Section 1 : Course Introduction
Lecture 1 Introduction to Course 00:02:22 Duration
Lecture 2 Course Curriculum 00:02:03 Duration
Lecture 3 What is Data Science
Lecture 4 About Certification

Section 2 : Course Best Practices
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTO
Lecture 2 Installation and Set-Up

Section 3 : Windows Installation Set-Up
Lecture 1 Windows Installation Procedure 00:06:19 Duration

Section 4 : Mac OS Installation Set-Up
Lecture 1 Mac OS Installation Procedure 00:05:27 Duration

Section 5 : Linux Installation
Lecture 1 Linux Unbuntu Installation Procedure

Section 6 : Development Environment Overview
Lecture 1 Development Environment Overview 00:00:15 Duration
Lecture 2 Course Notes
Lecture 3 Guide to RStudio 00:12:34 Duration

Section 7 : Introduction to R Basics
Lecture 1 Introduction to R Basics 00:02:13 Duration
Lecture 2 Arithmetic in R 00:04:30 Duration
Lecture 3 Variables 00:05:26 Duration
Lecture 4 R Basic Data Types 00:05:31 Duration
Lecture 5 Vector Basics 00:07:35 Duration
Lecture 6 Vector Operations 00:04:23 Duration
Lecture 7 Comparison Operators 00:06:31 Duration
Lecture 8 Vector Indexing and Slicing 00:09:36 Duration
Lecture 9 Getting Help with R and RStudio 00:02:12 Duration
Lecture 10 R Basics Training Exercise 00:02:06 Duration
Lecture 11 R Basics Training Exercise - Solutions Walkthr 00:07:21 Duration

Section 8 : R Matrices
Lecture 1 Introduction to R Matrices 00:00:42 Duration
Lecture 2 Creating a Matrix 00:10:23 Duration
Lecture 3 Matrix Arithmetic 00:04:16 Duration
Lecture 4 Matrix Operations 00:05:22 Duration
Lecture 5 Matrix Selection and Indexing 00:06:34 Duration
Lecture 6 Factor and Categorical Matrices 00:08:14 Duration
Lecture 7 Matrix Training Exercise 00:00:53 Duration
Lecture 8 Matrix Training Exercises - Solutions Walkthro 00:13:10 Duration

Section 9 : R Data Frames
Lecture 1 Introduction to R Data Frames 00:00:36 Duration
Lecture 2 Data Frame Basics 00:08:44 Duration
Lecture 3 Data Frame Indexing and Selection 00:09:16 Duration
Lecture 4 Overview of Data Frame Operations - Part 1 00:15:58 Duration
Lecture 5 Overview of Data Frame Operations - Part 2 00:18:40 Duration
Lecture 6 Data Frame Training Exercise 00:01:00 Duration
Lecture 7 Data Frame Training Exercises - Solutions Walk 00:15:08 Duration

Section 10 : R Lists
Lecture 1 List Basics 2 00:08:11 Duration

Section 11 : Data Input and Output with R
Lecture 1 Introduction to Data Input and Output with R 00:00:17 Duration
Lecture 2 CSV Files with R 00:06:09 Duration
Lecture 3 Note on R with Excel Download
Lecture 4 Excel Files with R 00:11:44 Duration
Lecture 5 SQL with R 00:09:56 Duration
Lecture 6 Web Scraping with R 00:06:52 Duration

Section 12 : R Programming Basics
Lecture 1 Introduction to Programming Basics 00:00:52 Duration
Lecture 2 Logical Operators 00:08:06 Duration
Lecture 3 if, else, and else if Statements 00:15:00 Duration
Lecture 4 Conditional Statements Training Exercise
Lecture 5 Conditional Statements Training Exercise - 00:12:05 Duration
Lecture 6 While Loops 00:06:53 Duration
Lecture 7 For Loops 00:12:29 Duration
Lecture 8 Functions 00:19:15 Duration
Lecture 9 Functions Training Exercise 00:02:14 Duration
Lecture 10 Functions Training Exercise - Solutions 00:20:16 Duration

Section 13 : Advanced R Programming
Lecture 1 Introduction to Advanced R Programming 00:00:46 Duration
Lecture 2 Built-in R Features 00:09:49 Duration
Lecture 3 Apply 00:15:16 Duration
Lecture 4 Math Functions with R 00:03:22 Duration
Lecture 5 Regular Expressions 00:05:17 Duration
Lecture 6 Dates and Timestamps 00:12:07 Duration

Section 14 : Data Manipulation with R
Lecture 1 Data Manipulation Overview 00:00:40 Duration
Lecture 2 Guide to Using Dplyr 00:11:43 Duration
Lecture 3 Guide to Using Dplyr - Part 2 00:10:05 Duration
Lecture 4 Pipe Operator 00:06:20 Duration
Lecture 5 Quick note on Dpylr exercise
Lecture 6 Dplyr Training Exercise 00:01:09 Duration
Lecture 7 Dplyr Training Exercise - Solutions Walkthroug 00:06:47 Duration
Lecture 8 Guide to Using Tidyr 00:20:31 Duration

