Statistics with R Capstone
link Source : www.coursera.org
list 8 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
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Les infos clés

credit_card Formation gratuite
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timer 80 heures de cours

En résumé

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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Le programme

  • 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...
  • 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...
  • 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 repor...
  • 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!
  • 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 hesit...
  • 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 ...
  • 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.
  • 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.
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Les intervenants

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

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Le concepteur

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.
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La plateforme

Coursera

Coursera est une entreprise numérique proposant des formations en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

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