R Programming Easy As ABC- For The Beginner Data Scientist

R Programming Easy As ABC: For The Beginner Data Scientist, available at $19.99, has an average rating of 4.36, with 59 lectures, 8 quizzes, based on 7 reviews, and has 13 subscribers.

You will learn about Foundation of R Programming How to create variables How to create data structures How to use r functions cbind(), rbind() Hands on practice with large dataset How to extend R with packages How to use data science package Tidyverse Learn to work with dataframes Learn data analyzation using ggplot2 package Data manipulation and conditional statements This course is ideal for individuals who are Beginner data scientist, data analysts, business analyst, programmer or developer or Any individual, hobbyist or enthusiast curious about learning data science using R Programming It is particularly useful for Beginner data scientist, data analysts, business analyst, programmer or developer or Any individual, hobbyist or enthusiast curious about learning data science using R Programming.

Enroll now: R Programming Easy As ABC: For The Beginner Data Scientist

Summary

Title: R Programming Easy As ABC: For The Beginner Data Scientist

Price: $19.99

Average Rating: 4.36

Number of Lectures: 59

Number of Quizzes: 8

Number of Published Lectures: 58

Number of Published Quizzes: 8

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 66

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Foundation of R Programming

How to create variables

How to create data structures

How to use r functions cbind(), rbind()

Hands on practice with large dataset

How to extend R with packages

How to use data science package Tidyverse

Learn to work with dataframes

Learn data analyzation using ggplot2 package

Data manipulation and conditional statements

Who Should Attend

Beginner data scientist, data analysts, business analyst, programmer or developer

Any individual, hobbyist or enthusiast curious about learning data science using R Programming

Target Audiences

Beginner data scientist, data analysts, business analyst, programmer or developer

Any individual, hobbyist or enthusiast curious about learning data science using R Programming

Hello and welcome to R Programming Easy as ABC!

This course was developed for the beginner programmer, data analyst, developer or individual who is interested in learning the fundamentals of R Programming.  R Programming has somewhat of a reputation for being difficult to learn.  That’s why we designed this course for the absolute beginner. This course will prepare you for working with data and learning the basics of the data science life cycle!

What makes this course unique?

Designed for the absolute beginner. No prior programming experience required.

Present complex concepts in a way that is relatable and easy to understand. 

Have included fun and practical examples and illustrations to enhance learning. 

Have included several examples and opportunities to practice concepts with hands on experience.

What Will I Learn:

Fundamentals to R Programming

How to import / export data

How to create unique data

How to analyze data

How to manipulate data

How to visualize data using graphs

What Is Covered In This Course:

Data Types (Character, Double, Integer, Logical)

Data Structures (Vector, Matrix, List, Dataframe)

How to work with Built In Functions (ex: cbind, rbind)

How to work with User Defined Functions

How to work with Packages (ggplot2)

Variable Names

Vectorization

Vector Recycling

Subsetting Data Structures

Implicit / Explicit Coercion

Loops (For Loop, While Loop, Repeat Loop)

Conditional Statements

Logical and Numeric Operators

Data Import and Export

Accessing R built in datasets

Data Creation

Data Analytics

Data Wrangling

Data Manipulation

Data Visualization

Grammar of Graphics using R package ggplot2

Most importantly HAVE FUN!!!!!

Course Curriculum

Chapter 1: What is R?

Lecture 1: Lets Have Some Fun

Chapter 2: Installing R

Lecture 1: Install R Base

Lecture 2: Install R Studio

Lecture 3: Opening R for the First Time

Lecture 4: R Layout

Chapter 3: Installing Packages

Lecture 1: Library and Packages

Lecture 2: Install and Activate tidyverse Package

Lecture 3: R for Data Science

Lecture 4: How To Get Help

Lecture 5: Helpful Tips and Tricks

Chapter 4: Data Types and Data Structures

Lecture 1: Introduction Data Types

Lecture 2: Data Types

Lecture 3: Introduction Data Structures

Chapter 5: Data Structure: Vector

Lecture 1: Introduction Vectors

Lecture 2: Vector and Data Types: Implicit Coercion

Lecture 3: Hands On Practice: Linear Model

Lecture 4: Vector and Explicit Coercion

Lecture 5: Vectors and Characters

Lecture 6: Vectors and Strings

Lecture 7: Vectors and Numbers

Lecture 8: Vectors and Logical Data Types

Lecture 9: Vectorization and Vector Recycling

Lecture 10: Vectors and Subsetting

Chapter 6: Data Structure: Matrix

Lecture 1: Introduction Matrix

Lecture 2: Matrix Function

Lecture 3: Matrix cbind and rbind

Lecture 4: Matrix and Subsetting

Chapter 7: Data Structures: List and Array

Lecture 1: Introduction List

Lecture 2: List

Lecture 3: Array

Chapter 8: Data Structure: Dataframe

Lecture 1: Introduction Dataframe

Lecture 2: Import External Dataset

Lecture 3: Create Dataframe In R Using Function

Lecture 4: R Built-In Datasets

Lecture 5: Dataframe and Subsetting

Chapter 9: Data Preparation and Data Exploration Analysis

Lecture 1: Data Preparation

Lecture 2: Hands On Data Preparation

Lecture 3: Data Exploration Analysis

Chapter 10: Data Manipulation

Lecture 1: dplyr Data Manipulation functions

Lecture 2: Group By and Summarize

Lecture 3: Hands On Data Manipulation

Chapter 11: Functions

Lecture 1: Built In Functions

Lecture 2: User Defined Functions (UDF)

Chapter 12: Loops

Lecture 1: For Loop

Lecture 2: While Loop

Lecture 3: Repeat Loop

Lecture 4: Loops vs Vectorization

Chapter 13: If Then Conditional Statements

Lecture 1: IF Statements

Lecture 2: IF ELSE Statements

Lecture 3: IF ELSE IF Statements

Lecture 4: ifelse function in R

Chapter 14: Data Visualization: ggplot2 package

Lecture 1: Introduction ggplot

Lecture 2: Basic Graph (Data, Aes, Geom)

Lecture 3: Stat

Lecture 4: ggplot Categorical vs Continuous Data

Lecture 5: Grammar of Graphics

Chapter 15: CONCLUSION

Lecture 1: BONUS Hands On: gganimate package

Lecture 2: Thank You

Instructors

Bayti Data
Data Analysts who are passionate about Data Science

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Frequently Asked Questions

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