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"description": "<p>The Big Data Analytics MS, Master Along the Way Track is a nonterminal Master’s degree program available only to students in the Big Data Analytics PhD program. The Master of Science in Statistics and Data Science provides a sound foundation in statistical theory, statistical methods, numerical methods in statistics, and the application of computer methodology to statistical analyses.</p><p>The Big Data Analytics MS, Master Along the Way Track requires a minimum of 30 credit hours beyond the bachelor's degree. It is typically completed within the first 2-3 years of the doctoral program. The degree includes 21 credit hours minimum of core required courses, 6 credit hours of approved electives, and 3 credit hours of independent learning/research project. There is no thesis option for the Big Data Analytics MS, Master Along the Way Track. At least half of total credit hours must be at the 6000 level or above.</p><p>Students must maintain a minimum GPA of 3.0, as well as a \"B\" (3.0) in all courses completed toward the degree and since admission to the program.</p>",
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"description": "<p>The Big Data Analytics MS, Master Along the Way Track is a nonterminal Master’s degree program available only to students in the Big Data Analytics PhD program. The Master of Science in Statistics and Data Science provides a sound foundation in statistical theory, statistical methods, numerical methods in statistics, and the application of computer methodology to statistical analyses.</p><p>The Big Data Analytics MS, Master Along the Way Track requires a minimum of 30 credit hours beyond the bachelor's degree. It is typically completed within the first 2-3 years of the doctoral program. The degree includes 21 credit hours minimum of core required courses, 6 credit hours of approved electives, and 3 credit hours of independent learning/research project. There is no thesis option for the Big Data Analytics MS, Master Along the Way Track. At least half of total credit hours must be at the 6000 level or above.</p><p>Students must maintain a minimum GPA of 3.0, as well as a \"B\" (3.0) in all courses completed toward the degree and since admission to the program.</p><h2>Track Prerequisites</h2><p>This degree is only available to students enrolled in the Big Data Analytics PhD program. Program prerequisites are the same as those of the Big Data Analytics PhD program.</p><h2>Degree Requirements</h2><h3>Required Courses</h3> 21 Total Credits <ul><li>Complete the following: <ul><li>STA6106 - Statistical Computing I (3)</li><li>STA6107 - Statistical Computing II (3)</li><li>STA6246 - Linear Models (3)</li><li>STA6326 - Theoretical Statistics I (3)</li><li>STA6327 - Theoretical Statistics II (3)</li><li>STA6346 - Advanced Statistical Inference I (3)</li><li>STA6366 - Statistical Methodology for Data Science I (3)</li></ul></li></ul><h3>Restricted Electives (at least 6 credit hours must be STA coursework)</h3> 6 Total Credits <ul><li>Earn at least 6 credits from the following: <ul><li>STA5104 - Advanced Computer Processing of Statistical Data (3)</li><li>STA5125 - Statistical Methods in Forensic Evidence Interpretation (3)</li><li>STA5176 - Introduction to Biostatistics (3)</li><li>STA5205 - Experimental Design (3)</li><li>STA5703 - Data Mining Methodology I (3)</li><li>STA5825 - Stochastic Processes and Applied Probability Theory (3)</li><li>STA6223 - Conventional Survey Methods (3)</li><li>STA6224 - Bayesian Survey Methods (3)</li><li>STA6226 - Sampling Theory and Applications (3)</li><li>STA6238 - Logistic Regression (3)</li><li>STA6329 - Statistical Applications of Matrix Algebra (3)</li><li>STA6346 - Advanced Statistical Inference I (3)</li><li>STA6347 - Advanced Statistical Inference II (3)</li><li>STA6507 - Nonparametric Statistics (3)</li><li>STA6662 - Statistical Methods for Industrial Practice (3)</li><li>STA6704 - Data Mining Methodology II (3)</li><li>STA6705 - Data Mining Methodology III (3)</li><li>STA6707 - Multivariate Statistical Methods (3)</li><li>STA6709 - Spatial Statistics (3)</li><li>STA6714 - Data Preparation (3)</li><li>STA6857 - Applied Time Series Analysis (3)</li><li>STA7239 - Dimension Reduction in Regression (3)</li><li>STA7348 - Bayesian Modeling and Computation (3)</li><li>STA7719 - Survival Analysis (3)</li><li>STA7722 - Statistical Learning Theory (3)</li><li>MAP6195 - Mathematical Foundations for Massive Data Modeling and Analysis (3)</li><li>MAP6197 - Mathematical Introduction to Deep Learning (3)</li><li>CAP5610 - Machine Learning (3)</li><li>CNT5805 - Network Science (3)</li><li>COP5711 - Parallel and Distributed Database Systems (3)</li><li>STA6367 - Statistical Methodology for Data Science II (3)</li><li>STA6236 - Regression Analysis (3)</li></ul></li></ul><h3>Independent Learning</h3> 3 Total Credits <ul><li>Earn at least 3 credits from the following: <ul><li>STA6908 - Directed Independent Studies (1 - 99)</li></ul></li></ul><h4>Grand Total Credits: <strong>30</strong></h4><h2>Financial Information</h2><p>Graduate students may receive financial assistance through loans or other financial aid. Nonthesis students are not considered for departmental graduate assistantships or tuition assistance. For more information, see the College of Graduate Studies Funding website, 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>Nonthesis students are not considered for departmental graduate assistantships or tuition assistance.</p><h2>Fellowship Information</h2><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 UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.</p>",
