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