Data Science: R Programming For Data Science Complete Bootcamp

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• Module 0: Introduction 16:20

  • 1: Intro to course 04:03
  • 2: Downloading and Installing R 05:05
  • 3: Downloading and Installing the RStudio IDE 06:04
  • 4: Setup working directory 01:08

• Module 1: Variables, Data Types and Hints 23:56

  • 5: Variables in detail 1 03:00
  • 6: Variables in detail 2 03:57
  • 7: Variables in detail 3 03:21
  • 8: Variables in detail 4 02:45
  • 9: Data Types 1 02:44
  • 10: Data Types 2 03:17
  • 11: Data Types 3 02:41
  • 12: Code hints 02:11

• Module 2: Numbers and Math 24:58

  • 13: Type of numbers 08:12
  • 14: Type Conversion 07:36
  • 15: Math operations 09:10

• Module 3: Characters 16:41

  • 16: Strings 1 03:30
  • 17: Strings 2 02:49
  • 18: Strings 3 03:06
  • 19: Strings 4 01:37
  • 20: Strings 5 01:52
  • 21: Strings 6 01:13
  • 22: Strings 7 02:34

• Module 4: Logical and operators 18:56

  • 23: Logical values 1 01:19
  • 24: Logical values 2 04:11
  • 25: Operators 1 01:46
  • 26: Operators 2 03:34
  • 27: Operators 3 02:15
  • 28: Operators 4 02:23
  • 29: Operators 5 03:28

• Module 5: DS - Vectors 20:45

  • 30: Vectors 1 01:32
  • 31: Vectors 2 03:29
  • 32: Vectors 3 03:21
  • 33: Vectors 4 03:17
  • 34: Vectors 5 04:17
  • 35: Vectors 6 02:54
  • 36: Vectors 7 01:55

• Module 6: DS - Lists 16:45

  • 37: Lists 1 02:44
  • 38: Lists 2 04:27
  • 39: Lists 3 04:54
  • 40: Lists 4 04:40

• Module 7: DS - Matrices 18:37

  • 41: DS - Matrices 1 01:19
  • 42: DS - Matrices 2 03:27
  • 43: DS - Matrices 3 04:54
  • 44: DS - Matrices 4 06:54
  • 45: DS - Matrices 5 02:03

• Module 8: DS - Arrays 12:13

  • 46: DS - Arrays 1 00:50
  • 47: DS - Arrays 2 02:00
  • 48: DS - Arrays 3 01:56
  • 49: DS - Arrays 4 03:35
  • 50: DS - Arrays 5 03:52

• Module 9: DS - Data Frame 18:20

  • 51: DS - Data Frame 1 01:36
  • 52: DS - Data Frame 2 03:05
  • 53: DS - Data Frame 3 02:36
  • 54: DS - Data Frame 4 05:06
  • 55: DS - Data Frame 5 02:55
  • 56: DS - Data Frame 6 03:02

• Module 10: DS - Factors 12:54

  • 57: DS - Factors 1 01:16
  • 58: DS - Factors 2 05:33
  • 59: DS - Factors 3 02:24
  • 60: DS - Factors 4 03:41

• Module 11: Decision making 12:02

  • 61: Conditional statements 1 02:18
  • 62: Conditional statements 2 03:29
  • 63: Conditional statements 3 02:28
  • 64: Conditional statements 4 03:47

• Module 12: Repetition 20:17

  • 65: While loop 1 01:47
  • 66: While loop 2 03:47
  • 67: While loop 3 02:54
  • 68: For loop 1 01:22
  • 69: For loop 2 02:31
  • 70: For loop 3 05:37
  • 71: For loop 4 02:19

• Module 13: Functional Programming 18:25

  • 72: Functions 1 01:11
  • 73: Functions 2 03:08
  • 74: Functions 3 05:21
  • 75: Functions 4 02:58
  • 76: Functions 5 05:47

• Module 14: Statistics and Data analysis 20:21

  • 77: Statistics and data analysis 1 01:53
  • 78: Statistics and data analysis 2 08:27
  • 79: Statistics and data analysis 3 03:42
  • 80: Statistics and data analysis 4 03:42
  • 81: Statistics and data analysis 5 02:37

• Module 15: Data Visualization and Graphics 35:55

  • 82: Plotting in R 1 00:37
  • 83: Plotting in R 2 04:52
  • 84: Plotting in R 3 03:25
  • 85: Plotting in R 4 01:57
  • 86: Lines 1 00:44
  • 87: Lines 2 03:21
  • 88: Lines 3 03:15
  • 89: Pie Charts 1 00:32
  • 90: Pie Charts 2 02:57
  • 91: Pie Charts 3 05:03
  • 92: bars and histograms 1 01:11
  • 93: bars and histograms 2 02:05
  • 94: bars and histograms 3 01:34
  • 95: bars and histograms 4 02:00
  • 96: bars and histograms 5 02:22

This course will cover all the R fundamentals needed 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

• 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

• No programming experience needed. You'll learn everything you need to know.

• Desire to learn R programming.

1. Data Science Foundations
2. Math and Statistics
3. R Programming for Data Analysis and Data Visualization
4. Building Projects with R

• The average annual pay for a R Programmer is $90,940 a year.
• The average annual pay for a R Developer is $130,327 a year.
• The average annual pay for a Remote R Programmer is $90,665 a year.
• The average annual pay for a Data Analyst Programmer is $79,345 a year.