Section 1 : Introduction
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 About Our Case Studies 00:05:01 Duration
Lecture 3 Why Data is the new Oil 00:06:37 Duration
Lecture 4 Defining Business Problems for Analytic Thinking & Data Driven Decision making 00:05:40 Duration
Lecture 5 10 Data Science Projects every Business should do! 00:14:03 Duration
Lecture 6 How Deep Learning is Changing Everything 00:05:09 Duration
Lecture 7 The Career paths of a Data Scientist 00:04:51 Duration
Lecture 8 The Data Science Approach to Problems 00:07:57 Duration

Section 2 : Setup (Google Colab) & Download Code
Lecture 1 Downloading and Running Your Code 00:02:18 Duration
Lecture 2 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 3 : Introduction to Python
Lecture 1 Why use Python for Data Science 00:03:05 Duration
Lecture 2 Python Introduction - Part 1 - Variables 00:06:31 Duration
Lecture 3 Python - Variables (Lists and Dictionaries) 00:11:09 Duration
Lecture 4 Python - Conditional Statements 00:06:55 Duration
Lecture 5 More information on elif
Lecture 6 Python - Loops 00:08:49 Duration
Lecture 7 Python - Functions 00:05:29 Duration
Lecture 8 Python - Classes 00:08:35 Duration

Section 4 : Pandas
Lecture 1 Pandas Introduction 00:02:48 Duration
Lecture 2 Pandas 1 - Data Series 00:06:17 Duration
Lecture 3 Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty cells 00:17:31 Duration
Lecture 4 Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering 00:05:51 Duration
Lecture 5 Pandas 3A - Data Cleaning - Alter ColomnsRows, Missing Data & String Operations 00:08:46 Duration
Lecture 6 Pandas 3B - Data Cleaning - Alter ColomnsRows, Missing Data & String Operations 00:19:46 Duration
Lecture 7 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 8 Pandas 5 - Feature Engineer, Lambda and Apply 00:04:24 Duration
Lecture 9 Pandas 6 - Concatenating, Merging and Joinining 00:15:21 Duration
Lecture 10 Pandas 7 - Time Series Data 00:09:56 Duration
Lecture 11 Pandas 8 - ADVANCED Operations - Iterows, Vectorization and Numpy 00:11:17 Duration
Lecture 12 Pandas 9 - ADVANCED Operations - Iterows, Vectorization and Numpy 00:04:29 Duration
Lecture 13 Pandas 10 - ADVANCED Operations - Parallel Processing 00:04:24 Duration
Lecture 14 Map Visualizations with Plotly - Cloropeths from Scratch - USA and World 00:11:25 Duration
Lecture 15 Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines 00:04:55 Duration

Section 5 : Statistics & Visualizations
Lecture 1 Introduction to Statistics 00:04:20 Duration
Lecture 2 Descriptive Statistics - Why Statistical Knowledge is so Important 00:03:47 Duration
Lecture 3 Descriptive Statistics 1 - Exploratory Data Analysis (EDA) & Visualizations 00:16:20 Duration
Lecture 4 Descriptive Statistics 2 - Exploratory Data Analysis (EDA) & Visualizations 00:06:23 Duration
Lecture 5 Sampling, Averages & Variance And How to lie and Mislead with Statistics 00:03:16 Duration
Lecture 6 Sampling - Sample Sizes & Confidence Intervals - What Can You Trust 00:09:41 Duration
Lecture 7 Types of Variables - Quantitive and Qualitative 00:05:41 Duration
Lecture 8 Frequency Distributions 00:04:41 Duration
Lecture 9 Frequency Distributions Shapes 00:02:56 Duration
Lecture 10 Analyzing Frequency Distributions - What is the Best Type of WIne Red or White 00:09:59 Duration
Lecture 11 Mean, Mode and Median - Not as Simple As You'd Think 00:12:38 Duration
Lecture 12 Variance, Standard Deviation and Bessel’s Correction 00:09:22 Duration
Lecture 13 Covariance & Correlation - Do Amazon & Google know you better than anyone else 00:11:36 Duration
Lecture 14 Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption 00:01:38 Duration
Lecture 15 The Normal Distribution & the Central Limit Theorem 00:03:43 Duration
Lecture 16 Z-Scores 00:08:16 Duration

