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"description_type": {
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"description": "<h1>Program Prerequisites</h1><p>Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses are: <a href=\"https://sciences.ucf.edu/math/course/mac-2311c-calculus-with-analytic-geometry-i/\">MAC 2311C: Calculus with Analytic Geometry I</a>, <a href=\"https://sciences.ucf.edu/math/course/mac-2312-calculus-with-analytic-geometry-ii/\">MAC 2312: Calculus with Analytic Geometry II</a>, <a href=\"https://sciences.ucf.edu/math/course/mac-2313-calculus-with-analytic-geometry-iii/\">MAC 2313: Calculus with Analytic Geometry III</a>, <a href=\"https://sciences.ucf.edu/math/course/mas-3105-matrix-and-linear-algebra/\">MAS 3105: Matrix and Linear Algebra</a> or <a href=\"https://sciences.ucf.edu/math/course/mas-3106-linear-algebra/\">MAS 3106: Linear Algebra</a>. These prerequisites are undergraduate courses offered through the Math department.</p><h1>Degree Requirements</h1><div><section><header data-test=\"grouping-0-header\"><div><h2 data-testid=\"grouping-label\"><span></span></h2></div><div><span>0</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\"><div>All Ph.D. students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.</div></div></li></ul></div></div></section><section><header data-test=\"grouping-1-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><span>Complete <!-- -->all<!-- --> of the following</span><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/64ef74d285f57171e920fdd5\" target=\"_blank\">STA6106</a> <!-- -->-<!-- --> <!-- -->Statistical Computing I<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815402fd3a04716d898d\" target=\"_blank\">STA6236</a> <!-- -->-<!-- --> <!-- -->Regression Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815802fd3ad7f86d8991\" target=\"_blank\">STA6326</a> <!-- -->-<!-- --> <!-- -->Theoretical Statistics I<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815ba38edf3fb73ecb2e\" target=\"_blank\">STA6327</a> <!-- -->-<!-- --> <!-- -->Theoretical Statistics II<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/631f432833d7637a3cd3fe59\" target=\"_blank\">STA6246</a> <!-- -->-<!-- --> <!-- -->Linear Models<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/64ef83cd0bd2ff5b1be968be\" target=\"_blank\">STA6107</a> <!-- -->-<!-- --> <!-- -->Statistical Computing II<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/631f42a13e0e0c8bb7e9a19e\" target=\"_blank\">STA6366</a> <!-- -->-<!-- --> <!-- -->Statistical Methodology for Data Science I<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/633f1f7b1ca9b32accfc56e9\" target=\"_blank\">STA6367</a> <!-- -->-<!-- --> <!-- -->Statistical Methodology for Data Science II<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815b5a15830bda9e74ff\" target=\"_blank\">STA7920</a> <!-- -->-<!-- --> <!-- -->Statistical Colloquium<!-- --> <span style=\"margin-left:5px\"></span></span></li><li><span><a href=\"#/courses/view/60ca81589d7535808687739c\" target=\"_blank\">STA7348</a> <!-- -->-<!-- --> <!-- -->Bayesian Modeling and Computation<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li></ul></div></div></li><li data-test=\"ruleView-B\"><div data-test=\"ruleView-B-result\">Complete at least <span>1</span> of the following: <div><ul style=\"margin-top:5px;margin-bottom:5px\"><li><span><a href=\"#/courses/view/60ca815ba38edfc77c3ecb2f\" target=\"_blank\">STA7722</a> <!-- -->-<!-- --> <!-- -->Statistical Learning Theory<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815b5a15833cc99e7500\" target=\"_blank\">STA7734</a> <!-- -->-<!-- --> <!-- -->Statistical Asymptotic Theory in Big Data<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li></ul></div></div></li></ul></li></ul></div></div></section><section><header data-test=\"grouping-2-header\"><div><h2 data-testid=\"grouping-label\"><span>Restricted Electives (at least 9 credit hours must be STA coursework)</span></h2></div><div><span>21</span><span>Total Credits</span></div><div><div><button aria-label=\"Collapse\"><i></i></button></div></div></header><div><div><ul><li><span>Complete <!-- -->all<!-- --> of the following</span><ul><li data-test=\"ruleView-A\"><div data-test=\"ruleView-A-result\"><div>\nOther courses may be included in a Plan of Study with departmental approval. Other electives can be used at the discretion of the student advisor and/or Graduate Coordinator.</div></div></li><li data-test=\"ruleView-B\"><div data-test=\"ruleView-B-result\">Earn at least <span>21</span> credits from the following: <div><ul style=\"margin-top:5px;margin-bottom:5px\"><li><span><a href=\"#/courses/view/64ef83cd0bd2ff5b1be968be\" target=\"_blank\">STA6107</a> <!-- -->-<!-- --> <!-- -->Statistical Computing II<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8154e6bc7991da73ec3d\" target=\"_blank\">STA6226</a> <!-- -->-<!-- --> <!