Section 1 : Course Introduction

 		lecture 1 Introduction to Course 2:22
lecture 2 Course Curriculum 2:3
lecture 3 What is Data Science
lecture 4 About Certification Pdf

Section 2 : Course Best Practices

 		lecture 5 INTRODUCTION TO BRAINMEASURES PROCTO Pdf
lecture 6 Installation and Set-Up Text

Section 3 : Windows Installation Set-Up

 		lecture 7 Windows Installation Procedure 6:19

Section 4 : Mac OS Installation Set-Up

 		lecture 8 Mac OS Installation Procedure 5:27

Section 5 : Linux Installation

 		lecture 9 Linux Unbuntu Installation Procedure Text

Section 6 : Development Environment Overview

 		lecture 10 Development Environment Overview 0:15
lecture 11 Course Notes
lecture 12 Guide to RStudio 12:34

Section 7 : Introduction to R Basics

 		lecture 13 Introduction to R Basics 2:13
lecture 14 Arithmetic in R 4:30
lecture 15 Variables 5:26
lecture 16 R Basic Data Types 5:31
lecture 17 Vector Basics 7:35
lecture 18 Vector Operations 4:23
lecture 19 Comparison Operators 6:31
lecture 20 Vector Indexing and Slicing 9:36
lecture 21 Getting Help with R and RStudio 2:12
lecture 22 R Basics Training Exercise 2:6
lecture 23 R Basics Training Exercise - Solutions Walkthr 7:21

Section 8 : R Matrices

 		lecture 24 Introduction to R Matrices 0:42
lecture 25 Creating a Matrix 10:23
lecture 26 Matrix Arithmetic 4:16
lecture 27 Matrix Operations 5:22
lecture 28 Matrix Selection and Indexing 6:34
lecture 29 Factor and Categorical Matrices 8:14
lecture 30 Matrix Training Exercise 0:53
lecture 31 Matrix Training Exercises - Solutions Walkthro 13:10

Section 9 : R Data Frames

 		lecture 32 Introduction to R Data Frames 0:36
lecture 33 Data Frame Basics 8:44
lecture 34 Data Frame Indexing and Selection 9:16
lecture 35 Overview of Data Frame Operations - Part 1 15:58
lecture 36 Overview of Data Frame Operations - Part 2 18:40
lecture 37 Data Frame Training Exercise 1:0
lecture 38 Data Frame Training Exercises - Solutions Walk 15:8

Section 10 : R Lists

 		lecture 39 List Basics 2 8:11

Section 11 : Data Input and Output with R

 		lecture 40 Introduction to Data Input and Output with R 0:17
lecture 41 CSV Files with R 6:9
lecture 42 Note on R with Excel Download Text
lecture 43 Excel Files with R 11:44
lecture 44 SQL with R 9:56
lecture 45 Web Scraping with R 6:52

Section 12 : R Programming Basics

 		lecture 46 Introduction to Programming Basics 0:52
lecture 47 Logical Operators 8:6
lecture 48 if, else, and else if Statements 15:0
lecture 49 Conditional Statements Training Exercise
lecture 50 Conditional Statements Training Exercise - 12:5
lecture 51 While Loops 6:53
lecture 52 For Loops 12:29
lecture 53 Functions 19:15
lecture 54 Functions Training Exercise 2:14
lecture 55 Functions Training Exercise - Solutions 20:16

Section 13 : Advanced R Programming

 		lecture 56 Introduction to Advanced R Programming 0:46
lecture 57 Built-in R Features 9:49
lecture 58 Apply 15:16
lecture 59 Math Functions with R 3:22
lecture 60 Regular Expressions 5:17
lecture 61 Dates and Timestamps 12:7

Section 14 : Data Manipulation with R

 		lecture 62 Data Manipulation Overview 0:40
lecture 63 Guide to Using Dplyr 11:43
lecture 64 Guide to Using Dplyr - Part 2 10:5
lecture 65 Pipe Operator 6:20
lecture 66 Quick note on Dpylr exercise Text
lecture 67 Dplyr Training Exercise 1:9
lecture 68 Dplyr Training Exercise - Solutions Walkthroug 6:47
lecture 69 Guide to Using Tidyr 20:31

