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  • The R working tools and environment for professionals
  • The R syntax and how to explain and describe the code using comments
  • Variables, Values and assignments
  • All the Data types available in R
  • Performing mathematical operations, type conversion built-in functions and many useful built-in functions for math operations
  • Working with collection of characters and strings in R, also essential character operations
  • Logical values and booleans
  • Handling different operations on variables and values by using different types of operators
  • All the Data Structures in R such as vectors, lists, matrices, data frames and factors. And also all the essential operations for these data structures
  • Decision making by using conditional statements in R
  • Repeat block of code and iterate over collections with loops
  • Functional programming and code reusing
  • Statistics and data analysis concepts: datasets, many built-in functions, techniques and tools for statistical operations
  • Data visualizations and Graphics in R: drawing points, line plotting, pie charts, bar charts, histograms and more
  • Get the instructor QA Support

Hello and welcome to the exciting world of the R programming language.
 
2023 Data Science: R Programming Complete Diploma
 
R is one of the most powerful programming languages, for statistical computing and graphical presentation to analyze and visualize data.
 
In this course, I’m going to show you how to code with R from the R basics to the R advanced concepts.
 
Also, you will explore how the R programming language can be used today for data analysis and the production of beautiful data visualizations and graphics.
 
The best part? Every single topic and tool in this course will be explained theoretically and practically with real examples step by step.
 
This course will cover all the R essentials needed for everyone such as:
 
The R working tools and environment for professionals
 
The R syntax and how to explain and describe the code using comments
 
Variables, Values and assignments
 
All the Data types available in R.
 
Performing mathematical operations, type conversion built-in functions and many useful built-in functions for math operations.
 
Working with collection of characters and strings in R, also essential character operations
 
Logical values and Booleans.
 
Handling different operations on variables and values by using different types of operators.
 
All the Data Structures in R such as vectors, lists, matrices, data frames and factors
 
And also all the essential operations for these data structures.
 
decision making by using conditional statements in R.
 
Repeat block of code and iterate over collections with loops.
 
Functional programming and code reusing.
 
Statistics and data analysis concepts: datasets, many built-in functions, techniques and tools for statistical operations.
 
Graphics and data visualizations in R: drawing points, line plotting, pie charts, bar charts, histograms and more.
 
You will learn and understand all these concepts and more.
 
 
 
R is free open source, and very widely used by professional statisticians and data scientists.
 
It is also very popular in certain application areas, including bioinformatics. R is a dynamically typed interpreted language, and is typically used interactively. It has many built-in functions and libraries, and is extensible, allowing users to define their own functions and procedures using R, C or Fortran. It also has a simple object system. So, it's really powerful!
 
So, what are you waiting for, enroll now to go through a complete bootcamp of one of the most popular and powerful programming languages on the market for , R.
 
Become A Professional R Programmer and Data Scientist in no time!
 
Let's get started

  • No programming experience needed. You'll learn everything you need to know
  • Beginner R Programmers
  • New developers and Engineers
  • Programming and software development engineering newbies
  • Developers and Engineers who know other programming language but are new to R
  • Developers curious about Learning R for data science
  • Beginner Data Engineers/Scientists
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  • Section 1 : Introduction 4 Lectures 00:16:19

    • Lecture 1 :
    • Lecture 2 :
    • Downloading, and Installing and R
    • Lecture 3 :
    • Downloading, and Installing and Exploring RStudio
    • Lecture 4 :
    • Setup working directory
  • Section 2 : Variables Data Types 8 Lectures 00:23:52

    • Lecture 1 :
    • Variables in detail 1
    • Lecture 2 :
    • Variables in detail 2
    • Lecture 3 :
    • Variables in detail 3
    • Lecture 4 :
    • Variables in detail 4
    • Lecture 5 :
    • Data types 1
    • Lecture 6 :
    • Data types 2
    • Lecture 7 :
    • Data types 3
    • Lecture 8 :
    • Code hints
  • Section 3 : Numbers and Math 3 Lectures 00:24:56

    • Lecture 1 :
    • Type of numbers
    • Lecture 2 :
    • Type conversion
    • Lecture 3 :
    • Math operations
  • Section 4 : Strings 7 Lectures 00:16:38

    • Lecture 1 :
    • Strings 1
    • Lecture 2 :
    • Strings 2
    • Lecture 3 :
    • Strings 3
    • Lecture 4 :
    • Strings 4
    • Lecture 5 :
    • Strings 5
    • Lecture 6 :
    • Strings 6
    • Lecture 7 :
    • Strings 7
  • Section 5 : Logical and Operators 7 Lectures 00:18:53

    • Lecture 1 :
    • Logical values 1
    • Lecture 2 :
    • Logical values 2
    • Lecture 3 :
    • Operators 1
    • Lecture 4 :
    • Operators 2
    • Lecture 5 :
    • Operators 3
    • Lecture 6 :
    • Operators 4
    • Lecture 7 :
    • Operators 5
  • Section 6 : DS -Vectors 7 Lectures 00:17:50

