Section 1 : Introduction

Lecture 1 The Data Science Hype copy 11:19
Lecture 2 About Our Case Studies 5:33
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 6:39
Lecture 10 Colab Setup Text

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 More information on elif Text
Lecture 15 Python - Conditional Statements 6:55
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 Pandas 5 - Feature Engineer, Lambda and Apply 4:24
Lecture 26 Pandas 6 - Concatenating, Merging and Joinining 15:21
Lecture 27 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 28 Pandas 8 - ADVANCED Operations - Iterows, Vectorization and Numpy 11:17
Lecture 29 Pandas 9 - ADVANCED Operations - Iterows, Vectorization and Numpy 4:29
Lecture 30 Pandas 10 - ADVANCED Operations - Parallel Processing 4:24
Lecture 31 Map Visualizations with Plotly - Cloropeths from Scratch - USA and World 11:25
Lecture 32 Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines 4:55

Section 5 : Statistics & Visualizations

Lecture 33 Introduction to Statistics 4:20
Lecture 34 Descriptive Statistics - Why Statistical Knowledge is so Important 3:47
Lecture 35 Descriptive Statistics 1 - Exploratory Data Analysis (EDA) & Visualizations 16:20
Lecture 36 Descriptive Statistics 2 - Exploratory Data Analysis (EDA) & Visualizations
Lecture 37 Sampling, Averages & Variance And How to lie and Mislead with Statistics 3:16
Lecture 38 Sampling - Sample Sizes & Confidence Intervals - What Can You Trust 9:41
Lecture 39 Types of Variables - Quantitive and Qualitative 5:41
Lecture 40 Frequency Distributions 4:41
Lecture 41 Frequency Distributions Shapes 2:56
Lecture 42 Analyzing Frequency Distributions - What is the Best Type of WIne Red or White 9:59
Lecture 43 Mean, Mode and Median - Not as Simple As You'd Think 12:38
Lecture 44 Variance, Standard Deviation and Bessel’s Correction 9:22
Lecture 45 Covariance & Correlation - Do Amazon & Google know you better than anyone else 11:36
Lecture 46 Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption 1:38
Lecture 47 The Normal Distribution & the Central Limit Theorem 3:43
Lecture 48 Z-Scores 8:16

Section 6 : Probability Theory

Lecture 49 Introduction to Probability 1:41
Lecture 50 Estimating Probability 5:14
Lecture 51 Addition Rule 7:58
Lecture 52 Bayes Theorem 7:50

Section 7 : Hypothesis Testing

Lecture 53 Introduction to Hypothesis Testing 3:3
Lecture 54 Statistical Significance 8:13
Lecture 55 Hypothesis Testing – P Value 7:44
Lecture 56 Hypothesis Testing – Pearson Correlation 5:26

Section 8 : AB Testing - A Worked Example

Lecture 57 Understanding the Problem + Exploratory Data Analysis and Visualizations 10:50
Lecture 58 AB Test Result Analysis 5:37
Lecture 59 AB Testing a Worked Real Life Example - Designing an AB Test 8:17
Lecture 60 Statistical Power and Significance 6:58
Lecture 61 Analysis of AB Test Resutls 8:16

Section 9 : Data Dashboards - Google Data Studio

Lecture 62 Intro to Google Data Studio 5:6
Lecture 63 Opening Google Data Studio and Uploading Data 4:27
Lecture 64 Your First Dashboard Part 1 14:29
Lecture 65 Your First Dashboard Part 2 10:2
Lecture 66 Creating New Fields 5:37
Lecture 67 Adding Filters to Tables 2:53
Lecture 68 Scorecard KPI Visalizations 6:10
Lecture 69 Scorecards with Time Comparison 5:44
Lecture 70 Bar Charts (Horizontal, Vertical & Stacked) 8:45
Lecture 71 Line Charts 7:1
Lecture 72 Pie Charts, Donut Charts and Tree Maps 4:52
Lecture 73 Time Series and Comparitive Time Series Plots 4:1
Lecture 74 Scatter Plots
Lecture 75 Geographic Plots 7:21
Lecture 76 Bullet and Line Area Plots 5:32
Lecture 77 Sharing and Final Conclusions 6:57
Lecture 78 Our Executive Sales Dashboard 2:19

