Section 1 : Introduction

 		Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 About Our Case Studies 5:1
Lecture 3 Why Data is the new Oil 6:37
Lecture 4 Defining Business Problems for Analytic Thinking & Data Driven Decision making 5:40
Lecture 5 10 Data Science Projects every Business should do! 14:3
Lecture 6 How Deep Learning is Changing Everything 5:9
Lecture 7 The Career paths of a Data Scientist 4:51
Lecture 8 The Data Science Approach to Problems 7:57

Section 2 : Setup (Google Colab) & Download Code

 		Lecture 9 Downloading and Running Your Code 2:18
Lecture 10 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 3 : Introduction to Python

 		Lecture 11 Why use Python for Data Science 3:5
Lecture 12 Python Introduction - Part 1 - Variables 6:31
Lecture 13 Python - Variables (Lists and Dictionaries) 11:9
Lecture 14 Python - Conditional Statements 6:55
Lecture 15 More information on elif Text
Lecture 16 Python - Loops 8:49
Lecture 17 Python - Functions 5:29
Lecture 18 Python - Classes 8:35

Section 4 : Pandas

 		Lecture 19 Pandas Introduction 2:48
Lecture 20 Pandas 1 - Data Series 6:17
Lecture 21 Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty cells 17:31
Lecture 22 Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering 5:51
Lecture 23 Pandas 3A - Data Cleaning - Alter ColomnsRows, Missing Data & String Operations 8:46
Lecture 24 Pandas 3B - Data Cleaning - Alter ColomnsRows, Missing Data & String Operations 19:46
Lecture 25 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 26 Pandas 5 - Feature Engineer, Lambda and Apply 4:24
Lecture 27 Pandas 6 - Concatenating, Merging and Joinining 15:21
Lecture 28 Pandas 7 - Time Series Data 9:56
Lecture 29 Pandas 8 - ADVANCED Operations - Iterows, Vectorization and Numpy 11:17
Lecture 30 Pandas 9 - ADVANCED Operations - Iterows, Vectorization and Numpy 4:29
Lecture 31 Pandas 10 - ADVANCED Operations - Parallel Processing 4:24
Lecture 32 Map Visualizations with Plotly - Cloropeths from Scratch - USA and World 11:25
Lecture 33 Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines 4:55

Section 5 : Statistics & Visualizations

 		Lecture 34 Introduction to Statistics 4:20
Lecture 35 Descriptive Statistics - Why Statistical Knowledge is so Important 3:47
Lecture 36 Descriptive Statistics 1 - Exploratory Data Analysis (EDA) & Visualizations 16:20
Lecture 37 Descriptive Statistics 2 - Exploratory Data Analysis (EDA) & Visualizations 6:23
Lecture 38 Sampling, Averages & Variance And How to lie and Mislead with Statistics 3:16
Lecture 39 Sampling - Sample Sizes & Confidence Intervals - What Can You Trust 9:41
Lecture 40 Types of Variables - Quantitive and Qualitative 5:41
Lecture 41 Frequency Distributions 4:41
Lecture 42 Frequency Distributions Shapes 2:56
Lecture 43 Analyzing Frequency Distributions - What is the Best Type of WIne Red or White 9:59
Lecture 44 Mean, Mode and Median - Not as Simple As You'd Think 12:38
Lecture 45 Variance, Standard Deviation and Bessel’s Correction 9:22
Lecture 46 Covariance & Correlation - Do Amazon & Google know you better than anyone else 11:36
Lecture 47 Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption 1:38
Lecture 48 The Normal Distribution & the Central Limit Theorem 3:43
Lecture 49 Z-Scores 8:16

Section 6 : Probability Theory

 		Lecture 50 Introduction to Probability 1:41
Lecture 51 Estimating Probability 5:14
Lecture 52 Addition Rule 7:58
Lecture 53 Bayes Theorem 7:50

Section 7 : Hypothesis Testing

 		Lecture 54 Introduction to Hypothesis Testing 3:3
Lecture 55 Statistical Significance 8:13
Lecture 56 Hypothesis Testing – P Value 7:44
Lecture 57 Hypothesis Testing – Pearson Correlation 5:26

Section 8 : AB Testing - A Worked Example

 		Lecture 58 Understanding the Problem + Exploratory Data Analysis and Visualizations 10:50
Lecture 59 AB Test Result Analysis 5:37
Lecture 60 AB Testing a Worked Real Life Example - Designing an AB Test 8:17
Lecture 61 Statistical Power and Significance 6:58
Lecture 62 Analysis of AB Test Resutls 8:16

