Hands On Training

Introduction to Data in R

Name: Introduction to Data in R
Brand: Careervira
Price: 149 USD
Availability: InStock
Rating: 5.0 (3 reviews)

DataCamp

(3)

Course Report

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Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Beginner

Teaching Type

Self Paced

Video Content

4 hours

Course Description

To answer questions, scientists use rigorous methods and closely observed observations. These observations are collected from field notes, surveys and experiments. These observations are called data. Statistics is the study of how data can be collected, analyzed and drawn conclusions. It is helpful to understand statistics in the context of an investigation. 4) Form a conclusion. This course will concentrate on the first two steps.

Highlights

Hands on training

Top 20 Percentile

Rating & Reviews

Top 30 Percentile

Parameters

Hands on training

This course stands out as one of the top 20 percentile options in R for Data Science, offering unparalleled hands-on training. Learners gain practical experience and skills through immersive learning, preparing them for real-world challenges. It ensures a well-rounded skill set, catering to a range of learning preferences. With a focus on Hands on training as well as essential Case Based Learning and Virtual Labs, this course is tailored to meet diverse educational needs.

Rating & Reviews

This highly acclaimed course is among the top-rated in R for Data Science, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in R for Data Science.

Course Overview

Virtual Labs

International Faculty

Case Based Learning

Post Course Interactions

Case Studies,Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to the Tidyverse

What You Will Learn

You will learn about observational studies and experiments, scope of inference, and Simpson's paradox

This chapter introduces terminology of datasets and data frames in R

Course Content

Expand all sections

Module 1: Language of data

Module 2: Study types and cautionary tales

Module 3: Sampling strategies and experimental design

Module 4: Case study

Course Instructors

Mine Cetinkaya-Rundel

Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio

Mine is the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University as well as a Professional Educator at RStudio. Her...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%