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:
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How to install R-Packages
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How to work with R-data types
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What is R DataFrame, Matrices, Vectors, etc?
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How to work with DataFrames
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How to perform join and merge operations on DataFrames
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How to plot data using ggplot2 in R 4.0
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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
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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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