Section 9 : Data Dashboards - Google Data Studio

 		Lecture 63 Intro to Google Data Studio 5:6
Lecture 64 Opening Google Data Studio and Uploading Data 4:27
Lecture 65 Your First Dashboard Part 1 14:29
Lecture 66 Your First Dashboard Part 2 10:2
Lecture 67 Creating New Fields 5:37
Lecture 68 Adding Filters to Tables
Lecture 69 Scorecard KPI Visalizations 6:10
Lecture 70 Scorecards with Time Comparison 5:44
Lecture 71 Bar Charts (Horizontal, Vertical & Stacked) 8:45
Lecture 72 Line Charts 7:1
Lecture 73 Pie Charts, Donut Charts and Tree Maps 4:52
Lecture 74 Time Series and Comparitive Time Series Plots 4:1
Lecture 75 Scatter Plots 4:50
Lecture 76 Geographic Plots 7:21
Lecture 77 Bullet and Line Area Plots 5:32
Lecture 78 Sharing and Final Conclusions 6:57
Lecture 79 Our Executive Sales Dashboard 2:19

Section 10 : Machine Learning

 		Lecture 80 Introduction to Machine Learning 3:33
Lecture 81 How Machine Learning enables Computers to Learn 3:25
Lecture 82 What is a Machine Learning Model 6:21
Lecture 83 Types of Machine Learning 7:41
Lecture 84 Linear Regression – Introduction to Cost Functions and Gradient Descent 9:11
Lecture 85 Linear Regressions in Python from Scratch and using Sklearn 14:18
Lecture 86 Polynomial and Multivariate Linear Regression 8:30
Lecture 87 Logistic Regression 11:40
Lecture 88 Support Vector Machines (SVMs) 5:36
Lecture 89 Decision Trees and Random Forests & the Gini Index 10:45
Lecture 90 K-Nearest Neighbors (KNN) 5:44
Lecture 91 Assessing Performance – Confusion Matrix, Precision and Recall 22:37
Lecture 92 Understanding the ROC and AUC Curve 6:39
Lecture 93 What Makes a Good Model Regularization, Overfitting, Generalization & Outliers 16:16
Lecture 94 Introduction to Neural Networks 2:7
Lecture 95 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs 7:38

Section 11 : Deep Learning

 		Lecture 96 Neural Networks Chapter Overview 1:35
Lecture 97 Machine Learning Overview 8:26
Lecture 98 Neural Networks Explained 3:51
Lecture 99 Forward Propagation
Lecture 100 Activation Functions 8:31
Lecture 101 Training Part 1 – Loss Functions 9:13
Lecture 102 Training Part 2 – Backpropagation and Gradient Descent 9:57
Lecture 103 Backpropagation & Learning Rates – A Worked Example 13:36
Lecture 104 Regularization, Overfitting, Generalization and Test Datasets 15:25
Lecture 105 Epochs, Iterations and Batch Sizes
Lecture 106 Measuring Performance and the Confusion Matrix 7:7
Lecture 107 Review and Best Practices 4:16

Section 12 : Unsupervised Learning - Clustering

 		Lecture 108 Introduction to Unsupervised Learning 4:56
Lecture 109 K-Means Clustering 9:34
Lecture 110 Choosing K – Elbow Method & Silhouette Analysis 7:51
Lecture 111 K-Means in Python - Choosing K using the Elbow Method & Silhoutte Analysis 7:51
Lecture 112 Agglomerative Hierarchical Clustering 4:53
Lecture 113 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 114 DBSCAN (Density-Based Spatial Clustering of Applications with Noise) 4:36
Lecture 115 DBSCAN in Python 2:31
Lecture 116 Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) 5:56

Section 13 : Dimensionality Reduction

 		Lecture 117 Principal Component Analysis 6:57
Lecture 118 t-Distributed Stochastic Neighbor Embedding (t-SNE) 5:38
Lecture 119 PCA & t-SNE in Python with Visualization Comparisons 5:22

Section 14 : Recommendation Systems

 		Lecture 120 Introduction to Recommendation Engines 5:4
Lecture 121 Before recommending, how do we rate or review Items 6:1
Lecture 122 User Collaborative Filtering and ItemContent-based Filtering 9:58
Lecture 123 The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me 8:20

Section 15 : Natural Language Processing

 		Lecture 124 Introduction to Natural Language Processing 3:21
Lecture 125 Modeling Language – The Bag of Words Model 4:15
Lecture 126 Normalization, Stop Word Removal, LemmatizingStemming 4:55
Lecture 127 TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency) 1:49
Lecture 128 Word2Vec - Efficient Estimation of Word Representations in Vector Space 5:48

Section 16 : Big Data

 		Lecture 129 Introduction to Big Data 6:53
Lecture 130 Challenges in Big Data 4:52
Lecture 131 Hadoop, MapReduce and Spark 7:3
Lecture 132 Introduction to PySpark 2:11
Lecture 133 RDDs, Transformations, Actions, Lineage Graphs & Jobs 7:3

