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.
Created by:Â Â Duke University
Taught by:Â Â Merlise A Clyde, Professor
Department of Statistical Science
Taught by:Â Â Colin Rundel , Assistant Professor of the Practice
Statistical Science
Taught by:Â Â David Banks, Professor of the Practice
Statistical Science
Taught by:  Mine Çetinkaya-Rundel, Associate Professor of the Practice
Department of Statistical Science
| Basic Info | Course 5 of 5 in the Statistics with R Specialization |
| Commitment | 5-10 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
About the Capstone Project
Welcome to the capstone project! This week's content is an introduction to the project assignment and goals. The readings in this week will introduce the data set that you will be analyzing for your project and the specific questions you will answer using data analysis techniques we learned in the previous courses. It is important to understand what we will be doing in the course before jumping into the detailed analysis. So we encourage you to start with the first lecture to get the big picture, and then delve into the specifics of the analysis. Enjoy, and good luck! Remember, if you have questions, you can post them on the discussion forums.
1 video, 5 readings
- Reading: Introduction to the Capstone Course
- Reading: Tips for Success and Suggested Work Pace
- Reading: What to Do This Week
- Reading: Feedback surveys
- Reading: Learning Objectives for Courses 1-4
WEEK 2
Exploratory Data Analysis (EDA)
This week you will work on conducting an exploratory analysis of the housing data. Exploratory analysis is an essential first step for familiarizing yourself with and understanding the data.
In this week, you will complete a quiz which will guide you through certain important aspects of the data. The insights you gain through this assignment will help inform modeling in the future quizzes and peer assessments.
Feel free to post questions about this assignment on the discussion forum.
3 readings
- Reading: What to Do This Week
- Reading: EDA Quiz - Assignment Guide
- Reading: Feedback Survey
Graded: EDA Quiz
WEEK 3
EDA and Basic Model Selection - Submission
This week we will dig deeper into our exploratory data analysis of the data. We now have all the information and data necessary to perform a deep dive into the EDA and it is time start your initial analysis report! We encourage you to start your analysis report (presented in peer-review format next week) early so you will have enough time to complete it. You will conduct exploratory data analysis, model selection, and model evaluation, and then complete a written report which answers several questions which will guide you through the process. This report will be your first peer-review assignment in this course.
1 reading
- Reading: What to Do This Week
WEEK 4
EDA and Basic Model Selection - Evaluation
Great work so far! We hope you will also learn as much from evaluating your peers' work as completing your own assignment. Happy learning!
2 readings
- Reading: What to Do This Week
- Reading: Feedback Survey
Graded: EDA and Basic Model Selection
WEEK 5
Model Selection and Diagnostics
We are half way through the course! In this week, you will continue model selection and model diagnostics, which will serve a starting point for your final project. You will be assessed on your work through a quiz. If you have any questions so far, don't hesitate to post on the forum so that others can help and discuss the question together.
3 readings
- Reading: What to Do This Week
- Reading: Model Selection and Diagnostics Quiz - Assignment Guide
- Reading: Feedback Survey
Graded: Model Selection and Diagnostics Quiz
WEEK 6
Out of Sample Prediction
In this week, you will gain experience using your model to perform out-of-sample prediction and validation. The skills honed this week will guide you through your final analysis in the weeks to come. Please feel free to go back to prior weeks and review the necessary background knowledge.
3 readings
- Reading: What do Do This Week
- Reading: Out of Sample Prediction Quiz - Assignment Guide
- Reading: Feedback Survey
Graded: Out of Sample Prediction Quiz
WEEK 7
Final Data Analysis - Submission
In the next two weeks, you will complete your final data analysis project. You will submit your answers using the Final Data Analysis peer review assignment link in Week 8.
1 reading
- Reading: What to Do This Week
WEEK 8
Final Data Analysis - Evaluation
Congratulations on making through to the final week of the course! In this week, we will finish this data analysis project by completing the evaluation of three of your peers' assignments.
2 readings
- Reading: What to Do This Week
- Reading: Feedback Survey
Graded: Final Data Analysis
FAQs
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Coursework
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Pricing
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| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
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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.