Section 10 : Machine Learning

Lecture 79 Introduction to Machine Learning 3:33
Lecture 80 How Machine Learning enables Computers to Learn 3:25
Lecture 81 What is a Machine Learning Model 6:21
Lecture 82 Types of Machine Learning 7:41
Lecture 83 Linear Regression – Introduction to Cost Functions and Gradient Descent 9:11
Lecture 84 Linear Regressions in Python from Scratch and using Sklearn
Lecture 85 Polynomial and Multivariate Linear Regression 8:30
Lecture 86 Logistic Regression 11:40
Lecture 87 Support Vector Machines (SVMs) 5:36
Lecture 88 Decision Trees and Random Forests & the Gini Index 10:45
Lecture 89 K-Nearest Neighbors (KNN) 5:44
Lecture 90 Assessing Performance – Confusion Matrix, Precision and Recall 22:37
Lecture 91 Understanding the ROC and AUC Curve 6:39
Lecture 92 What Makes a Good Model Regularization, Overfitting, Generalization & Outliers 16:16
Lecture 93 Introduction to Neural Networks 2:7
Lecture 94 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs 7:38

Section 11 : Deep Learning

Lecture 95 Neural Networks Chapter Overview 1:35
Lecture 96 Machine Learning Overview 8:26
Lecture 97 Neural Networks Explained 3:51
Lecture 98 Forward Propagation 8:34
Lecture 99 Activation Functions 8:31
Lecture 100 Training Part 1 – Loss Functions 9:13
Lecture 101 Training Part 2 – Backpropagation and Gradient Descent 9:57
Lecture 102 Backpropagation & Learning Rates – A Worked Example 13:36
Lecture 103 Regularization, Overfitting, Generalization and Test Datasets 15:25
Lecture 104 Epochs, Iterations and Batch Sizes 3:38
Lecture 105 Measuring Performance and the Confusion Matrix 7:7
Lecture 106 Review and Best Practices 4:16

Section 12 : Unsupervised Learning - Clustering

Lecture 107 Introduction to Unsupervised Learning 4:56
Lecture 108 K-Means Clustering 9:34
Lecture 109 Choosing K – Elbow Method & Silhouette Analysis 7:51
Lecture 110 Agglomerative Hierarchical Clustering 4:53
Lecture 111 DBSCAN (Density-Based Spatial Clustering of Applications with Noise) 4:36
Lecture 112 DBSCAN in Python 2:31
Lecture 113 Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) 5:56

Section 13 : Dimensionality Reduction

Lecture 114 Principal Component Analysis 6:57
Lecture 115 t-Distributed Stochastic Neighbor Embedding (t-SNE) 5:38
Lecture 116 PCA & t-SNE in Python with Visualization Comparisons 5:22

Section 14 : Recommendation Systems

Lecture 117 Introduction to Recommendation Engines 5:4
Lecture 118 Before recommending, how do we rate or review Items 6:1
Lecture 119 User Collaborative Filtering and ItemContent-based Filtering 9:58
Lecture 120 The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me 8:20

Section 15 : Natural Language Processing

Lecture 121 Introduction to Natural Language Processing
Lecture 122 Modeling Language – The Bag of Words Model 4:15
Lecture 123 Normalization, Stop Word Removal, LemmatizingStemming 4:55
Lecture 124 TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency) 1:49
Lecture 125 Word2Vec - Efficient Estimation of Word Representations in Vector Space 5:48

Section 16 : Big Data

Lecture 126 Introduction to Big Data 6:53
Lecture 127 Challenges in Big Data 4:52
Lecture 128 Hadoop, MapReduce and Spark 7:3
Lecture 129 Introduction to PySpark 2:11
Lecture 130 RDDs, Transformations, Actions, Lineage Graphs & Jobs 7:3

Section 17 : Predicting the US 2020 Election

Lecture 131 Understanding Polling Data 7:52
Lecture 132 Cleaning & Exploring our Dataset 5:56
Lecture 133 Data Wrangling our Dataset 4:55
Lecture 134 Understanding the US Electoral System 6:20
Lecture 135 Visualizing our Polling Data 4:6
Lecture 136 Statistical Analysis of Polling Data 3:43
Lecture 137 Polling Simulations 10:42
Lecture 138 Polling Simulation Result Analysis 7:19
Lecture 139 Visualizing our results on a US Map 4:32

