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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/statistics-project
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课程名称：使用R进行统计分析顶点项目 课程概述：此顶点项目将使用R进行分析，旨在回答课程团队提供的特定科学或商业问题。学习者将获得一个大型且复杂的数据集，分析过程需运用前面课程中介绍的多种方法和技术，包括通过数据可视化和数值摘要进行的探索性数据分析、统计推断和建模，以及在数据和研究问题背景下对结果的解读。分析将实施频率派和贝叶斯两种技术，并讨论在数据背景下这两种方法的相似性和差异，以及这些差异对从数据中得出的结论的意义。 课程大纲： 1. 项目介绍：本周内容是对项目任务和目标的介绍，包括将要分析的数据集和使用的数据分析技术的具体问题。建议先了解课程全貌，再深入具体分析。 2. 探索性数据分析（EDA）：学习者将对房屋数据进行探索性分析，这是理解数据的基础步骤。 3. EDA和基本模型选择 - 提交：深入进行探索性数据分析，准备初步分析报告，该报告将在下一周进行同行评审。 4. EDA和基本模型选择 - 评估：在评估同伴作品中学习。 5. 模型选择与诊断：继续模型选择和诊断，为最终项目奠定基础。 6. 样本外预测：使用模型进行样本外预测和验证，为最终分析积累经验。 7. 最终数据分析 - 提交：完成最终数据分析项目的作业提交。 8. 最终数据分析 - 评估：完成项目，评估三位同伴的作业。 注意：只有通过该专业的四门之前课程的学习者才有资格参加顶点项目。
Part: 1
Title:About the Capstone Project
Description: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.
Part: 2
Title:Exploratory Data Analysis (EDA)
Description: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.
Part: 3
Title:EDA and Basic Model Selection - Submission
Description: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.
Part: 4
Title:EDA and Basic Model Selection - Evaluation
Description:Great work so far! We hope you will also learn as much from evaluating your peers' work as completing your own assignment. Happy learning!
Part: 5
Title:Model Selection and Diagnostics
Description: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.
Part: 6
Title:Out of Sample Prediction
Description: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.
Part: 7
Title:Final Data Analysis - Submission
Description: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.
Part: 8
Title:Final Data Analysis - Evaluation
Description: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.
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.