Section 6 : Probability Theory
Lecture 1 Introduction to Probability 00:01:41 Duration
Lecture 2 Estimating Probability 00:05:14 Duration
Lecture 3 Addition Rule 00:07:58 Duration
Lecture 4 Bayes Theorem 00:07:50 Duration

Section 7 : Hypothesis Testing
Lecture 1 Introduction to Hypothesis Testing 00:03:03 Duration
Lecture 2 Statistical Significance 00:08:13 Duration
Lecture 3 Hypothesis Testing – P Value 00:07:44 Duration
Lecture 4 Hypothesis Testing – Pearson Correlation 00:05:26 Duration

Section 8 : AB Testing - A Worked Example
Lecture 1 Understanding the Problem + Exploratory Data Analysis and Visualizations 00:10:50 Duration
Lecture 2 AB Test Result Analysis 00:05:37 Duration
Lecture 3 AB Testing a Worked Real Life Example - Designing an AB Test 00:08:17 Duration
Lecture 4 Statistical Power and Significance 00:06:58 Duration
Lecture 5 Analysis of AB Test Resutls 00:08:16 Duration

Section 9 : Data Dashboards - Google Data Studio
Lecture 1 Intro to Google Data Studio 00:05:06 Duration
Lecture 2 Opening Google Data Studio and Uploading Data 00:04:27 Duration
Lecture 3 Your First Dashboard Part 1 00:14:29 Duration
Lecture 4 Your First Dashboard Part 2 00:10:02 Duration
Lecture 5 Creating New Fields 00:05:37 Duration
Lecture 6 Adding Filters to Tables
Lecture 7 Scorecard KPI Visalizations 00:06:10 Duration
Lecture 8 Scorecards with Time Comparison 00:05:44 Duration
Lecture 9 Bar Charts (Horizontal, Vertical & Stacked) 00:08:45 Duration
Lecture 10 Line Charts 00:07:01 Duration
Lecture 11 Pie Charts, Donut Charts and Tree Maps 00:04:52 Duration
Lecture 12 Time Series and Comparitive Time Series Plots 00:04:01 Duration
Lecture 13 Scatter Plots 00:04:50 Duration
Lecture 14 Geographic Plots 00:07:21 Duration
Lecture 15 Bullet and Line Area Plots 00:05:32 Duration
Lecture 16 Sharing and Final Conclusions 00:06:57 Duration
Lecture 17 Our Executive Sales Dashboard 00:02:19 Duration

Section 10 : Machine Learning
Lecture 1 Introduction to Machine Learning 00:03:33 Duration
Lecture 2 How Machine Learning enables Computers to Learn 00:03:25 Duration
Lecture 3 What is a Machine Learning Model 00:06:21 Duration
Lecture 4 Types of Machine Learning 00:07:41 Duration
Lecture 5 Linear Regression – Introduction to Cost Functions and Gradient Descent 00:09:11 Duration
Lecture 6 Linear Regressions in Python from Scratch and using Sklearn 00:14:18 Duration
Lecture 7 Polynomial and Multivariate Linear Regression 00:08:30 Duration
Lecture 8 Logistic Regression 00:11:40 Duration
Lecture 9 Support Vector Machines (SVMs) 00:05:36 Duration
Lecture 10 Decision Trees and Random Forests & the Gini Index 00:10:45 Duration
Lecture 11 K-Nearest Neighbors (KNN) 00:05:44 Duration
Lecture 12 Assessing Performance – Confusion Matrix, Precision and Recall 00:22:37 Duration
Lecture 13 Understanding the ROC and AUC Curve 00:06:39 Duration
Lecture 14 What Makes a Good Model Regularization, Overfitting, Generalization & Outliers 00:16:16 Duration
Lecture 15 Introduction to Neural Networks 00:02:07 Duration
Lecture 16 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs 00:07:38 Duration

