2024 R 4.0 Programming for Data Science || Beginners to Pro

2024 R 4.0 Programming for Data Science || Beginners to Pro, available at $59.99, has an average rating of 4.65, with 126 lectures, based on 150 reviews, and has 13014 subscribers.

You will learn about Learn to write a program in R 4.0 Learn fundamentals of R programming How to use R-Studio How to analyze the data How to plot beautiful plots Real exercise for data analysis Use for Machine Learning programming Write code for Linear Regression and Logistic Regression Analysis Data visualization on real dataset | Covid-19, Boston Housing Price and Titanic dataset Learn Plotly for Covid-19 Data Analysis Advanced Plotly in R Linear Regression in R Non-Linear and Polynomial Regression Multiple Simple Linear Regression in R on Boston Housing Price Prediction This course is ideal for individuals who are Data Scientist Beginners or R Programmers or Data Scientist who codes in R or Data Analyst who codes in R or Data Scientist managers, executives or students It is particularly useful for Data Scientist Beginners or R Programmers or Data Scientist who codes in R or Data Analyst who codes in R or Data Scientist managers, executives or students.

Enroll now: 2024 R 4.0 Programming for Data Science || Beginners to Pro

Summary

Title: 2024 R 4.0 Programming for Data Science || Beginners to Pro

Price: $59.99

Average Rating: 4.65

Number of Lectures: 126

Number of Published Lectures: 126

Number of Curriculum Items: 126

Number of Published Curriculum Objects: 126

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to write a program in R 4.0
Learn fundamentals of R programming
How to use R-Studio
How to analyze the data
How to plot beautiful plots
Real exercise for data analysis
Use for Machine Learning programming
Write code for Linear Regression and Logistic Regression Analysis
Data visualization on real dataset | Covid-19, Boston Housing Price and Titanic dataset
Learn Plotly for Covid-19 Data Analysis
Advanced Plotly in R
Linear Regression in R
Non-Linear and Polynomial Regression
Multiple Simple Linear Regression in R on Boston Housing Price Prediction

Who Should Attend

Data Scientist Beginners
R Programmers
Data Scientist who codes in R
Data Analyst who codes in R
Data Scientist managers, executives or students

Target Audiences

Data Scientist Beginners
R Programmers
Data Scientist who codes in R
Data Analyst who codes in R
Data Scientist managers, executives or students

Take your first step towards becoming a data science expert with our comprehensive R programming course. This course is designed for beginners with little or no programming experience, as well as experienced R developers looking to expand their skill set.

You’ll start with the basics of R programming and work your way up to advanced techniques used in data science. Along the way, you’ll gain hands-on experience with popular R libraries such as dplyr, ggplot2, and tidyr.

You will learn how to import, clean and manipulate data, create visualizations and statistical models to gain insights and make predictions. You will also learn data wrangling techniques and how to use R for data visualization.

By the end of the course, you’ll have a solid understanding of R programming and be able to apply your new skills to a wide range of data science projects. You’ll also learn how to use R in Jupyter notebook, so that you can easily share your work and collaborate with others.

So, if you’re ready to take your first step towards becoming a data science expert, this is the course for you! With our hands-on approach and interactive quizzes, you’ll be able to put your new skills into practice right away.

In this course, you learn:

How to install R-Packages
How to work with R-data types
What is R DataFrame, Matrices, Vectors, etc?
How to work with DataFrames
How to perform join and merge operations on DataFrames
How to plot data using ggplot2 in R 4.0
Analysis of real-life dataset Covid-19

How this course will help you?

This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.

What makes us qualified to teach you?

I am a Ph.D. Scholar in Machine Learning and taught tens of thousands of students over the years through my classes at the KGP Talkie YouTube channel. A few of my courses are part of Udemy’s top 5000 courses collection and curated for Udemy Business. I promise you will not regret it.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Install R and R-Studio for Data Science

Lecture 2: Download Code Files || Do Not Skip This!!!

