Learn R For Datascience

[LeeAndro]

Published 9/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 3.88 GB | Duration: 4h 13m

learn the R libraries and packages to do Data Science

What you'll learn
Reading and Writing data with readr
Data Visualization using ggplot2
Data Transformation using dplyr
Relational Data with dplyr
Data Cleaning using tidyr
Communication using RMarkdown
Requirements
No Prerequisites.​

No Programming experience needed. You will learn everything you need to know.
Description
This course teaches you all the necessary libraries and packages you need to know in order to do Data Science. ! The tools which you will learn can be applied to any of the real world datasets.What you will learn in this courseLibrary tidyversePackages:ggplot2 - Data Visualizationdplyr - Data Transformation and Relational Datatidyr - Data Cleaningreadr - Importing Datarmarkdown - Communicating the results / findingsGit and GitHubVersion control system that will be helpful in maintaining RMarkdown files in the cloud and work with the files seamlessly any you wish from anywhere.R and RStudioHow to work with R dev kit and RStudio, an IDE to work with R-Projects.This course introduces you to the necessary tools listed above and slowly teaches you all the functions within every single packages along with the exercises. The exercises are provided through the GitHub which you can pull and complete. It also enables you to collaborate with me directly via the GitHub for every specific exercise.There are case studies which I will teach you within this course to demonstrate on how to apply the tools and packages we learn in this course to a real world dataset.There are many different courses in the market. But I bet, this course will provide you with all the essential skills you need to explore the Data Science Venture.I can't wait to see you in the course. Sincerely,Sathish Gnanaprakasam.

Overview

Section 1: Introduction & Setting up the environment

Lecture 1 Introduction

Lecture 2 Installing R

Lecture 3 Installing R Studio

Lecture 4 Installing Tidyverse package inside RStudio

Section 2: Importing data into R and R Studio

Lecture 5 Reading CSV data into R

Lecture 6 Exporting data from R and R Studio to a external csv file

Section 3: Cleaning Data

Lecture 7 Introduction to tidyr

Lecture 8 What makes a data tidier

Lecture 9 Spreading data

Lecture 10 Gathering data

Lecture 11 Separating Columns

Lecture 12 Uniting Columns

Lecture 13 How to deal with Missing Values

Lecture 14 A case study on data cleansing

Section 4: Data Transformation with dplyr

Lecture 15 Prerequisites

Lecture 16 Filtering observations

Lecture 17 Arrag observations

Lecture 18 Selecting columns

Lecture 19 Creating new columns

Lecture 20 Grouped summaries

Lecture 21 Grouped mutates and filters

Section 5: Handling relational data in R

Lecture 22 Introduction

Lecture 23 Understanding keys

Lecture 24 Mutating joins

Lecture 25 inner join

Lecture 26 outer joins

Lecture 27 Duplicate keys

Lecture 28 Defining the Key Columns

Lecture 29 Filtering Joins

Lecture 30 Set operations

Section 6: Visualizing data in R

Lecture 31 Understanding mpg dataset

Lecture 32 Creating a ggplot

Lecture 33 Aesthetic mappings

Lecture 34 Facet your plots

Lecture 35 Geometric objects in ggplot

Lecture 36 Statistical transformations in ggplot

Lecture 37 Position adjustments in ggplot

Lecture 38 Coordinate Systems

Section 7: Using Git and Github with R Studio

Lecture 39 Setting up Git in R Studio

Lecture 40 Creating a project in Github

Lecture 41 Cloning a project in R Studio from Github

Lecture 42 Commit Changes and Push the changes to Github

Lecture 43 Pull the changes from Github

Data Science Bners and All the aspiring students who wants to learn R to do DataScience

HomePage:

Https://anonymz.com/https://www.udemy.com/course/learn-r-for-datascience/

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