-- -->Sampling Theory and Applications<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8154e6bc794fc973ec43\" target=\"_blank\">STA6237</a> <!-- -->-<!-- --> <!-- -->Nonlinear Regression<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8158e6bc794b6c73ec4c\" target=\"_blank\">STA6346</a> <!-- -->-<!-- --> <!-- -->Advanced Statistical Inference I<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca81589d7535045f87739d\" target=\"_blank\">STA6347</a> <!-- -->-<!-- --> <!-- -->Advanced Statistical Inference II<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815b5a1583748b9e74fe\" target=\"_blank\">STA6507</a> <!-- -->-<!-- --> <!-- -->Nonparametric Statistics<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca81585ada370939eca0cf\" target=\"_blank\">STA6662</a> <!-- -->-<!-- --> <!-- -->Statistical Methods for Industrial Practice<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/6212ec08833bc229d163688c\" target=\"_blank\">STA6705</a> <!-- -->-<!-- --> <!-- -->Data Mining Methodology III<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815860402bed76ae78d7\" target=\"_blank\">STA6707</a> <!-- -->-<!-- --> <!-- -->Multivariate Statistical Methods<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/6169a86003f6422fdb4a489e\" target=\"_blank\">STA6709</a> <!-- -->-<!-- --> <!-- -->Spatial Statistics<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca81589d7535760587739b\" target=\"_blank\">STA6857</a> <!-- -->-<!-- --> <!-- -->Applied Time Series Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8158e6bc79cbb773ec49\" target=\"_blank\">STA7239</a> <!-- -->-<!-- --> <!-- -->Dimension Reduction in Regression<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca815ba38edf22893ecb30\" target=\"_blank\">STA7719</a> <!-- -->-<!-- --> <!-- -->Survival Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca81585ada372407eca0d0\" target=\"_blank\">STA7935</a> <!-- -->-<!-- --> <!-- -->Current Topics in Big Data Analytics<!-- --> <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/60ca6a2ba38edf165d3ec20b\" target=\"_blank\">CAP6307</a> <!-- -->-<!-- --> <!-- -->Text Mining I<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a2b00f827a58d140ce1\" target=\"_blank\">CAP6315</a> <!-- -->-<!-- --> <!-- -->Social Media and Network Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a2b714b5f844d521f5d\" target=\"_blank\">CAP6318</a> <!-- -->-<!-- --> <!-- -->Computational Analysis of Social Complexity<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a3002fd3a48326d82ab\" target=\"_blank\">CAP6737</a> <!-- -->-<!-- --> <!-- -->Interactive Data Visualization<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a70a38edf53b33ec2d8\" target=\"_blank\">COP5537</a> <!-- -->-<!-- --> <!-- -->Network Optimization<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a7002fd3a9b276d8345\" target=\"_blank\">COP6526</a> <!-- -->-<!-- --> <!-- -->Parallel and Cloud Computation<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a7102fd3a70346d8348\" target=\"_blank\">COP6616</a> <!-- -->-<!-- --> <!-- -->Multicore Programming<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a71a8d2fbd8ae2d80b7\" target=\"_blank\">COT6417</a> <!-- -->-<!-- --> <!-- -->Algorithms on Strings and Sequences<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a73a38edfc1a83ec2ea\" target=\"_blank\">COT6505</a> <!-- -->-<!-- --> <!-- -->Computational Methods/Analysis I<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a8d00f82784cb140df3\" target=\"_blank\">ECM6308</a> <!-- -->-<!-- --> <!-- -->Current Topics in Parallel Processing<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/620d5cd59d660170e5c3989f\" target=\"_blank\">EEL5825</a> <!-- -->-<!-- --> <!-- -->Machine Learning and Pattern Recognition<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6b536b6b624a4a400146\" target=\"_blank\">EEL6760</a> <!-- -->-<!-- --> <!-- -->Data Intensive Computing<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/65b80e2c612f6d60efb1cf56\" target=\"_blank\">FIL6146</a> <!-- -->-<!-- --> <!-- -->Screenplay Refinement<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6b91a8d2fb2efe2d81ff\" target=\"_blank\">ESI6247</a> <!-- -->-<!-- --> <!-- -->Experimental Design and Taguchi Methods<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6b91a38edfa0c73ec4fb\" target=\"_blank\">ESI6358</a> <!-- -->-<!-- --> <!-- -->Decision Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6b9100f827ee57140f09\" target=\"_blank\">ESI6418</a> <!-- -->-<!-- --> <!-- -->Linear Programming and Extensions<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6b9100f8272af5140f0e\" target=\"_blank\">ESI6609</a> <!-- -->-<!-- --> <!-- -->Industrial Engineering Analytics for Healthcare<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6b91a38edf55463ec501\" target=\"_blank\">ESI6891</a> <!-- -->-<!-- --> <!