Duke University

Introduction to probability and data with R

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В любой момент
14 часов
Стоимость курса
49 USD/мес.
Подробности и регистрация

Introduction to probability and data with R

Высший рейтинг на Coursera
Навыки, которые вы получите:
R Statistics Rstudio Exploratory data analysis

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 Program 

  1. Introduction to Data. Introduction to Data Project
    You will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
  2. Exploratory Data Analysis and Introduction to Inference. Exploratory Data Analysis and Introduction to Inference Project
    This week you will delve into numerical and categorical data in more depth, and introduce inference.
  3. Introduction to Probability. Introduction to Probability Project
    This week you will discuss probability, conditional probability, the Bayes’ theorem, and provide a light introduction to Bayesian inference. 
  4. Probability Distributions
    This week you will introduce two probability distributions: the normal and the binomial distributions in particular. 
  5. Data Analysis Project
    There will not be any new videos in this week, instead, you will be asked to complete an initial data analysis project with a real-world data set. The project is designed to help you discover and explore research questions of your own, using real data and statistical methods.
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