Graduate certificate in statistics
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The Interdisciplinary Graduate Certificate in Applied Statistics is a program aimed specifically at Western Michigan University graduate students who are not majoring in statistics. Graduates will be quantitatively aware and capable of conducting quantitative research for their PhD dissertations and master’s theses. They’ll be able to properly design experiments or surveys, collect data, and analyze the results using statistical software packages like SPSS or SAS.
The program is open to WMU graduate students as well as graduates of bachelor’s programs who find pursuing a master’s degree too difficult or time consuming. Students learn skills that can be applied in a variety of fields, including business, education, health, science, government, and technology.
One accepted quantitative study course from a graduate student’s own department can be replaced for a course on this list for WMU graduate students. The course would count toward their graduate degree if it was necessary.
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Exploratory data analysis, estimate and hypothesis testing, factor and regression analysis, discriminant analysis, and forecasting are all quantitative skills to learn. Get hands-on experience with statistical software programs like SPSS and SAS.
IELTS (Academic Module): Overall 6.5 with no individual band below 6.0; or EAP 5 Advanced level with overall 70 percent and all skills 65 percent or above; or TOEFL iBT: minimum score 79 (Reading no less than 18, Writing no less than 20); or equivalent measures available at http://www.swinburne.edu.au/study/international/apply/entry-requirements/.
RPL is a process by which a student may be given credit or partial credit toward a qualification in exchange for skills and knowledge acquired through work experience, life experience, and/or formal training. Visit the RPL website for more information for students considering higher education degrees: http://www.future.swinburne.edu.au/pathways/workforce/index.html
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The Graduate Certificate in Applied Statistics (GCAS) provides a fundamental background in the design and analysis of quantitative data to students enrolled in programs other than the Dr. Bing Zhang Department of Statistics. This credential enables students to show quantitative knowledge to potential employers and to conduct quantitative research more effectively in their dissertations. This webpage contains tuition information as well as refund information.
Students must be accepted to the graduate school in order to enroll in this program and work toward an Applied Statistics Certificate. Admission to the graduate certificate program does not require GRE scores.
You must use a different email address when applying than the one you used to apply to your current degree program.
You must upload your most recent UK transcript, which you will find on myUK.
If you are currently enrolled in a graduate program, you may request a fee waiver by emailing your Admissions Officer.
Please note that you will not be eligible for a fee waiver if you have already graduated.
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Anat Fuchs, a PhD student in the research lab of Mona Singh, professor of computer science and director of the Lewis Sigler Institute for Integrative Genomics, presents an example of her work. Fuchs is enrolled in the Center for Statistics and Machine Learning’s graduate certificate program.
Take three classes for credit and earn a B+ (3.3) or better in each of the three categories on the approved list: core machine learning, core statistics and probabilistic modeling, and electives. From each category, one course must be chosen. The elective course can be chosen from a core category with the approval of the certificate director, as long as it does not substantially overlap with the other course chosen from that category. At least one of the three courses must be taken outside of the student’s home department, with a maximum of one course below the 500 level.
The core curriculum is designed to provide foundational training in statistics and machine learning while also ensuring that certificate students have a broad understanding of statistics and machine learning. Below is a list of selected core courses in the two regions. A certificate student must also choose a third course from a list of elective courses that complement the core courses. These electives focus on promoting material (for example, optimization) or applications in a particular domain.