Statistics with R Specialization

Starts Jun 04

Statistics with R Specialization

Master Statistics with R. Statistical mastery of data analysis including inference, modeling, and Bayesian approaches.

About This Specialization

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.

Created by:

courses
5 courses

Follow the suggested order or choose your own.

projects
Projects

Designed to help you practice and apply the skills you learn.

certificates
Certificates

Highlight your new skills on your resume or LinkedIn.

Courses
Beginner Specialization.
No prior experience required.
  1. COURSE 1

    Introduction to Probability and Data

    Upcoming session: Jun 4
    Commitment
    5 weeks of study, 5-7 hours/week
    Subtitles
    English, Korean

    About the Course

    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 explorat
  2. COURSE 2

    Inferential Statistics

    Upcoming session: Jun 4
    Commitment
    5 weeks of study, 5-7 hours/week
    Subtitles
    English

    About the Course

    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 interpret
  3. COURSE 3

    线性回归和建模

    Upcoming session: Jun 4
    Commitment
    学习时间4周,每周5-7个小时
    Subtitles
    English

    About the Course

    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
  4. COURSE 4

    Bayesian Statistics

    Upcoming session: May 28
    Commitment
    5 weeks of study, 5-7 hours/week
    Subtitles
    English

    About the Course

    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 introduce
  5. COURSE 5

    Statistics with R Capstone

    Upcoming session: Jun 18
    Commitment
    5-10 hours/week
    Subtitles
    English

    About the Capstone Project

    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

Creators

  • Duke University

    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

    Associate 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

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