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.
This course is part of the Statistics with R Specialization
Statistics with R Capstone
Offered By
About this Course
4.7
100 ratings
Skills you will gain
Model SelectionBayesian StatisticsStatistical AnalysisR Programming
Syllabus - What you will learn from this course
Week
1About 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 (Total 6 min), 4 readings
4 readings
Introduction to the Capstone Course10m
Tips for Success and Suggested Work Pace10m
What to Do This Week5m
Learning Objectives for Courses 1-410m
Week
2Exploratory 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.
2 readings, 1 quiz
2 readings
What to Do This Week10m
EDA Quiz - Assignment Guide10m
1 practice exercise
EDA Quiz28m
Week
3EDA 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
1 reading
What to Do This Week5m
Week
4EDA 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!
1 reading, 1 quiz
1 reading
What to Do This Week10m
4.7
25 Reviews25%
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got a tangible career benefit from this course
Top Reviews
By JN•Mar 24th 2017
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.
By AC•Jul 13th 2017
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.
Instructors
Merlise A Clyde
ProfessorDepartment of Statistical Science
Colin Rundel
Assistant Professor of the PracticeStatistical Science
David Banks
Professor of the PracticeStatistical Science
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
About Duke University
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
About the Statistics with R Specialization
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
More questions? Visit the Learner Help Center.
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