-- -->IEMS Research Methods<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8154a38edf2b133ecb26\" target=\"_blank\">STA5825</a> <!-- -->-<!-- --> <!-- -->Stochastic Processes and Applied Probability Theory<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a71714b5fb749521ffe\" target=\"_blank\">COP6731</a> <!-- -->-<!-- --> <!-- -->Advanced Database Systems<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8154a38edffc2a3ecb25\" target=\"_blank\">STA5104</a> <!-- -->-<!-- --> <!-- -->Advanced Computer Processing of Statistical Data<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/654e612268b04a9ff77bf149\" target=\"_blank\">STA5176</a> <!-- -->-<!-- --> <!-- -->Introduction to Biostatistics<!-- --> <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/60ca81545a158313c29e74f3\" target=\"_blank\">STA6223</a> <!-- -->-<!-- --> <!-- -->Conventional Survey Methods<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca8154a38edf32093ecb23\" target=\"_blank\">STA6224</a> <!-- -->-<!-- --> <!-- -->Bayesian Survey Methods<!-- --> <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/60ca8158a8d2fb2f1e2d858a\" target=\"_blank\">STA6714</a> <!-- -->-<!-- --> <!-- -->Data Preparation<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca7fc3e6bc791b8e73e8df\" target=\"_blank\">MAP6195</a> <!-- -->-<!-- --> <!-- -->Mathematical Foundations for Massive Data Modeling and Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca7fc3e6bc796ef573e8e0\" target=\"_blank\">MAP6197</a> <!-- -->-<!-- --> <!-- -->Mathematical Introduction to Deep Learning<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a74e6bc794bfe73e4ab\" target=\"_blank\">COP5711</a> <!-- -->-<!-- --> <!-- -->Parallel and Distributed Database Systems<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca6a71714b5fb749521ffe\" target=\"_blank\">COP6731</a> <!-- -->-<!-- --> <!-- -->Advanced Database Systems<!-- --> <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></ul></div></div></li></ul></li></ul></div></div></section><section><header data-test=\"grouping-3-header\"><div><h2 data-testid=\"grouping-label\"><span>Dissertation</span></h2></div><div><span>21</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\">Earn at least <span>21</span> credits from the following types of courses: <div>STA 7980 - Dissertation Research\n\nThe student must select a dissertation adviser by the end of the first year. In consultation with the dissertation adviser, the student should form a dissertation advisory committee. The dissertation adviser will be the chair of the student's dissertation advisory committee. In consultation with the dissertation advisor and with the approval of the chair of the department, each student must secure qualified members of their dissertation committee. This committee will consist of at least four faculty members chosen by the candidate, three of whom must be from the department and one from outside the department or UCF. Graduate faculty members must form the majority of any given committee. A dissertation committee must be formed prior to enrollment in dissertation hours.\n\nThe dissertation serves as the culmination of the coursework that comprises this degree. It must make a significant original theoretical, intellectual, practical, creative or research contribution to the student's area within the discipline. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor. The dissertation will be completed through a minimum of 15 hours of dissertation research credit.\n</div></div></li></ul></div></div></section><section><header data-test=\"grouping-4-header\"><div><h2 data-testid=\"grouping-label\"><span>Examinations</span></h2></div><div><span>0</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\"><div>\nAfter passing candidacy, students will enroll into dissertation hours (STA7980) with their dissertation advisor. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor.\n</div></div></li></ul></div><div><section><div><header><div><h2>Qualifying Examination</h2></div><div><span>0</span><span>Total Credits</span></div><div><button aria-label=\"Collapse\"><i></i></button></div></header><div><div><ul><li data-test=\"ruleView-A\"><div data-test=\"ruleView-A-result\"><div>The qualifying examination is a written examination that will be administered by the doctoral exam committee at the start of the fall term (end of the summer) and at the start of the spring term. The courses required to prepare for the examination are STA 6246, STA 6366, STA 6367, STA 6326, STA 6327 and STA 6236. Students must obtain permission from the Graduate Program Coordinator to take the examination. Students normally take this exam just before the start of their second year and are expected to have completed the exam by the end of their second year. To be eligible to take the Ph.D. qualifying examination, the student must have a minimum grade point average of 3.0 (out of 4.0) in all the coursework for the Ph.D. The exam may be taken twice. If a student does not pass the qualifying exam after the second try, he/she will be dismissed from the program.