p> <p>CAP 5636 Advanced Artificial Intelligence</p> <p>CAP 6942 Project in Data Analytics</p>",
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"program": 2083
}