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.
This course is part of the Statistics with R Specialization
Offered By
Statistics with R SpecializationDuke UniversityAbout this Course
Learner Career Outcomes
17%
33%
17%
Skills you will gain
Learner Career Outcomes
17%
33%
17%
Offered by
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.
Syllabus - What you will learn from this course
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.
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.
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.
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!
Reviews
TOP REVIEWS FROM STATISTICS WITH R CAPSTONE
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 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.
Good, challenging problem sets. Final project is interesting enough, albeit perhaps could have required a bit more in the final submission to make it really rigorous.
Very interesting project, a great real-world application example. Highly recommended to anyone that finishes the previous classes.
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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.