\nIt is strongly recommended that the student select a dissertation adviser by the completion of 18 credit hours of course work, and it is strongly recommended that the student works with the dissertation adviser to form a dissertation committee within two semesters of passing the Qualifying Examination.\nTo pass the exam, students need to pass all 4 parts. Students must take all 4 parts of the\nqualifying exam in their first attempt and must have completed all courses covered by the exam.</div></div></li></ul></div></div></div></section><section><div><header><div><h2>Candidacy Examination</h2></div><div><span>0</span><span>Total Credits</span></div><div><button aria-label=\"Collapse\"><i></i></button></div></header><div><div><ul><li data-test=\"ruleView-A\"><div data-test=\"ruleView-A-result\"><div>\nThe candidacy exam is administered by the student's dissertation advisory committee and will be tailored to the student's individual program to propose either a research‐ or project‐based dissertation. The candidacy exam involves a dissertation proposal presented in an open forum, followed by an oral defense conducted by the student's advisory committee. This committee will give a Pass/No Pass grade. In addition to the dissertation proposal, the advisory committee may incorporate other requirements for the exam. The student can attempt candidacy any time after passing the qualifying examination, after the student has begun dissertation research (STA7919, if necessary), but prior to the end of the second year following the qualifying examination. The candidacy examination can be taken no more than two times. If a student does not pass the candidacy exam after the second try, he/she will be removed from the program\n\nAfter passing the candidacy examination and meeting other requirements, the student can register for Doctoral Dissertation (STA7980). A minimum of 21 Doctoral Dissertation credit hours are required. The Candidacy Examination can be attempted after passing the qualifying examination. The Candidacy Examination must be completed within one years after passing the qualifying examination. A student must successfully pass the Candidacy Examination within at most two attempts.</div></div></li></ul></div></div></div></section></div></div></section><section><header data-test=\"grouping-5-header\"><div><h2 data-testid=\"grouping-label\"><span>Admission to Candidacy</span></h2></div><div><span>0</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\"><div>\nThe following are required to be admitted to candidacy and enroll in dissertation hours.\n\nCompletion of all coursework, except for dissertation hours\nSuccessful completion of the qualifying examination\nSuccessful completion of the candidacy examination including a written proposal and oral defense\nThe dissertation advisory committee is formed, consisting of approved graduate faculty and graduate faculty scholars\nSubmittal of an approved program of study\n</div></div></li></ul></div></div></section><section><header data-test=\"grouping-6-header\"><div><h2 data-testid=\"grouping-label\"><span>Masters Along the Way</span></h2></div><div><span>0</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\"><div>PhD Students can obtain their Master's degree in Statistics & Data Science - Data Science Track along the way to their PhD degree. To satisfy the requirements for the MS degree, the student must complete the following requirements: 1 - Complete the 24 hours of required courses for the MS degree - Data Science track. 2.- Complete 12 credit hours from the elective list for the MS degree - Data Science track, except STA 5205, STA 5505 and STA 5738.\n\nThe student has the option of choosing between thesis option or non-thesis option.</div></div></li></ul></div></div></section><section><header data-test=\"grouping-7-header\"><div><h2 data-testid=\"grouping-label\"><span>Independent Learning</span></h2></div><div><span>0</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\"><div>\nAs will all graduate programs, independent learning is an important component of the Big Data Analytics doctoral program. Students will demonstrate independent learning through research seminars and projects and the dissertation.\n</div></div></li></ul></div></div></section><h3>Grand Total Credits:<!-- --> <strong>72</strong></h3></div><h1>Application Requirements</h1><h1>Application Deadlines</h1><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://graduate.ucf.edu/funding/\" 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 <a href=\"https://www.ucf.edu/catalog/graduate/#/content/609c0ff42bc0ac001c6f46ea\">Financial Information</a> 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://graduate.ucf.edu/funding/\" target=\"_blank\">Website</a></p>",
"primary": false,
"program": 905
}