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"description": "<h1>Track Prerequisites</h1><p>Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses <a href=\"https://sciences.ucf.edu/math/course/mac-2313-calculus-with-analytic-geometry-iii/\">are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, 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> <a href=\"https://sciences.ucf.edu/math/course/mac-2313-calculus-with-analytic-geometry-iii/\">or</a> <a href=\"https://sciences.ucf.edu/math/course/mas-3106-linear-algebra/\">MAS 3106: Linear Algebra</a> These pre-required courses are basic undergraduate courses from the Math department.</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><span>Complete <!-- -->all<!-- --> of the following</span><ul><li data-test=\"ruleView-A\"><div data-test=\"ruleView-A-result\">Take 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/64ef83cd0bd2ff5b1be968be\" target=\"_blank\">STA6107</a> <!-- -->-<!-- --> <!-- -->Statistical Computing II<!-- --> <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/631f432833d7637a3cd3fe59\" target=\"_blank\">STA6246</a> <!-- -->-<!-- --> <!-- -->Linear Models<!-- --> <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/60ca8158e6bc794b6c73ec4c\" target=\"_blank\">STA6346</a> <!-- -->-<!-- --> <!-- -->Advanced Statistical Inference I<!-- --> <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/60ca815b5a15830bda9e74ff\" target=\"_blank\">STA7920</a> <!-- -->-<!-- --> <!-- -->Statistical Colloquium<!-- --> <span style=\"margin-left:5px\"></span></span></li><li><span><a href=\"#/courses/view/60ca8154a8d2fb8bba2d8588\" target=\"_blank\">STA6238</a> <!-- -->-<!-- --> <!-- -->Logistic Regression<!-- --> <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-1-header\"><div><h2 data-testid=\"grouping-label\"><span>Elective Courses</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\">Take at least <span>7</span> of the following: <div><ul style=\"margin-top:5px;margin-bottom:5px\"><li><span><a href=\"#/courses/view/60ca81589d7535808687739c\" target=\"_blank\">STA7348</a> <!-- -->-<!-- --> <!-- -->Bayesian Modeling and Computation<!-- --> <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/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/60ca7fc4e6bc79282773e8e3\" target=\"_blank\">MAP6465</a> <!-- -->-<!-- --> <!-- -->Wavelets and Their Applications<!-- --> <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/60ca8158a38edf5c413ecb2a\" target=\"_blank\">STA6704</a> <!-- -->-<!-- --> <!-- -->Data Mining Methodology 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/60ca8158e6bc79cbb773ec49\" target=\"_blank\">STA7239</a> <!-- -->-<!-- --> <!-- -->Dimension Reduction in Regression<!-- --> <span style=\"margin-left:5px\">(3)</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><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/6169a86003f6422fdb4a489e\" target=\"_blank\">STA6709</a> <!-- -->-<!-- --> <!-- -->Spatial Statistics<!-- --> <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/60ca7fb75a1583808e9e7015\" target=\"_blank\">MAA6238</a> <!-- -->-<!-- --> <!-- -->Measure and Probability I<!-- --> <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/60ca81589d7535045f87739d\" target=\"_blank\">STA6347</a> <!-- -->-<!-- --> <!-- -->Advanced Statistical Inference II<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca7fc95ada374d7aec9f3b\" target=\"_blank\">MAS5145</a> <!-- -->-<!-- --> <!-- -->Advanced Linear Algebra and Matrix Theory<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca7fc4e6bc794ed273e8e2\" target=\"_blank\">MAP6207</a> <!-- -->-<!-- --> <!-- -->Optimization Theory<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li><li><span><a href=\"#/courses/view/60ca7fb89d75359af28770be\" target=\"_blank\">MAA7239</a> <!-- -->-<!-- --> <!-- -->Asymptotic Methods in Mathematical Statistics<!-- --> <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/60ca8154a8d2fb7e6f2d8583\" target=\"_blank\">STA5205</a> <!-- -->-<!-- --> <!-- -->Experimental Design<!-- --> <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/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/60ca815b5a15837e129e74fd\" target=\"_blank\">STA6329</a> <!-- -->-<!-- --> <!-- -->Statistical Applications of Matrix Algebra<!-- --> <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/60ca815860402bed76ae78d7\" target=\"_blank\">STA6707</a> <!-- -->-<!-- --> <!-- -->Multivariate Statistical Methods<!-- --> <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/60ca815ba38edf22893ecb30\" target=\"_blank\">STA7719</a> <!-- -->-<!-- --> <!-- -->Survival Analysis<!-- --> <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/60ca6a636b6b62568940006e\" target=\"_blank\">CNT5805</a> <!-- -->-<!-- --> <!-- -->Network Science<!-- --> <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></ul></div></div></li><li data-test=\"ruleView-B\"><div data-test=\"ruleView-B-result\"><div>Other 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></ul></li></ul></div></div></section><section><header data-test=\"grouping-2-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 take the candidacy exam and 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 21 hours of dissertation research credit.</div></div></li></ul></div></div></section><section><header data-test=\"grouping-3-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>After 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. </div></div></li></ul></div></div></section><section><header data-test=\"grouping-4-header\"><div><h2 data-testid=\"grouping-label\"><span>Qualifying Examination</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>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 6326, STA 6327, STA 6236, STA 6246, STA 6366, and STA 6346 . 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 start of their third 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.\nTo pass the exam, students need to pass all 3 parts. Students must take all 3 parts of the qualifying exam in their first attempt and must have completed all courses covered by the exam.\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.</div></div></li></ul></div></div></section><section><header data-test=\"grouping-5-header\"><div><h2 data-testid=\"grouping-label\"><span>Candidacy Examination</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>The 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></section><section><header data-test=\"grouping-6-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>The following are required to be admitted to candidacy and enroll in dissertation hours. \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 Submittal of an approved program of study\n</div></div></li></ul></div></div></section><section><header data-test=\"grouping-7-header\"><div><h2 data-testid=\"grouping-label\"><span>Dissertation Defense</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>Upon completion of a student's research, the student's committee schedules an oral defense of the dissertation. Most students complete the program within five years after obtaining their bachelor's degree. Students are expected to complete the dissertation in no more than seven years from the date of admission to the program.\n\nThe dissertation defense examination can be taken no more than two times. If a student does not pass the dissertation defense exam after the second try, he/she will be removed from the program.\n</div></div></li></ul></div></div></section><section><header data-test=\"grouping-8-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 - Statistics Track along the way to their PhD degree in big Data Analytics – Statistics track. To satisfy the requirements for the MS degree, the student must complete the following requirements: \n1. Complete the 21 hours of required courses for the MS degree.\n2. Complete 15 credit hours from the elective list for the MS degree except STA 5505 and STA 5738.\n The 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-9-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>As with all graduate programs, independent learning is an important component of the Big Data Analytics – Statistics track doctoral program. Students will demonstrate independent learning through research seminars and projects and the dissertation.</div></div></li></ul></div></div></section><h3>Grand Total Credits:<!-- --> <strong>72</strong></h3></div><h1>Application Requirements</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><div> <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>",
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}