Statistics with R 专项课程

于 3月 27 开始

Statistics with R 专项课程

Master Statistics with R

Statistical mastery of data analysis including inference, modeling, and Bayesian approaches.

本专项课程介绍

In this Specialization, you will learn to analyze and visualize data in R and created 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.

制作方:

courses
5 courses

按照建议的顺序或选择您自己的顺序。

projects
项目

旨在帮助您实践和应用所学到的技能。

certificates
证书

在您的简历和领英中展示您的新技能。

课程
Beginner Specialization.
No prior experience required.
  1. 第 1 门课程

    Introduction to Probability and Data

    即将开课的班次:3月 27 — 5月 8。
    课程学习时间
    5 weeks of study, 5-7 hours/week
    字幕
    English

    课程概述

    This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
  2. 第 2 门课程

    Inferential Statistics

    即将开课的班次:3月 27 — 5月 8。
    课程学习时间
    5 weeks of study, 5-7 hours/week
    字幕
    English

    课程概述

    This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
  3. 第 3 门课程

    线性回归和建模

    即将开课的班次:3月 27 — 5月 1。
    课程学习时间
    学习时间4周,每周5-7个小时
    字幕
    English

    课程概述

    这门课程介绍一元和多元线性回归模型。 这些模型能够让你获得数据集和一个连续变量之间的关系。(比如说:)在教授的外表吸引程度和学生的评分之间有什么关联么?我们可以根据孩子母亲的特定特征来预测这个孩子的测试分数么?在这门课程当中,你将会学习线性回归的基本理论,运用免费统计软件R、RStudio分析一些数据例子来学习如何拟合、检验,以及如何利用回归模型去检验多元变量之间的关系。
  4. 第 4 门课程

    Bayesian Statistics

    即将开课的班次:4月 3 — 5月 15。
    课程学习时间
    5 weeks of study, 5-7 hours/week
    字幕
    English

    课程概述

    This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. The course will apply Bayesian methods to several practical problems, to show end-to-end Bayesian analyses that move from framing the question to building models to eliciting prior probabilities to implementing in R the final posterior distribution. Additionally, the course will introduce credible regions, Bayesian comparisons of means and proportions, Bayesian regression and inference using multiple models, and discussion of Bayesian prediction.
  5. 第 5 门课程

    Statistics with R Capstone

    即将开课的班次:4月 24 — 6月 26。
    课程学习时间
    5-10 hours/week
    字幕
    English

    毕业项目介绍

    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.

制作方

  • 杜克大学

    Duke University is consistently ranked as a top research institution, with graduate and professional schools among the leaders in their fields.

    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.

  • Mine Çetinkaya-Rundel

    Mine Çetinkaya-Rundel

    Assistant Professor of the Practice
  • David Banks

    David Banks

    Professor of the Practice
  • Colin Rundel

    Colin Rundel

    Assistant Professor of the Practice
  • Merlise A Clyde

    Merlise A Clyde

    Professor

FAQs

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