Lecture 3: R-Studio Introduction

Chapter 2: R Programming Fundamentals

Lecture 1: Variable Assignments

Lecture 2: Rules of Variable Names in R

Lecture 3: Arithmetic Operators

Lecture 4: Relational Operators

Lecture 5: Logical Operators

Lecture 6: Assignment Operators

Lecture 7: Miscellaneous Operators

Lecture 8: Function in R

Lecture 9: Data Types in R

Lecture 10: Strings Assignment

Lecture 11: paste() Function for String Manipulation

Lecture 12: format() Function for Numeric Data Formatting

Lecture 13: Colon (:) Operator for Vector Generation

Lecture 14: Using [] Operator and c() Function to Access Vector Elements

Lecture 15: Vector Manipulation

Lecture 16: List Creation

Lecture 17: Named List

Lecture 18: List Manipulation and Merging

Lecture 19: List to Vectors and Vectors to List

Lecture 20: Introduction to Matrix

Lecture 21: Arithmetic Operations on Matrix

Lecture 22: Arrays Introductions

Lecture 23: Arrays Naming and Accessing the Values

Lecture 24: Factors in R

Lecture 25: If If-Else and If-Else-If Statements

Lecture 26: repeat() and while() Loops

Lecture 27: for() Loop

Lecture 28: next Statement and break Statement

Chapter 3: Fundamentals of DataFrames in R Programming

Lecture 1: Create DataFrame in R

Lecture 2: Get the DataFrame Details

Lecture 3: Working with [, [[ and $ Operator

Lecture 4: Access DataFrames Like Matrix

Lecture 5: Modify a DataFrame

Lecture 6: Loading a DataFrame from .CSV File

Lecture 7: Load DataFrame from Excel .xlsx File

Lecture 8: Loading a DataFrame from .XML File

Lecture 9: Loading a DataFrame from .json File

Lecture 10: Bind Rows || rbind() and bind_rows()

Lecture 11: Bind Columns || cbind() and bind_cols()

Lecture 12: Data Frame Selection and Indexing

Lecture 13: Conditional DataFrame Selection with subset()

Lecture 14: Working with DateTime in DataFrame

Lecture 15: Export DataFrame in .CSV File

Lecture 16: Data Frame Sorting

Lecture 17: Groupby on DataFrame in R

Lecture 18: Data Frame Merge and Join || Inner Join

Lecture 19: Left, Right, and Outer Merge (Join) of DataFrame in R

Chapter 4: Jupyter Notebook Introduction for R Programming

Lecture 1: Jupyter Notebook Introduction

Lecture 2: Anaconda Installation for Windows 10

Lecture 3: Anaconda Installation for Linux

Lecture 4: R 4.x Installation in Anaconda with Jupyter Notebook

Lecture 5: Jupyter Notebook Shortcuts Part 1

Lecture 6: Jupyter Notebook Shortcuts Part 2

Lecture 7: Jupyter Notebook Shortcuts Part 3

Lecture 8: Jupyter Notebook Shortcuts Part 4

Lecture 9: R Coding Practice with Jupyter Notebook vs R-Studio

Chapter 5: Fundamentals of Data Visualization with GGPlot2

Lecture 1: Introduction to GGPlot2

Lecture 2: R Packages Installation and Loading

Lecture 3: Must Read

Lecture 4: Covid-19 Dataset Loading

Lecture 5: Bar Plot – Top 10 Worst Hit Countries

Lecture 6: Add Title, Subtitle and Caption in GGPlot

Lecture 7: Change Title and Caption Style- Font Size, Color and Face

Lecture 8: Change Text Position and Increase Figure Size

Lecture 9: Scatter Plot (Point Plot) for Covid-19 Dataset

Lecture 10: Line Plot for Covid-19 Data || Confirmed, Recovered and Deaths Analysis

Lecture 11: Loading the Boston Housing Price Dataset for Visualization

Lecture 12: Scatter Plot for Boston Housing Data

Lecture 13: Pair Plot – Scatter Matrix Plot for Boston Housing Dataset

Lecture 14: Load Titanic Dataset for Visualization

Lecture 15: Data Cleaning and Bar Plot

Lecture 16: Scatter Plot for Titanic Dataset

Lecture 17: Histogram Plot

Lecture 18: Stacked Histogram

Lecture 19: Density Plot

Lecture 20: Box Plot

Lecture 21: Violin Plot

Chapter 6: Data Preprocessing and Analysis with tidyverse and dplyr

Lecture 1: Jupyter Notebook Opening

Lecture 2: Getting Started with tidyverse and dplyr

Lecture 3: Must Read

Lecture 4: select() – Select Columns of a Dataframe

Lecture 5: filter() || Extract subset of Rows

Lecture 6: arrange() – DataFrame Sorting

Lecture 7: rename() || Renaming DataFrame Columns

Lecture 8: mutate() || Compute Transformations of Variables

Lecture 9: group_by() || Group DataFrame Column-wise

Lecture 10: %>% || Pipeline Operator

Lecture 11: distinct() || Get the Unique Rows

Lecture 12: count() tally() add_count() add_tally() || Count the Unique Values in DataFrame

Lecture 13: rename_with() || Rename Columns by using Function

Lecture 14: summarise() and summarize() || Create Summary of Columns in a DataFrame

Instructors

Laxmi Kant | KGP Talkie
AVP, Data Science Join Ventures | IIT Kharagpur | KGPTalkie

Rating Distribution

1 stars: 0 votes
2 stars: 7 votes
3 stars: 13 votes
4 stars: 35 votes
5 stars: 95 votes

Frequently Asked Questions

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