Section 15 : Data Visualization with R
Lecture 1 Overview of ggplot2
Lecture 2 Histograms 00:18:26 Duration
Lecture 3 Scatterplots 00:17:00 Duration
Lecture 4 Barplots 00:07:57 Duration
Lecture 5 Boxplots 00:07:02 Duration
Lecture 6 2 Variable Plotting 00:07:48 Duration
Lecture 7 Coordinates and Faceting 00:10:47 Duration
Lecture 8 Themes 00:05:23 Duration
Lecture 9 ggplot2 Exercises 00:02:29 Duration
Lecture 10 ggplot2 Exercise Solutions 00:12:51 Duration

Section 16 : Data Visualization Project
Lecture 1 Data Visualization Project 00:02:47 Duration
Lecture 2 Data Visualization Project - Solutions Walkthr 00:10:50 Duration
Lecture 3 Data Visualization Project Solutions Walkthrou 00:10:50 Duration

Section 17 : Interactive Visualizations with Plotly
Lecture 1 Overview of Plotly and Interactive Visualizati 00:08:50 Duration
Lecture 2 Resources for Plotly and ggplot2

Section 18 : Capstone Data Project
Lecture 1 Introduction to Capstone Project 00:07:47 Duration
Lecture 2 Capstone Project Solutions Walkthrough 00:22:00 Duration

Section 19 : Introduction to Machine Learning with R
Lecture 1 ISLR PDF
Lecture 2 Introduction to Machine Learning

Section 20 : Machine Learning with R - Linear Regression
Lecture 1 Introduction to Linear Regression 00:05:26 Duration
Lecture 2 Linear Regression with R - Part 1 00:19:40 Duration
Lecture 3 Linear Regression with R - Part 2 00:20:11 Duration
Lecture 4 Linear Regression with R - Part 3 00:11:54 Duration

Section 21 : Machine Learning Project - Linear Regression
Lecture 1 Introduction to Linear Regression Project 00:08:28 Duration
Lecture 2 ML - Linear Regression Project - Solutions Par 00:21:23 Duration
Lecture 3 ML - Linear Regression Project - Solutions Par 00:10:55 Duration

Section 22 : Machine Learning with R - Logistic Regression
Lecture 1 Introduction to Logistic Regression 00:11:37 Duration
Lecture 2 Logistic Regression with R - Part 1 00:20:00 Duration
Lecture 3 Logistic Regression with R - Part 2 00:18:41 Duration

Section 23 : Machine Learning Project - Logistic Regression
Lecture 1 Introduction to Logistic Regression Project 00:01:40 Duration
Lecture 2 Logistic Regression Project Solutions - Part 00:20:02 Duration
Lecture 3 Logistic Regression Project Solutions 00:15:04 Duration
Lecture 4 Logistic Regression Project - Solutions Part 3 00:13:09 Duration

Section 24 : Machine Learning with R - K Nearest Neighbors
Lecture 1 Introduction to K Nearest Neighbors 00:05:01 Duration
Lecture 2 K Nearest Neighbors with R 00:19:06 Duration

Section 25 : Machine Learning Project - K Nearest Neighbors
Lecture 1 Introduction K Nearest Neighbors Project 00:03:17 Duration
Lecture 2 K Nearest Neighbors Project Solutions 00:11:22 Duration

Section 26 : Machine Learning with R - Decision Trees and Random Forests
Lecture 1 Introduction to Tree Methods 00:06:30 Duration
Lecture 2 Decision Trees and Random Forests with R 00:12:02 Duration

Section 27 : Machine Learning Project - Decision Trees and Random Forests
Lecture 1 Introduction to Decision Trees and Random For 00:01:41 Duration
Lecture 2 Tree Methods Project Solutions - Part 1 00:16:42 Duration
Lecture 3 Tree Methods Project Solutions - Part 2 00:04:46 Duration

Section 28 : Machine Learning with R - Support Vector Machines
Lecture 1 Introduction to Support Vector Machines 00:04:13 Duration
Lecture 2 Support Vector Machines with R 00:14:50 Duration

Section 29 : Machine Learning Project - Support Vector Machines
Lecture 1 Introduction to SVM Project 00:02:14 Duration
Lecture 2 Support Vector Machines Project - Solutions P 00:11:04 Duration
Lecture 3 Support Vector Machines Project - Solutions 00:10:18 Duration

Section 30 : Machine Learning with R - K-means Clustering
Lecture 1 Introduction to K-Means Clustering 00:04:51 Duration
Lecture 2 K Means Clustering with R 00:09:33 Duration

Section 31 : Machine Learning Project - K-means Clustering
Lecture 1 Introduction to K Means Clustering Project 00:01:56 Duration
Lecture 2 K Means Clustering Project - Solutions Walkth 00:17:12 Duration

Section 32 : Machine Learning with R - Natural Language Processing
Lecture 1 Introduction to Natural Language Processing 00:04:25 Duration
Lecture 2 Natural Language Processing with R - Part 1 00:04:51 Duration
Lecture 3 Natural Language Processing with R - Part 2 00:15:56 Duration

Section 33 : Machine Learning with R - Neural Nets
Lecture 1 Introduction to Neural Nets 00:06:14 Duration
Lecture 2 Neural Nets with R 00:02:09 Duration

Section 34 : Machine Learning Project - Neural Nets
Lecture 1 Introduction to Neural Nets Project 00:02:09 Duration
Lecture 2 Neural Nets Project - Solutions 00:09:12 Duration