About this course: The capstone project will be an analysis using R that answers a specific scientific/business question provided by the course team. A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question. The analysis will implement both frequentist and Bayesian techniques and discuss in context of the data how these two approaches are similar and different, and what these differences mean for conclusions that can be drawn from the data. A sampling of the final projects will be featured on the Duke Statistical Science department website. Note: Only learners who have passed the four previous courses in the specialization are eligible to take the Capstone.
Taught by: Merlise A Clyde, Professor
Department of Statistical Science
Taught by: Colin Rundel , Assistant Professor of the Practice
Statistical Science
Taught by: David Banks, Professor of the Practice
Statistical Science
Taught by: Mine Çetinkaya-Rundel, Assistant Professor of the Practice
Department of Statistical Science
| Basic Info |
Course 5 of 5 in the
Statistics with R Specialization.
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| Commitment | 5-10 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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|---|---|---|
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| Earn a shareable Course Certificate | Not available | Available |
Very interesting project, a great real-world application example. Highly recommended to anyone that finishes the previous classes.
Overall, great course, learned quite a bit. I would say though, the discussion boards weren't helpful for resolving issues when working on a project, and there should be a section to help explain, what now? In other words, I finished the course, put it on my LinkedIn, but not sure which direction to go in.
Thank you very much for teaching me all the statistics courses in the specialization. Although it is an online study, I think I benefit a lot from the course contents, quizzes and problem sets and the training from final projects one by one. Also, I appreciate a lot the feedback from other students in the courses, which gives me more confidence on my study. As a matter of fact, I know I still have a lot of room to improve on my final project, such as consistency of EDA with the later part of my project, assessment on residual plots, real understanding on the trade off between model interpretation and prediction accuracy, etc., which I will improve in my future study and analysis. No matter where and what I will study in the future, I always bring the statistics knowledge and R skills that I learned from my first specialization from professors at Duke University. Thank you.
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EF
This section guides you through performing a complete analysis. Some of the grading criteria are inconsistent with the tasks stated in the project. However, in my experience giving the kind of report that you'd generate from R markdown to a non-technical, non-statistically minded stakeholder will result in bewilderment. While Rmd is a good format for documenting your approach and creating a reproducible data analysis, the report presented to stakeholders should take a much different form. In writing an Rmd, you are necessarily constrained by the need to write code in a particular order -- that order may not be the most beneficial one to present. Furthermore, having long summary output of various models and other such code is generally not helpful to non-technical stakeholders. I understand that not using Rmd would make grading more difficult, so I'm ok with Rmd being used here. But I think that it would be good to explain this all to students.