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"description": "<p style=\"line-height: 108%; margin-bottom: 0.11in;\">The Big Data Analytics MS, Master Along the Way Track is a nonterminal Master’s degree program available only to students in the Big Data Analytics PhD program. The Master of Science in Statistics and Data Science provides a sound foundation in statistical theory, statistical methods, numerical methods in statistics, and the application of computer methodology to statistical analyses.</p> <p>The Big Data Analytics MS, Master Along the Way Track requires a minimum of 30 credit hours beyond the bachelor's degree. It is typically completed within the first 2-3 years of the doctoral program. The degree includes 21 credit hours minimum of core required courses, 6 credit hours of approved electives, and 3 credit hours of independent learning/research project. There is no thesis option for the Big Data Analytics MS, Master Along the Way Track. At least half of total credit hours must be at the 6000 level or above.</p> <br /> <p>Students must maintain a minimum GPA of 3.0, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.</p> <p><br /><br /></p>",
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"description": "<h1>Track Prerequisites</h1><p style=\"line-height: 108%; margin-bottom: 0.11in;\">This degree is only available to students enrolled in the Big Data Analytics PhD program. Program prerequisites are the same as those of the Big Data Analytics PhD program.</p> <p><br /><br /></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>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\">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/64ef83cd0bd2ff5b1be968be\" target=\"_blank\">STA6107</a> <!-- -->-<!-- --> <!-- -->Statistical Computing 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/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></ul></div></div></li></ul></div></div></section><section><header data-test=\"grouping-1-header\"><div><h2 data-testid=\"grouping-label\"><span>Restricted Electives (at least 6 credit hours must be STA coursework)</span></h2></div><div><span>6</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>6</span> credits from the following: <div><ul style=\"margin-top:5px;margin-bottom:5px\"><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/64cbf2b57aa6821ec8d14ddb\" target=\"_blank\">STA5125</a> <!-- -->-<!-- --> <!-- -->Statistical Methods in Forensic Evidence Interpretation<!-- --> <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/60ca8154a8d2fb7d132d8586\" target=\"_blank\">STA5703</a> <!-- -->-<!-- --> <!-- -->Data Mining Methodology I<!-- --> <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/60ca8154e6bc7991da73ec3d\" target=\"_blank\">STA6226</a> <!-- -->-<!-- --> <!-- -->Sampling Theory and Applications<!-- --> <span style=\"margin-left:5px\">(3)</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><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/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/60ca8158a38edf5c413ecb2a\" target=\"_blank\">STA6704</a> <!-- -->-<!-- --> <!-- -->Data Mining Methodology II<!-- --> <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/60ca8158a8d2fb2f1e2d858a\" target=\"_blank\">STA6714</a> <!-- -->-<!-- --> <!-- -->Data Preparation<!-- --> <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/60ca81589d7535808687739c\" target=\"_blank\">STA7348</a> <!-- -->-<!-- --> <!-- -->Bayesian Modeling and Computation<!-- --> <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/60ca815ba38edfc77c3ecb2f\" target=\"_blank\">STA7722</a> <!-- -->-<!-- --> <!-- -->Statistical Learning Theory<!-- --> <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/60ca6a2b5a1583b5779e6c28\" target=\"_blank\">CAP5610</a> <!-- -->-<!-- --> <!-- -->Machine 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><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/60ca815402fd3a04716d898d\" target=\"_blank\">STA6236</a> <!-- -->-<!-- --> <!-- -->Regression Analysis<!-- --> <span style=\"margin-left:5px\">(3)</span></span></li></ul></div></div></li></ul></div><div></div></div></section><section><header data-test=\"grouping-2-header\"><div><h2 data-testid=\"grouping-label\"><span>Independent Learning</span></h2></div><div><span>3</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>3</span> credits from the following: <div><ul style=\"margin-top:5px;margin-bottom:5px\"><li><span><a href=\"#/courses/view/6418678edcd1aa1ffe0829c7\" target=\"_blank\">STA6908</a> <!-- -->-<!-- --> <!-- -->Directed Independent Studies<!-- --> <span style=\"margin-left:5px\">(1 - 99)</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 loans or other financial aid. Nonthesis students are not considered for departmental graduate assistantships or tuition assistance. 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>Nonthesis students are not considered for departmental graduate assistantships or tuition assistance.</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><div style=\"overflow-wrap: break-word;\"> <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>",
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