Section 18 : Predicting Diabetes Cases

Lecture 140 Understanding and Preparing Our Healthcare Data 8:12
Lecture 141 First Attempt - Trying a Naive Model 3:53
Lecture 142 Trying Different Models and Comparing the Results 6:29

Section 19 : Market Basket Analysis

Lecture 143 Understanding our Dataset 6:51
Lecture 144 Data Preparation 5:22
Lecture 145 Visualizing Our Frequent Sets 10:22

Section 20 : Predicting the World Cup Winner (SoccerFootball)

Lecture 146 Understanding and Preparing Our Soccer Datasets 6:5
Lecture 147 Understanding and Preparing Our Soccer Datasets 5:52
Lecture 148 Predicting Game Outcomes with our Model 6:20
Lecture 149 Simulating the World Cup Outcome with Our Model 13:30

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

Lecture 150 Understanding Our Covid-19 Data 7:31
Lecture 151 Analysis of the most Recent Data 5:46
Lecture 152 World Visualizations 8:55
Lecture 153 Analyzing Confirmed Cases in each Country 4:29
Lecture 154 Mapping Covid-19 Cases 5:56
Lecture 155 Animating our Maps 4:48
Lecture 156 Comparing Countries and Continents
Lecture 157 Flourish Bar Chart Race - 1 10:59
Lecture 158 Flourish Bar Chart Race - 2 9:53

Section 22 : Analyzing Olmypic Winners

Lecture 159 Understanding our Olympic Datasets 9:40
Lecture 160 Getting The Medals Per Country 9:40
Lecture 161 Analyzing the Winter Olympic Data and Viewing Medals Won Over Time 5:42

Section 23 : Is Home Advantage Real in Soccer and Basketball

Lecture 162 Understanding Our Dataset and EDA 9:28
Lecture 163 Goal Difference Ratios Home versus Away 4:26
Lecture 164 How Home Advantage Has Evolved Over 5:9

Section 24 : IPL Cricket Data Analysis

Lecture 165 Loading and Understanding our Cricket Datasets 7:25
Lecture 166 Man of Match and Stadium Analysis 5:50
Lecture 167 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 168 Understanding our Dataset 6:36
Lecture 169 EDA and Visualizations 8:45
Lecture 170 Best Movies Per Genre Platform Comparisons 12:40

Section 26 : Micro Brewery and Pub Data Analysis

Lecture 171 EDA, Visualizations and Map 13:41

Section 27 : Pizza Resturant Data Analysis

Lecture 172 EDA and Visualizations 6:48
Lecture 173 Analysis Per State 4:46
Lecture 174 Pizza Maps 7:24

Section 28 : Supply Chain Data Analysis

Lecture 175 Understanding our Dataset 4:4
Lecture 176 Visualizations and EDA 9:21
Lecture 177 More Visualizations 4:32

Section 29 : Indian Election Result Analysis

Lecture 178 Intro 7:33
Lecture 179 Visualizations of Election Results 9:29
Lecture 180 Visualizing Gender Turnout 10:59

Section 30 : Africa Economic Crisis Data Analysis

Lecture 181 Economic Dataset Understanding 5:15
Lecture 182 Visualizations and Correlations 7:43

Section 31 : Predicting Which Employees May Quit

Lecture 183 Figuring Out Which Employees May Quit –Understanding the Problem & EDA 7:6
Lecture 184 Data Cleaning and Preparation 7:27
Lecture 185 Machine Learning Modeling + Deep Learning 17:44

Section 32 : Figuring Out Which Customers May Leave

Lecture 186 Understanding the Problem 3:39
Lecture 187 Exploratory Data Analysis & Visualizations 6:50
Lecture 188 Data Preprocessing 5:57
Lecture 189 Machine Learning Modeling + Deep Learning 14:52

Section 33 : Who to Target For Donations

Lecture 190 Understanding the Problem 3:31
Lecture 191 Exploratory Data Analysis & Visualizations 7:7
Lecture 192 Preparing our Dataset for Machine Learning 13:18
Lecture 193 Modeling using Grid Search for finding the best parameters 5:4

