Published 9/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: 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 course?Library 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 anytime 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 Arranging 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 Beginners and All the aspiring students who wants to learn R to do DataScience
Homepage
https://www.udemy.com/course/learn-r-for-datascience/
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