Section 15 : Data Visualization with R

 		lecture 70 Overview of ggplot2
lecture 71 Histograms 18:26
lecture 72 Scatterplots 17:0
lecture 73 Barplots 7:57
lecture 74 Boxplots 7:2
lecture 75 2 Variable Plotting 7:48
lecture 76 Coordinates and Faceting 10:47
lecture 77 Themes 5:23
lecture 78 ggplot2 Exercises 2:29
lecture 79 ggplot2 Exercise Solutions 12:51

Section 16 : Data Visualization Project

 		lecture 80 Data Visualization Project 2:47
lecture 81 Data Visualization Project - Solutions Walkthr 10:50
lecture 82 Data Visualization Project Solutions Walkthrou 10:50

Section 17 : Interactive Visualizations with Plotly

 		lecture 83 Overview of Plotly and Interactive Visualizati 8:50
lecture 84 Resources for Plotly and ggplot2 Text

Section 18 : Capstone Data Project

 		lecture 85 Introduction to Capstone Project 7:47
lecture 86 Capstone Project Solutions Walkthrough 22:0

Section 19 : Introduction to Machine Learning with R

 		lecture 87 ISLR PDF Text
lecture 88 Introduction to Machine Learning

Section 20 : Machine Learning with R - Linear Regression

 		lecture 89 Introduction to Linear Regression 5:26
lecture 90 Linear Regression with R - Part 1 19:40
lecture 91 Linear Regression with R - Part 2 20:11
lecture 92 Linear Regression with R - Part 3 11:54

Section 21 : Machine Learning Project - Linear Regression

 		lecture 93 Introduction to Linear Regression Project 8:28
lecture 94 ML - Linear Regression Project - Solutions Par 21:23
lecture 95 ML - Linear Regression Project - Solutions Par 10:55

Section 22 : Machine Learning with R - Logistic Regression

 		lecture 96 Introduction to Logistic Regression 11:37
lecture 97 Logistic Regression with R - Part 1 20:0
lecture 98 Logistic Regression with R - Part 2 18:41

Section 23 : Machine Learning Project - Logistic Regression

 		lecture 99 Introduction to Logistic Regression Project 1:40
lecture 100 Logistic Regression Project Solutions - Part 20:2
lecture 101 Logistic Regression Project Solutions 15:4
lecture 102 Logistic Regression Project - Solutions Part 3 13:9

Section 24 : Machine Learning with R - K Nearest Neighbors

 		lecture 103 Introduction to K Nearest Neighbors 5:1
lecture 104 K Nearest Neighbors with R 19:6

Section 25 : Machine Learning Project - K Nearest Neighbors

 		lecture 105 Introduction K Nearest Neighbors Project 3:17
lecture 106 K Nearest Neighbors Project Solutions 11:22

Section 26 : Machine Learning with R - Decision Trees and Random Forests

 		lecture 107 Introduction to Tree Methods 6:30
lecture 108 Decision Trees and Random Forests with R 12:2

Section 27 : Machine Learning Project - Decision Trees and Random Forests

 		lecture 109 Introduction to Decision Trees and Random For 1:41
lecture 110 Tree Methods Project Solutions - Part 1 16:42
lecture 111 Tree Methods Project Solutions - Part 2 4:46

Section 28 : Machine Learning with R - Support Vector Machines

 		lecture 112 Introduction to Support Vector Machines 4:13
lecture 113 Support Vector Machines with R 14:50

Section 29 : Machine Learning Project - Support Vector Machines

 		lecture 114 Introduction to SVM Project 2:14
lecture 115 Support Vector Machines Project - Solutions P 11:4
lecture 116 Support Vector Machines Project - Solutions 10:18

Section 30 : Machine Learning with R - K-means Clustering

 		lecture 117 Introduction to K-Means Clustering 4:51
lecture 118 K Means Clustering with R 9:33

Section 31 : Machine Learning Project - K-means Clustering

 		lecture 119 Introduction to K Means Clustering Project 1:56
lecture 120 K Means Clustering Project - Solutions Walkth 17:12

Section 32 : Machine Learning with R - Natural Language Processing

 		lecture 121 Introduction to Natural Language Processing 4:25
lecture 122 Natural Language Processing with R - Part 1 4:51
lecture 123 Natural Language Processing with R - Part 2 15:56

Section 33 : Machine Learning with R - Neural Nets

 		lecture 124 Introduction to Neural Nets 6:14
lecture 125 Neural Nets with R 2:9

Section 34 : Machine Learning Project - Neural Nets

 		lecture 126 Introduction to Neural Nets Project 2:9
lecture 127 Neural Nets Project - Solutions 9:12