Learn R for DataScience

Learn R for DataScience, available at $19.99, with 43 lectures.

You will learn about 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 This course is ideal for individuals who are Data Science Beginners and All the aspiring students who wants to learn R to do DataScience It is particularly useful for Data Science Beginners and All the aspiring students who wants to learn R to do DataScience.

Enroll now: Learn R for DataScience

Summary

Title: Learn R for DataScience

Price: $19.99

Number of Lectures: 43

Number of Published Lectures: 43

Number of Curriculum Items: 43

Number of Published Curriculum Objects: 43

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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 tidyverse
- Packages:
  - ggplot2 – Data Visualization
  - dplyr – Data Transformation and Relational Data
  - tidyr – Data Cleaning
  - readr – Importing Data
  - rmarkdown – Communicating the results / findings
Git and GitHub
- Version 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 RStudio
- How 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.

Course Curriculum

Chapter 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?

Chapter 2: Importing data into R and R Studio

Lecture 1: Reading CSV data into R

Lecture 2: Exporting data from R and R Studio to a external csv file

Chapter 3: Cleaning Data

Lecture 1: Introduction to tidyr

Lecture 2: What makes a data tidier?

Lecture 3: Spreading data

Lecture 4: Gathering data

Lecture 5: Separating Columns

Lecture 6: Uniting Columns

Lecture 7: How to deal with Missing Values?

Lecture 8: A case study on data cleansing

Chapter 4: Data Transformation with dplyr

Lecture 1: Prerequisites

Lecture 2: Filtering observations

Lecture 3: Arranging observations

Lecture 4: Selecting columns

Lecture 5: Creating new columns

Lecture 6: Grouped summaries

Lecture 7: Grouped mutates and filters

Chapter 5: Handling relational data in R

Lecture 1: Introduction

Lecture 2: Understanding keys

Lecture 3: Mutating joins

Lecture 4: inner join

Lecture 5: outer joins

Lecture 6: Duplicate keys

Lecture 7: Defining the Key Columns

Lecture 8: Filtering Joins

Lecture 9: Set operations

Chapter 6: Visualizing data in R

Lecture 1: Understanding mpg dataset

Lecture 2: Creating a ggplot

Lecture 3: Aesthetic mappings

Lecture 4: Facet your plots

Lecture 5: Geometric objects in ggplot

Lecture 6: Statistical transformations in ggplot

Lecture 7: Position adjustments in ggplot

Lecture 8: Coordinate Systems

Chapter 7: Using Git and Github with R Studio

Lecture 1: Setting up Git in R Studio

Lecture 2: Creating a project in Github

Lecture 3: Cloning a project in R Studio from Github

Lecture 4: Commit Changes and Push the changes to Github

Lecture 5: Pull the changes from Github

Instructors

Sathish Gnanaprakasam
Software Developer

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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