- Ph.D. Program Statistics
- Statistics | Duke Graduate School
- Cognitively Based Music Informatics Research
Graduation Review graduation requirements, as well as ceremony information. Leaves and Withdrawals Find procedural information if you wish to change your status, either as a leave of absence, a withdrawal, or a readmission. Academic Student Services Get assistance with academic policies and procedures. Dissertation Publication Learn more about the policies surrounding your dissertation publication. Funding Resources Find financial support options, including external and internal fellowships. Family Resources Access resources for graduate student parents.
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Dealing with Conflict Resolve conflicts that may arise between graduate students and faculty. International Student Services Take advantage of English language development resources and more. Legal Services Access an attorney for a variety of legal issues. Housing Explore on-campus and off-campus housing options. International Travel Learn more about what you need to do before traveling abroad. All tuition expenses are paid and there is a fixed monthly stipend determined to be sufficient to pay living expenses.
Financial support can be continued for five years, department resources permitting, for students in good standing. The resources for student financial support derive from funds made available for student teaching and research assistantships. Students receive both a teaching and research assignment each quarter which, together, do not exceed 20 hours.
Students are encouraged to apply for outside scholarships, fellowships, and other forms of financial support. Students must complete a total of 30 units for the Ph. The remaining 10 units can be from Statistics courses numbered and above, and may be taken for a letter grade or credit. Students may not include more than one unit of Stats , Consulting Workshop, towards the 30 units. The selection of courses must be approved by the Statistics Department and the Application for the Ph. Minor form must be approved by both the student's Ph. For further information about the Statistics Ph.
The Department of Statistics is committed to providing academic advising in support of graduate student scholarly and professional development. When most effective, this advising relationship entails collaborative and sustained engagement by both the adviser and the advisee.
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As a best practice, advising expectations should be periodically discussed and reviewed to ensure mutual understanding. Both the adviser and the advisee are expected to maintain professionalism and integrity. Faculty advisers guide students in key areas such as selecting courses, designing and conducting research, developing of teaching pedagogy, navigating policies and degree requirements, and exploring academic opportunities and professional pathways. Graduate students are active contributors to the advising relationship, proactively seeking academic and professional guidance and taking responsibility for informing themselves of policies and degree requirements for their graduate program.
Ph.D. Program Statistics
For a statement of University policy on graduate advising, see the " Graduate Advising " section of this bulletin. The adviser serves as a key resource for the purposes of course placement and approval of elective coursework as it relates to fulfilling degree requirements. Those planning to apply to doctoral programs are also able to receive feedback on research opportunities.
First and second year students are advised on course selection and other academic matters by the Director of Graduate Studies who is available by appointment to consult with students about any graduate student related matter, including degree progress. The DGS also leads cohort-specific workshops addressing topics such as qualifying exams, adviser selection, oral exams and post-graduation placement.
By the final study list deadline of Spring Quarter of the second year students are expected to have selected a research adviser who later serves as their principal dissertation adviser. The dissertation adviser must be a member of the Academic Council, and may be from outside the department. Students may also opt to have two co-advisers rather than one principal adviser, which may include one from outside the department. The adviser-student mentorship takes many different forms, including, but not limited to programmatic consultation and degree progress, and support and collaboration relating to research, conferences, publications, and academic and professional opportunities.
It is the responsibility of the student to meet with their adviser at least once per quarter during the academic year to discuss academic standing and graduate degree progress. In addition, the Director of Graduate Studies is always available to Ph. Handbook, available on the department website. Donoho, Bradley Efron, Trevor J. Hastie, Susan P. Holmes, Iain M. Johnstone, Tze L. Romano, Chiara Sabatti, David O. Tibshirani, Guenther Walther, Wing H.
Introduction to R for Undergraduates. This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven.
Statistics | Duke Graduate School
No prior programming experience is needed. Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class. Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome.
Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates.
How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures.
During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media newspapers, blogs, etc. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students. Introduction to Statistical Methods: Precalculus.
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages. This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal in-game strategies and ensure fairness between participants.
Topics will include examples drawn from multiple sports such as basketball, baseball, soccer, football and tennis.
Cognitively Based Music Informatics Research
The course is intended to focus on data-based applications, and will involve computations in R with real data sets via tutorial sessions and homework assignments. Prereqs: No statistical or programming background is assumed, but introductory courses, e. A prior knowledge of Linear Algebra e. Students will engage with the fundamental ideas in inferential and computational thinking.
Each week, we will explore a core topic comprising three lectures and two labs a module , in which students will manipulate real-world data and learn about statistical and computational tools. Students will engage in statistical computing and visualization with current data analytic software Jupyter, R. The objectives of this course are to have students 1 be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, and 2 be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures.
No programming or statistical background is assumed. Freshmen and sophomores interested in data science, computing and statistics are encouraged to attend.