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自分のスケジュールですぐに学習を始めてください。

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スケジュールに従って期限をリセットします。

約9時間で修了

推奨:5-10 hours/week...

英語

字幕:英語

習得するスキル

Model SelectionBayesian StatisticsStatistical AnalysisR Programming

次における5の5コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約9時間で修了

推奨:5-10 hours/week...

英語

字幕:英語

シラバス - 本コースの学習内容

1
1時間で修了

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件のビデオ (合計6分), 4 readings
4件の学習用教材
Introduction to the Capstone Course10 分
Tips for Success and Suggested Work Pace10 分
What to Do This Week5 分
Learning Objectives for Courses 1-410 分
2
1時間で修了

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. ...
2 readings, 1 quiz
2件の学習用教材
What to Do This Week10 分
EDA Quiz - Assignment Guide10 分
1の練習問題
EDA Quiz28 分
3
5分で修了

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
1件の学習用教材
What to Do This Week5 分
4
2時間で修了

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!...
1 reading, 1 quiz
1件の学習用教材
What to Do This Week10 分
5
1時間で修了

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....
2 readings, 1 quiz
2件の学習用教材
What to Do This Week10 分
Model Selection and Diagnostics Quiz - Assignment Guide10 分
1の練習問題
Model Selection and Diagnostics Quiz24 分
6
1時間で修了

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. ...
2 readings, 1 quiz
2件の学習用教材
What do Do This Week10 分
Out of Sample Prediction Quiz - Assignment Guide10 分
1の練習問題
Out of Sample Prediction Quiz14 分
7
5分で修了

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
1件の学習用教材
What to Do This Week5 分
8
2時間で修了

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. ...
1 reading, 1 quiz
1件の学習用教材
What to Do This Week10 分
4.7
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人気のレビュー

by 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.

by 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.

講師

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Merlise A Clyde

Professor
Department of Statistical Science
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Colin Rundel

Assistant Professor of the Practice
Statistical Science
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David Banks

Professor of the Practice
Statistical Science
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Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department of Statistical Science

デューク大学(Duke University)について

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....

Statistics with Rの専門講座について

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

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