Section 34 : Predicting Insurance Premiums

Lecture 194 Understanding the Problem + Exploratory Data Analysis and Visualizations 10:11
Lecture 195 Data Preparation and Machine Learning Modeling 12:44

Section 35 : Predicting Airbnb Prices

Lecture 196 Understanding the Problem + Exploratory Data Analysis and Visualizations 19:53
Lecture 197 Machine Learning Modeling 18:7
Lecture 198 Using our Model for Value Estimation for New Clients 4:3

Section 36 : Detecting Credit Card Fraud

Lecture 199 Understanding our Dataset 6:6
Lecture 200 Exploratory Analysis 4:20
Lecture 201 Feature Extraction 6:20
Lecture 202 Creating and Validating Our Model 10:43

Section 37 : Analyzing Conversion Rates in Marketing Campaigns

Lecture 203 Exploratory Analysis of Understanding Marketing Conversion Rates 17:51

Section 38 : Predicting Advertising Engagement

Lecture 204 Understanding the Problem + Exploratory Data Analysis and Visualizations 8:42
Lecture 205 Data Preparation and Machine Learning Modeling 8:32

Section 39 : Product Sales Analysis

Lecture 206 Problem and Plan of Attack 8:44
Lecture 207 Sales and Revenue Analysis 10:34
Lecture 208 Analysis per Country, Repeat Customers and Items 11:49

Section 40 : Determing Your Most Valuable Customers

Lecture 209 Understanding the Problem + Exploratory Data Analysis and Visualizations 7:6
Lecture 210 Customer Lifetime Value Modeling 6:42

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

Lecture 211 Data Exploration & Description 6:48
Lecture 212 Simple Exploratory Data Analysis and Visualizations 9:46
Lecture 213 Feature Engineering 8:35
Lecture 214 K-Means Clustering of Customer Data 13:5
Lecture 215 Cluster Analysis 4:15

Section 42 : Build a Product Recommendation System

Lecture 216 Dataset Description and Data Cleaning 4:34
Lecture 217 Making a Customer-Item Matrix 4:14
Lecture 218 User-User Matrix - Getting Recommended Items 15:18
Lecture 219 Item-Item Collaborative Filtering - Finding the Most Similar Items 6:31

Section 43 : Movie Recommendation System - LiteFM

Lecture 220 Intro Text

Section 44 : Deep Learning Recommendation System

Lecture 221 Understanding Our Wikipedia Movie Dataset 5:40
Lecture 222 Creating Our Dataset 5:55
Lecture 223 Deep Learning Embeddings and Training 4:8
Lecture 224 Getting Recommendations based on Movie Similarity 4:20

Section 45 : Predicting Brent Oil Prices

Lecture 225 Understanding our Dataset and it's Time Series Nature 5:9
Lecture 226 Creating our Prediction Model 5:9
Lecture 227 Making Future Predictions 12:56

Section 46 : Stock Trading using Reinforcement Learning

Lecture 228 Introduction to Reinforcement Learning Text
Lecture 229 Using Q-Learning and Reinforcement Learning to Build a Trading Bot Text

Section 47 : SalesDemand Forecasting

Lecture 230 Problem and Plan of Attack Text

Section 48 : Detecting Sentiment in Tweets

Lecture 231 Understanding our Dataset and Word Clouds 7:1
Lecture 232 Visualizations and Feature Extraction 10:18
Lecture 233 Training our Model 6:31

Section 49 : Spam or Ham Detection

Lecture 234 Loading and Understanding our SpamHam Dataset 6:53
Lecture 235 Training our Spam Detector 7:46

Section 50 : Explore Data with PySpark and Titanic Surival Prediction

Lecture 236 Exploratory Analysis of our Titantic Dataset 13:35
Lecture 237 Transformation Operations 6:48
Lecture 238 Machine Learning with PySpark 5:0

Section 51 : Newspaper Headline Classification using PySpark

Lecture 239 Loading and Understanding our Dataset 3:56
Lecture 240 Building our Model with PySpark 4:59

Section 52 : Deployment into Production

Lecture 241 Introduction to Production Deployment Systems 6:0
Lecture 242 Creating the Model 4:49
Lecture 243 Introduction to Flask 4:41
Lecture 244 About our WebApp 6:8
Lecture 245 Deploying our WebApp on Heroku 13:16