Section 11 : Deep Learning
Lecture 1 Neural Networks Chapter Overview 00:01:35 Duration
Lecture 2 Machine Learning Overview 00:08:26 Duration
Lecture 3 Neural Networks Explained 00:03:51 Duration
Lecture 4 Forward Propagation
Lecture 5 Activation Functions 00:08:31 Duration
Lecture 6 Training Part 1 – Loss Functions 00:09:13 Duration
Lecture 7 Training Part 2 – Backpropagation and Gradient Descent 00:09:57 Duration
Lecture 8 Backpropagation & Learning Rates – A Worked Example 00:13:36 Duration
Lecture 9 Regularization, Overfitting, Generalization and Test Datasets 00:15:25 Duration
Lecture 10 Epochs, Iterations and Batch Sizes
Lecture 11 Measuring Performance and the Confusion Matrix 00:07:07 Duration
Lecture 12 Review and Best Practices 00:04:16 Duration

Section 12 : Unsupervised Learning - Clustering
Lecture 1 Introduction to Unsupervised Learning 00:04:56 Duration
Lecture 2 K-Means Clustering 00:09:34 Duration
Lecture 3 Choosing K – Elbow Method & Silhouette Analysis 00:07:51 Duration
Lecture 4 K-Means in Python - Choosing K using the Elbow Method & Silhoutte Analysis 00:07:51 Duration
Lecture 5 Agglomerative Hierarchical Clustering 00:04:53 Duration
Lecture 6 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 7 DBSCAN (Density-Based Spatial Clustering of Applications with Noise) 00:04:36 Duration
Lecture 8 DBSCAN in Python 00:02:31 Duration
Lecture 9 Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM) 00:05:56 Duration

Section 13 : Dimensionality Reduction
Lecture 1 Principal Component Analysis 00:06:57 Duration
Lecture 2 t-Distributed Stochastic Neighbor Embedding (t-SNE) 00:05:38 Duration
Lecture 3 PCA & t-SNE in Python with Visualization Comparisons 00:05:22 Duration

Section 14 : Recommendation Systems
Lecture 1 Introduction to Recommendation Engines 00:05:04 Duration
Lecture 2 Before recommending, how do we rate or review Items 00:06:01 Duration
Lecture 3 User Collaborative Filtering and ItemContent-based Filtering 00:09:58 Duration
Lecture 4 The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me 00:08:20 Duration

Section 15 : Natural Language Processing
Lecture 1 Introduction to Natural Language Processing 00:03:21 Duration
Lecture 2 Modeling Language – The Bag of Words Model 00:04:15 Duration
Lecture 3 Normalization, Stop Word Removal, LemmatizingStemming 00:04:55 Duration
Lecture 4 TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency) 00:01:49 Duration
Lecture 5 Word2Vec - Efficient Estimation of Word Representations in Vector Space 00:05:48 Duration

Section 16 : Big Data
Lecture 1 Introduction to Big Data 00:06:53 Duration
Lecture 2 Challenges in Big Data 00:04:52 Duration
Lecture 3 Hadoop, MapReduce and Spark 00:07:03 Duration
Lecture 4 Introduction to PySpark 00:02:11 Duration
Lecture 5 RDDs, Transformations, Actions, Lineage Graphs & Jobs 00:07:03 Duration

Section 17 : Predicting the US 2020 Election
Lecture 1 Understanding Polling Data 00:07:52 Duration
Lecture 2 Cleaning & Exploring our Dataset 00:05:56 Duration
Lecture 3 Data Wrangling our Dataset 00:04:55 Duration
Lecture 4 Understanding the US Electoral System 00:06:20 Duration
Lecture 5 Visualizing our Polling Data 00:06:20 Duration
Lecture 6 Statistical Analysis of Polling Data 00:03:43 Duration
Lecture 7 Polling Simulations 00:10:42 Duration
Lecture 8 Polling Simulation Result Analysis 00:07:19 Duration
Lecture 9 Visualizing our results on a US Map 00:04:32 Duration