Section 17 : Predicting the US 2020 Election

 		Lecture 134 Understanding Polling Data 7:52
Lecture 135 Cleaning & Exploring our Dataset 5:56
Lecture 136 Data Wrangling our Dataset 4:55
Lecture 137 Understanding the US Electoral System 6:20
Lecture 138 Visualizing our Polling Data 6:20
Lecture 139 Statistical Analysis of Polling Data 3:43
Lecture 140 Polling Simulations 10:42
Lecture 141 Polling Simulation Result Analysis 7:19
Lecture 142 Visualizing our results on a US Map 4:32

Section 18 : Predicting Diabetes Cases

 		Lecture 143 Understanding and Preparing Our Healthcare Data 8:12
Lecture 144 First Attempt - Trying a Naive Model 3:53
Lecture 145 Trying Different Models and Comparing the Results 6:29

Section 19 : Market Basket Analysis

 		Lecture 146 Understanding our Dataset 6:51
Lecture 147 Data Preparation 5:22
Lecture 148 Visualizing Our Frequent Sets 10:22

Section 20 : Predicting the World Cup Winner (SoccerFootball)

 		Lecture 149 Understanding and Preparing Our Soccer Dataset
Lecture 150 Understanding and Preparing Our Soccer Datase 5:52
Lecture 151 Predicting Game Outcomes with our Model 6:20
Lecture 152 Simulating the World Cup Outcome with Our Mode 13:30

Section 21 : Covid-19 Data Analysis and Flourish Bar Chart Race Visualization

 		Lecture 153 Understanding Our Covid-19 Data 7:31
Lecture 154 Analysis of the most Recent Data 5:46
Lecture 155 World Visualizations 8:55
Lecture 156 Analyzing Confirmed Cases in each Country 4:29
Lecture 157 Mapping Covid-19 Cases 5:56
Lecture 158 Animating our Maps 4:48
Lecture 159 Comparing Countries and Continents 4:50
Lecture 160 Flourish Bar Chart Race - 1 10:59
Lecture 161 Flourish Bar Chart Race - 2 9:53

Section 22 : Analyzing Olmypic Winners

 		Lecture 162 Understanding our Olympic Datasets 9:40
Lecture 163 Getting The Medals Per Country 9:30
Lecture 164 Analyzing the Winter Olympic Data and Viewing Medals Won Over Time 5:42

Section 23 : Is Home Advantage Real in Soccer and Basketball

 		Lecture 165 Understanding Our Dataset and EDA 9:28
Lecture 166 Goal Difference Ratios Home versus Away 4:26
Lecture 167 How Home Advantage Has Evolved Over 5:9

Section 24 : IPL Cricket Data Analysis

 		Lecture 168 Loading and Understanding our Cricket Datasets 7:25
Lecture 169 Man of Match and Stadium Analysis 5:50
Lecture 170 Do Toss Winners Win More And Team vs Team Comparisons 7:8

Section 25 : Streaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) - Movie Analysi

 		Lecture 171 Understanding our Dataset 6:36
Lecture 172 EDA and Visualizations 8:45
Lecture 173 Best Movies Per Genre Platform Comparisons 12:40

Section 26 : Micro Brewery and Pub Data Analysis

 		Lecture 174 EDA, Visualizations and Map 13:41

Section 27 : Pizza Resturant Data Analysis

 		Lecture 175 EDA and Visualizations 6:48
Lecture 176 Analysis Per State 4:46
Lecture 177 Pizza Maps 7:24

Section 28 : Supply Chain Data Analysis

 		Lecture 178 Understanding our Dataset 4:4
Lecture 179 Visualizations and EDA 9:21
Lecture 180 More Visualizations 4:32

Section 29 : Indian Election Result Analysis

 		Lecture 181 Intro 7:33
Lecture 182 Visualizations of Election Results 9:29
Lecture 183 Visualizing Gender Turnout 10:59

Section 30 : Africa Economic Crisis Data Analysis

 		Lecture 184 Economic Dataset Understanding 5:15
Lecture 185 Visualizations and Correlations 7:43

Section 31 : Predicting Which Employees May Quit

 		Lecture 186 Figuring Out Which Employees May Quit –Understanding the Problem & EDA 7:6
Lecture 187 Data Cleaning and Preparation 7:27
Lecture 188 Machine Learning Modeling + Deep Learning 17:44

Section 32 : Figuring Out Which Customers May Leave

 		Lecture 189 Understanding the Problem 3:39
Lecture 190 Exploratory Data Analysis & Visualizations 6:50
Lecture 191 Data Preprocessing 5:57
Lecture 192 Machine Learning Modeling + Deep Learning 14:52

