Sampling in R

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

Sampling is an important part of hypothesis testing. Sampling is essential in survey analysis and experimental design. This course will teach you how to conduct common sampling methods and why sampling is so important. This course covers simple random sampling up to more complex methods like stratified and cluster sampling. This course will teach you how to estimate the population and quantify uncertainty by using bootstrap or sampling distributions. This course will provide real-world data about employee attrition, Spotify songs and coffee ratings.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to Statistics in R

What You Will Learn

Master sampling to get more accurate statistics with less data

This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling

Throughout the course, you'll explore real-world datasets on coffee ratings, Spotify songs, and employee attrition

Course Content

Expand all sections

Module 1: Bias Any Stretch of the Imagination

Module 2: Don't get theory eyed

Module 3: The n's justify the means

Module 4: Pull Your Data Up By Its Bootstraps

Course Instructors

Richie Cotton

Curriculum Architect at DataCamp

Richie is a Learning Solutions Architect at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R package...