R Programming Ninja Course 2024-Data Science with 5 Projects

R Programming Ninja Course 2024:Data Science with 5 Projects, available at $79.99, has an average rating of 4.5, with 50 lectures, based on 139 reviews, and has 1364 subscribers.

You will learn about R Programming R Datatypes R Data Structures Vectors, Matrices, Arrays, Lists Data analysis Data Visualization using GGPLOT2 Case Studies on Data analysis using R Projects on Data analysis using R Data Cleaning Data Transformations using tidyr, Dplyr String Manipulations using Stringr Handling Date and Time using Lubridate Projects on Data Visualization using R This course is ideal for individuals who are Beginner or Intermediate or Advanced It is particularly useful for Beginner or Intermediate or Advanced.

Enroll now: R Programming Ninja Course 2024:Data Science with 5 Projects

Summary

Title: R Programming Ninja Course 2024:Data Science with 5 Projects

Price: $79.99

Average Rating: 4.5

Number of Lectures: 50

Number of Published Lectures: 49

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 49

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

R Programming

R Datatypes

R Data Structures

Vectors, Matrices, Arrays, Lists

Data analysis

Data Visualization using GGPLOT2

Case Studies on Data analysis using R

Projects on Data analysis using R

Data Cleaning

Data Transformations using tidyr, Dplyr

String Manipulations using Stringr

Handling Date and Time using Lubridate

Projects on Data Visualization using R

Who Should Attend

Beginner

Intermediate

Advanced

Target Audiences

Beginner

Intermediate

Advanced

Data Science and Analytics is a highly rewarding career that allows you to solve some of the world’s most interesting problems. The field of data science has exploded in the past two decades and shows no signs of stopping any time soon. Many big or small businesses and companies wish to make use of the insights gained through the big data.

Due to its open-source nature and its extreme versatility, R has become the primary tool for statistical analysis and data science. With the industry facing a shortage of data scientists all over the world, both novice and professional R programmers can enter. R community represents the cutting-edge in the field of data science.

This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.

This course providesFull-fledged knowledge of R, we cover it all.

Our exotic journey will include the concepts of:

1. What’s and Why’s of R programming Language –Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.

2. Core knowledge for DataTypes.

3. String Manipulation and handling using Stringr Package

4. Data Structures (Vectors, Matrices, Arrays, List)

5. Loops and Conditionsand Functionsfor programming skills in R.

6. Dataframesexplained in detail and perspective for Data Analysis Process and Concepts.

7. Most importantly Data Transformationshave been covered to make you comfortable with how data should be handled and transformed for analysis.

8. Date Time Modulehelps to understand and handle date and time in R.

9. Descriptive Statisticsallows to explore the data summaries for statistics.

10. Data Visualization using GGPLOT2used for simple and complex visual analysis.

11. All the modules include practice questionsand case studiesto give you idea on the real world problems and enhancing problem solving skills.

12. 5 Projects allow you to perform analysis on datasets with scope for further exploring and enhancing skills while building confidence.

Course Curriculum

Chapter 1: Introduction To Course

Lecture 1: Course Introduction

Chapter 2: Rstudio – Getting Started

Lecture 1: Introduction

Lecture 2: Rstudio Introduction and Loading Packages

Chapter 3: R Datatypes

Lecture 1: R Datatypes

Chapter 4: R Datatypes Practice

Lecture 1: R Datatypes Practice

Chapter 5: R Class object Data types and Logical operators

Lecture 1: R Class object Data types and Logical operators

Chapter 6: Characters

Lecture 1: characters

Chapter 7: Practice : Numbers and Strings

Lecture 1: PQ: Numbers and Strings

Chapter 8: Vectors vs List vs Matrix vs Array vs DataFrames

Lecture 1: Vectors vs list vs matrix vs array vs DF

Chapter 9: Vectors

Lecture 1: Vectors

Lecture 2: Vector Indexing

Chapter 10: Matrices

Lecture 1: Matrices

Chapter 11: Lists

Lecture 1: Lists

Chapter 12: Practice Questions : Vectors

Lecture 1: PQ3: Vectors

Lecture 2: PQ4: Vectors

Chapter 13: Loops and Conditions

Lecture 1: Loops And Conditions

Chapter 14: Functions

Lecture 1: Functions

Lecture 2: Functions and Scope of Variables

Chapter 15: Practice Questions

Lecture 1: PQ5: Functions

Chapter 16: Factor Variable Trap (FVT)

Lecture 1: Factor Variable Trap

Chapter 17: Stringr Package

Lecture 1: String Functions using StringR

Chapter 18: Dataframes

Lecture 1: Dataframes

Lecture 2: Variable Types

Lecture 3: Data Preparation Guidelines

Chapter 19: Practice Questions

Lecture 1: PQ6: Dataframes

Chapter 20: Data Transformations (Apply Family)

Lecture 1: Data Transformations

Chapter 21: Data Transformation Using Dplyr & Tidyr

Lecture 1: Data Transformations using Dplyr and tidyr

Chapter 22: Practice Questions

Lecture 1: PQ7: Data Preparation

Lecture 2: PQ 8: Data transformation

Chapter 23: Date Time in R using Lubridate

Lecture 1: lubridate

Chapter 24: Descriptive Statistics using R

Lecture 1: Descriptive Statistics

Lecture 2: Descriptive Statistics Using R

Chapter 25: Data Visualization Using GGPLOT2

Lecture 1: GGPLOT INTRO

Lecture 2: GGPLOT2 plotting

Lecture 3: QPlot function

Chapter 26: GGPLOT2 Practice questions

Lecture 1: ggplot2 practice questions

Chapter 27: Case Studies

Lecture 1: Case Study 1 :Titanic Dataset Complete Visualization

Lecture 2: Case Study 2 : HR Analytics

Chapter 28: Projects And Solutions

Lecture 1: Project 1: Seattle Data Transformation Problem

Lecture 2: Project 1: Seattle Collision Data Transformation Solution

Lecture 3: Project 2: Diamond data analysis Problem

Lecture 4: Project 2: Diamonds Dataset Analysis Solution

Lecture 5: Project 3: Facebook data analysis Problem

Lecture 6: Project 3: Facebook data Analysis Solution

Lecture 7: Project 4: GOT Data Analysis Problem

Lecture 8: Project 4: GOT Data Analysis Solution

Chapter 29: Major Project

Lecture 1: Project 5: NYC Taxi Data Analysis Problem

Lecture 2: Project 5: NYC Taxi Data Analysis Solution

Chapter 30: BONUS!!!

Lecture 1: BONUS !!!

Instructors

MG Analytics
Data Scientist and Professional Trainer

Rating Distribution

1 stars: 4 votes

2 stars: 2 votes

3 stars: 17 votes

4 stars: 41 votes

5 stars: 75 votes

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!