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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....
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Dates limites flexibles

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Clock

Approx. 9 hours to complete

Recommandé : 5-10 hours/week...
Comment Dots

English

Sous-titres : English...

Compétences que vous acquerrez

Model SelectionBayesian StatisticsStatistical AnalysisR Programming
Stacks
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Clock

Approx. 9 hours to complete

Recommandé : 5-10 hours/week...
Comment Dots

English

Sous-titres : English...

Programme du cours : ce que vous apprendrez dans ce cours

Week
1
Clock
1 heure pour terminer

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....
Reading
1 vidéo (Total 6 min), 4 lectures
Reading4 lectures
Introduction to the Capstone Course10 min
Tips for Success and Suggested Work Pace10 min
What to Do This Week5 min
Learning Objectives for Courses 1-410 min
Week
2
Clock
1 heure pour terminer

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. ...
Reading
2 lectures, 1 quiz
Reading2 lectures
What to Do This Week10 min
EDA Quiz - Assignment Guide10 min
Quiz1 exercice pour s'entraîner
EDA Quiz28 min
Week
3
Clock
5 minutes pour terminer

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. ...
Reading
1 lecture
Reading1 lecture
What to Do This Week5 min
Week
4
Clock
2 heures pour terminer

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!...
Reading
1 lecture, 1 quiz
Reading1 lecture
What to Do This Week10 min
4.7
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Meilleurs avis

par JNMar 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.

par ACJul 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.

Enseignants

Merlise A Clyde

Professor
Department of Statistical Science

Colin Rundel

Assistant Professor of the Practice
Statistical Science

David Banks

Professor of the Practice
Statistical Science

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

À propos de 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....

À propos de la Spécialisation Statistics with R

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....
Statistics with R

Foire Aux Questions

  • 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.

  • 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.

  • 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.

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