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.

This Capstone has giving me a good understanding of statistic's, and the role this discipline plays in the real world. A special thanks to Mine Çetinkaya-Rundel I feel that she does an amazing job communicating the concepts.

A very useful and practical course. The learner had an opportunity to practice the skills he/she learned in the previous course and use it in a real-life situation.

I will recommend to give the information about the projects even if the proect starts in September 10th so we could advance while enrollment opens up.

V

Sometimes capstone courses are focused just on a final project. In this case, the capstone also includes quizzes which force you to review material that will be helpful in doing the final project. Overall, I would say that I felt a little behind compared to my peers in this specialization, since I started from zero; but on the other hand, I was still able to successfully complete it, so with a lot of hard work it is possible.

Loved this course

Strong Capstone Project

Excellent course

This project will reward you for the time you invest. It's a good simulation of a real-world exercise in using concepts from the specialization. The reason I took away one star is because the grading rubrics at every step are relatively superficial, sometimes absurdly superficial.

The capstone project is an excellent way to utilise the skills and knowledge learnt from the previous 4 courses in the specialisation. I learnt a lot about conducting a data science project from start to finish and it allows for ample time to experiment with different approaches to accomplish the project goals. This capstone project, and the 5-course specialisation, is highly recommended.

Great lead up to a final capstone project, in which you test a model for overfitting and validate on out-of-sample data.

Very good !

This was a highly challenging sequence of four courses and capstone, but worth the considerable investment in time and effort. Overall, with just a few glitches here and there, the specialization was superbly taught and well-designed. I thank Mine and the rest of the Stats team at Duke for offering it. I would also like to see Bayesian Stats made perhaps into two courses. Or even a follow-up Bayesian course perhaps not required for the specialization. Professor M. Clyde is one of the world's leading experts on Bayes, and the creator of the BAS package. All respect! I would like to have more of her teaching, but at a slightly less steeper learning curve. To my fellow students and the Duke team, thanks again.

Excellent

Great! Enjoyed it!

Great course, learned a lot and got me started on another project that I've turned into a really nice portfolio item. I feel much more comfortable with R and statistics principles.

Great

Very interesting project, a great real-world application example. Highly recommended to anyone that finishes the previous classes.

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.

We spent several months waiting for this capstone to appear. The instructors dissapeared and nobody knew if this one was going finally to happen or what.

The Capstone project really helped tie the program together

I think this is a very advisable course as a whole, The capstone offers a good occasion to put into practice what has been learned during the four previous courses and also works as a sort of review.

Great activity!!