Section 33 : Who to Target For Donations

 		Lecture 193 Understanding the Problem 3:31
Lecture 194 Exploratory Data Analysis & Visualizations 7:7
Lecture 195 Preparing our Dataset for Machine Learning 13:18
Lecture 196 Modeling using Grid Search for finding the best parameters 5:4

Section 34 : Predicting Insurance Premiums

 		Lecture 197 Understanding the Problem + Exploratory Data Analysis and Visualizations 10:11
Lecture 198 Data Preparation and Machine Learning Modeling 12:44

Section 35 : Predicting Airbnb Prices

 		Lecture 199 Understanding the Problem + Exploratory Data Analysis 19:53
Lecture 200 Machine Learning Modeling 18:7
Lecture 201 Using our Model for Value Estimation for New Clients 4:3

Section 36 : Detecting Credit Card Fraud

 		Lecture 202 Understanding our Dataset
Lecture 203 Exploratory Analysis 4:20
Lecture 204 Feature Extraction 6:20
Lecture 205 Creating and Validating Our Model 10:43

Section 37 : Analyzing Conversion Rates in Marketing Campaigns

 		Lecture 206 Exploratory Analysis of Understanding Marketing Conversion Rates 17:51

Section 38 : Predicting Advertising Engagement

 		Lecture 207 Understanding the Problem + Exploratory Data Analysis and Visualizations 8:42
Lecture 208 Data Preparation and Machine Learning Modeling 8:32

Section 39 : Product Sales Analysis

 		Lecture 209 Problem and Plan of Attack 8:44
Lecture 210 Sales and Revenue Analysis 10:34
Lecture 211 Analysis per Country, Repeat Customers and Items 11:49

Section 40 : Determing Your Most Valuable Customers

 		Lecture 212 Understanding the Problem + Exploratory Data Analysis and Visualizations 7:6
Lecture 213 Customer Lifetime Value Modeling 6:42

Section 41 : Customer Clustering (K-means, Hierarchial) - Train Passenger

 		Lecture 214 Data Exploration & Description 6:48
Lecture 215 Simple Exploratory Data Analysis and Visualizations 9:46
Lecture 216 Feature Engineering 8:35
Lecture 217 K-Means Clustering of Customer 13:5
Lecture 218 Cluster Analysis 4:15

Section 42 : Build a Product Recommendation System

 		Lecture 219 Dataset Description and Data Cleaning 4:34
Lecture 220 Making a Customer-Item Matrix 4:14
Lecture 221 User-User Matrix - Getting Recommended Items 15:18
Lecture 222 Item-Item Collaborative Filtering - Finding the Most Similar Items 6:31

Section 43 : Movie Recommendation System - LiteFM

 		Lecture 223 Intro Text

Section 44 : Deep Learning Recommendation System

 		Lecture 224 Understanding Our Wikipedia Movie Dataset 5:40
Lecture 225 Creating Our Dataset 5:55
Lecture 226 Deep Learning Embeddings and Training 4:8
Lecture 227 Getting Recommendations based on Movie Similarity 4:20

Section 45 : Predicting Brent Oil Prices

 		Lecture 228 Understanding our Dataset and it's Time Series Nature 5:9
Lecture 229 Creating our Prediction Model 5:8
Lecture 230 Making Future Predictions 12:56

Section 46 : Stock Trading using Reinforcement Learning

 		Lecture 231 Introduction to Reinforcement Learning Text
Lecture 232 Using Q-Learning and Reinforcement Learning to Build a Trading Bot Text

Section 47 : SalesDemand Forecasting

 		Lecture 233 Problem and Plan of Attack Text

Section 48 : Detecting Sentiment in Tweets

 		Lecture 234 Understanding our Dataset and Word Clouds 7:1
Lecture 235 Visualizations and Feature Extraction 10:17
Lecture 236 Training our Model 6:31

Section 49 : Spam or Ham Detection

 		Lecture 237 Loading and Understanding our SpamHam Dataset 6:53
Lecture 238 Training our Spam Detector 7:46

Section 50 : Explore Data with PySpark and Titanic Surival Prediction

 		Lecture 239 Exploratory Analysis of our Titantic Dataset 13:35
Lecture 240 Transformation Operations 6:48
Lecture 241 Machine Learning with PySpark 5:0

Section 51 : Newspaper Headline Classification using PySpark

 		Lecture 242 Loading and Understanding our Dataset 3:56
Lecture 243 Building our Model with PySpark 4:59

Section 52 : Deployment into Production

 		Lecture 244 Introduction to Production Deployment Systems 6:0
Lecture 245 Creating the Model 4:49
Lecture 246 Introduction to Flask 4:41
Lecture 247 About our WebApp 6:8
Lecture 248 Deploying our WebApp on Heroku 13:16