Program Description Detail

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    "id": 294126,
    "description_type": {
        "id": 3,
        "name": "Full Catalog Description"
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    "description": "<p>Big Data Analytics, Statistics track, will train researchers with a strong statistics background to analyze massive, structured or unstructured data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.</p><p>The track will provide a strong foundation in statistical theory and the major methodologies associated with Big Data Analytics such as predictive analytics, data mining, text analytics and statistical analysis with an interdisciplinary component that combines the strength of statistics and computer science. It will focus on statistical theory in addition to statistical computing, statistical data mining and their application to business, social, and health problems complemented with ongoing industrial collaborations.</p><p>The Ph.D. in Big Data Analytics, Statistics track, requires 72 hours beyond an earned Bachelor's degree. Required coursework includes 30 credit hours of required courses, 21 credit hours of restricted elective coursework, and 21 credit hours of dissertation research.</p><p>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.</p><p>Statistical Colloquium Requirement - The department has a course, STA 7920 (Statistical Colloquium). This is a 0-credit course and should not impact your GPA. However, you will need at least 5 semesters of STA 7920 before you can graduate. With this course, you must attend the departmental colloquial.</p><p><strong>Total Credit Hours Required: 72 Credit Hours Minimum beyond the Bachelor's Degree</strong></p><h2>Track Prerequisites</h2><p>Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, MAC 2313: Calculus with Analytic Geometry III, MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra These pre-required courses are basic undergraduate courses from the Math department.</p><h2>Application Requirements</h2><h2>Financial Information</h2><p>Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. 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><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> 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. ",
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    "program": 1957
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