**Data Science and Machine Learning Bootcamp with R**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 17.5 Hours | 2.39 GB

Learn how to use the R programming language for data science and machine learning and data visualization!

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!

We’ll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning:

- Programming with R
- Advanced R Features
- Using R Data Frames to solve complex tasks
- Use R to handle Excel Files
- Web scraping with R
- Connect R to SQL
- Use ggplot2 for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with R, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Data Mining Twitter
- Neural Nets and Deep Learning
- Support Vectore Machines
- and much, much more!

What you’ll learn

- Program in R
- Use R for Data Analysis
- Create Data Visualizations
- Use R to handle csv, excel, SQL files or web scraping
- Use R to manipulate data easily
- Use R for Machine Learning Algorithms
- Use R for Data Science

**Table of Contents**

**Course Introduction**

1 Introduction to Course

2 Course Curriculum

3 What is Data Science

4 Course FAQ

**Course Best Practices**

5 How to Get Help in the Course!

6 Installation and Set-Up

**Windows Installation Set-Up**

7 Windows Installation Procedure

**Mac OS Installation Set-Up**

8 Mac OS Installation Procedure

**Linux Installation**

9 LinuxUnbuntu Installation Procedure

**Development Environment Overview**

10 Development Environment Overview

11 Course Notes

12 Guide to RStudio

**Introduction to R Basics**

13 Introduction to R Basics

14 R Basics Training Exercise

15 R Basics Training Exercise – Solutions Walkthrough

16 Arithmetic in R

17 Variables

18 R Basic Data Types

19 Vector Basics

20 Vector Operations

21 Comparison Operators

22 Vector Indexing and Slicing

23 Getting Help with R and RStudio

**R Matrices**

24 Introduction to R Matrices

25 Creating a Matrix

26 Matrix Arithmetic

27 Matrix Operations

28 Matrix Selection and Indexing

29 Factor and Categorical Matrices

30 Matrix Training Exercise

31 Matrix Training Exercises – Solutions Walkthrough

**R Data Frames**

32 Introduction to R Data Frames

33 Data Frame Basics

34 Data Frame Indexing and Selection

35 Overview of Data Frame Operations – Part 1

36 Overview of Data Frame Operations – Part 2

37 Data Frame Training Exercise

38 Data Frame Training Exercises – Solutions Walkthrough

**R Lists**

39 List Basics

**Data Input and Output with R**

40 Introduction to Data Input and Output with R

41 CSV Files with R

42 Excel Files with R

43 SQL with R

44 Web Scraping with R

**R Programming Basics**

45 Introduction to Programming Basics

46 Functions Training Exercise – Solutions

47 Logical Operators

48 if, else, and else if Statements

49 Conditional Statements Training Exercise

50 Conditional Statements Training Exercise – Solutions Walkthrough

51 While Loops

52 For Loops

53 Functions

54 Functions Training Exercise

**Advanced R Programming**

55 Introduction to Advanced R Programming

56 Built-in R Features

57 Apply

58 Math Functions with R

59 Regular Expressions

60 Dates and Timestamps

**Data Manipulation with R**

61 Data Manipulation Overview

62 Guide to Using Dplyr

63 Guide to Using Dplyr – Part 2

64 Pipe Operator

65 Quick note on Dpylr exercise

66 Dplyr Training Exercise

67 Dplyr Training Exercise – Solutions Walkthrough

68 Guide to Using Tidyr

**Data Visualization with R**

69 Overview of ggplot2

70 ggplot2 Exercise Solutions

71 Histograms

72 Scatterplots

73 Barplots

74 Boxplots

75 Variable Plotting

76 Coordinates and Faceting

77 Themes

78 ggplot2 Exercises

**Data Visualization Project**

79 Data Visualization Project

80 Data Visualization Project – Solutions Walkthrough – Part 1

81 Data Visualization Project Solutions Walkthrough – Part 2

**Interactive Visualizations with Plotly**

82 Overview of Plotly and Interactive Visualizations

83 Resources for Plotly and ggplot2

**Capstone Data Project**

84 Introduction to Capstone Project

85 Capstone Project Solutions Walkthrough

**Introduction to Machine Learning with R**

86 ISLR PDF

87 Introduction to Machine Learning

**Machine Learning with R – Linear Regression**

88 Introduction to Linear Regression

89 Linear Regression with R – Part 1

90 Linear Regression with R – Part 2

91 Linear Regression with R – Part 3

**Machine Learning Project – Linear Regression**

92 Introduction to Linear Regression Project

93 ML – Linear Regression Project – Solutions Part 1

94 ML – Linear Regression Project – Solutions Part 2

**Machine Learning with R – Logistic Regression**

95 Introduction to Logistic Regression

96 Logistic Regression with R – Part 1

97 Logistic Regression with R – Part 2

**Machine Learning Project – Logistic Regression**

98 Introduction to Logistic Regression Project

99 Logistic Regression Project Solutions – Part 1

100 Logistic Regression Project Solutions – Part 2

101 Logistic Regression Project – Solutions Part 3

**Machine Learning with R – K Nearest Neighbors**

102 Introduction to K Nearest Neighbors

103 K Nearest Neighbors with R

**Machine Learning Project – K Nearest Neighbors**

104 Introduction K Nearest Neighbors Project

105 K Nearest Neighbors Project Solutions

**Machine Learning with R – Decision Trees and Random Forests**

106 Introduction to Tree Methods

107 Decision Trees and Random Forests with R

**Machine Learning Project – Decision Trees and Random Forests**

108 Introduction to Decision Trees and Random Forests Project

109 Tree Methods Project Solutions – Part 1

110 Tree Methods Project Solutions – Part 2

**Machine Learning with R – Support Vector Machines**

111 Introduction to Support Vector Machines

112 Support Vector Machines with R

**Machine Learning Project – Support Vector Machines**

113 Introduction to SVM Project

114 Support Vector Machines Project – Solutions Part 1

115 Support Vector Machines Project – Solutions Part 2

**Machine Learning with R – K-means Clustering**

116 Introduction to K-Means Clustering

117 K Means Clustering with R

**Machine Learning Project – K-means Clustering**

118 Introduction to K Means Clustering Project

119 K Means Clustering Project – Solutions Walkthrough

**Machine Learning with R – Natural Language Processing**

120 Introduction to Natural Language Processing

121 Natural Language Processing with R – Part 1

122 Natural Language Processing with R – Part 2

**Machine Learning with R – Neural Nets**

123 Introduction to Neural Nets

124 Neural Nets with R

**Machine Learning Project – Neural Nets**

125 Introduction to Neural Nets Project

126 Neural Nets Project – Solutions

**Bonus Section – Discounts for Other Courses**

127 Bonus Lecture Coupons

Resolve the captcha to access the links!