    • Lecture 1 :
    • DS -Vectors 1
    • Lecture 2 :
    • DS -Vectors 2
    • Lecture 3 :
    • DS -Vectors 3
    • Lecture 4 :
    • DS -Vectors 4
    • Lecture 5 :
    • DS -Vectors 5
    • Lecture 6 :
    • DS -Vectors 6
    • Lecture 7 :
    • DS -Vectors 7
  • Section 7 : DS - Lists 4 Lectures 00:16:44

    • Lecture 1 :
    • DS - Lists 1
    • Lecture 2 :
    • DS - Lists 2
    • Lecture 3 :
    • DS - Lists 3
    • Lecture 4 :
    • DS - Lists 4
  • Section 8 : DS - Matrices 5 Lectures 00:18:35

    • Lecture 1 :
    • DS - Matrices 1
    • Lecture 2 :
    • DS - Matrices 2
    • Lecture 3 :
    • DS - Matrices 3
    • Lecture 4 :
    • DS - Matrices 4
    • Lecture 5 :
    • DS - Matrices 5
  • Section 9 : DS - Arrays 5 Lectures 00:12:10

    • Lecture 1 :
    • DS - Arrays 1
    • Lecture 2 :
    • DS - Arrays 2
    • Lecture 3 :
    • DS - Arrays 3
    • Lecture 4 :
    • DS - Arrays 4
    • Lecture 5 :
    • DS - Arrays 5
  • Section 10 : DS - Data Frame 6 Lectures 00:18:17

    • Lecture 1 :
    • DS - Data Frame 1
    • Lecture 2 :
    • DS - Data Frame 2
    • Lecture 3 :
    • DS - Data Frame 3
    • Lecture 4 :
    • DS - Data Frame 4
    • Lecture 5 :
    • DS - Data Frame 5
    • Lecture 6 :
    • DS - Data Frame 6
  • Section 11 : DS - Factors 4 Lectures 00:10:28

    • Lecture 1 :
    • DS - Factors 1
    • Lecture 2 :
    • DS - Factors 2
    • Lecture 3 :
    • DS - Factors 3
    • Lecture 4 :
    • DS - Factors 5
  • Section 12 : Conditional Statements 4 Lectures 00:11:58

    • Lecture 1 :
    • Conditional Statements 1
    • Lecture 2 :
    • Conditional Statements 2
    • Lecture 3 :
    • Conditional Statements 3
    • Lecture 4 :
    • Conditional Statements 4
  • Section 13 : Repetition 7 Lectures 00:20:12

    • Lecture 1 :
    • While Loop 1
    • Lecture 2 :
    • While Loop 2
    • Lecture 3 :
    • While Loop 3
    • Lecture 4 :
    • For Loop 1
    • Lecture 5 :
    • For Loop 2
    • Lecture 6 :
    • For Loop 3
    • Lecture 7 :
    • For Loop 4
  • Section 14 : Functions 5 Lectures 00:18:23

    • Lecture 1 :
    • Functions 1
    • Lecture 2 :
    • Functions 2
    • Lecture 3 :
    • Functions 3
    • Lecture 4 :
    • Functions 4
    • Lecture 5 :
    • Functions 5
  • Section 15 : Statistics and Data Analysis 5 Lectures 00:20:19

    • Lecture 1 :
    • Statistics 1
    • Lecture 2 :
    • Statistics 2
    • Lecture 3 :
    • Statistics 3
    • Lecture 4 :
    • Statistics 4
    • Lecture 5 :
    • Statistics 5
  • Section 16 : Data Visualizations and R-Graphics. 15 Lectures 00:35:46

    • Lecture 1 :
    • Plotting in R 1
    • Lecture 2 :
    • Plotting in R 2
    • Lecture 3 :
    • Plotting in R 3
    • Lecture 4 :
    • Plotting in R 4
    • Lecture 5 :
    • Lines 1
    • Lecture 6 :
    • Lines 2
    • Lecture 7 :
    • Lines 3
    • Lecture 8 :
    • Pie Charts 1
    • Lecture 9 :
    • Pie Charts 2
    • Lecture 10 :
    • Pie Charts 3
    • Lecture 11 :
    • Bars and Histograms 1
    • Lecture 12 :
    • Bars and Histograms 2
    • Lecture 13 :
    • Bars and Histograms 3
    • Lecture 14 :
    • Bars and Histograms 4
    • Lecture 15 :
    • Bars and Histograms 5
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Ahmed Ibrahim is A Software Development Engineer | Data Science Professional | Training Content Engineer/Instructor I taught more than 700,000 students, developers and engineers from more than 190 countries around the world. - Programming Languages: Python, R, JavaScript, Java and Go. - Data Science: Data Analysis and Visualization tools and Libraries with Python, R, SQL and Spark. - Databases: Relational and Non-Relational. - Applied experience with many programming languages and tools, also a proficient knowledge and experience in Software Engineering and Data Science with skills to analyze, design and develop. - Bachelor's degree in Electrical, Communications and Computer Engineering. I always have a passion to develop my work and I like to simplify and clarify Software and Data Science skills and tools and share my skills and expertise with others via high-quality, and direct-to-point video training courses.
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