Section 18 : Predicting Diabetes Cases
Lecture 1 Understanding and Preparing Our Healthcare Data 00:08:12 Duration
Lecture 2 First Attempt - Trying a Naive Model 00:03:53 Duration
Lecture 3 Trying Different Models and Comparing the Results 00:06:29 Duration

Section 19 : Market Basket Analysis
Lecture 1 Understanding our Dataset 00:06:51 Duration
Lecture 2 Data Preparation 00:05:22 Duration
Lecture 3 Visualizing Our Frequent Sets 00:10:22 Duration

Section 20 : Predicting the World Cup Winner (SoccerFootball)
Lecture 1 Understanding and Preparing Our Soccer Dataset
Lecture 2 Understanding and Preparing Our Soccer Datase 00:05:52 Duration
Lecture 3 Predicting Game Outcomes with our Model 00:06:20 Duration
Lecture 4 Simulating the World Cup Outcome with Our Mode 00:13:30 Duration

Section 21 : Covid-19 Data Analysis and Flourish Bar Chart Race Visualization
Lecture 1 Understanding Our Covid-19 Data 00:07:31 Duration
Lecture 2 Analysis of the most Recent Data 00:05:46 Duration
Lecture 3 World Visualizations 00:08:55 Duration
Lecture 4 Analyzing Confirmed Cases in each Country 00:04:29 Duration
Lecture 5 Mapping Covid-19 Cases 00:05:56 Duration
Lecture 6 Animating our Maps 00:04:48 Duration
Lecture 7 Comparing Countries and Continents 00:04:50 Duration
Lecture 8 Flourish Bar Chart Race - 1 00:10:59 Duration
Lecture 9 Flourish Bar Chart Race - 2 00:09:53 Duration

Section 22 : Analyzing Olmypic Winners
Lecture 1 Understanding our Olympic Datasets 00:09:40 Duration
Lecture 2 Getting The Medals Per Country 00:09:30 Duration
Lecture 3 Analyzing the Winter Olympic Data and Viewing Medals Won Over Time 00:05:42 Duration

Section 23 : Is Home Advantage Real in Soccer and Basketball
Lecture 1 Understanding Our Dataset and EDA 00:09:28 Duration
Lecture 2 Goal Difference Ratios Home versus Away 00:04:26 Duration
Lecture 3 How Home Advantage Has Evolved Over 00:05:09 Duration

Section 24 : IPL Cricket Data Analysis
Lecture 1 Loading and Understanding our Cricket Datasets 00:07:25 Duration
Lecture 2 Man of Match and Stadium Analysis 00:05:50 Duration
Lecture 3 Do Toss Winners Win More And Team vs Team Comparisons 00:07:08 Duration

Section 25 : Streaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) - Movie Analysi
Lecture 1 Understanding our Dataset 00:06:36 Duration
Lecture 2 EDA and Visualizations 00:08:45 Duration
Lecture 3 Best Movies Per Genre Platform Comparisons 00:12:40 Duration

Section 26 : Micro Brewery and Pub Data Analysis
Lecture 1 EDA, Visualizations and Map 00:13:41 Duration

Section 27 : Pizza Resturant Data Analysis
Lecture 1 EDA and Visualizations 00:06:48 Duration
Lecture 2 Analysis Per State 00:04:46 Duration
Lecture 3 Pizza Maps 00:07:24 Duration

Section 28 : Supply Chain Data Analysis
Lecture 1 Understanding our Dataset 00:04:04 Duration
Lecture 2 Visualizations and EDA 00:09:21 Duration
Lecture 3 More Visualizations 00:04:32 Duration

Section 29 : Indian Election Result Analysis
Lecture 1 Intro 00:07:33 Duration
Lecture 2 Visualizations of Election Results 00:09:29 Duration
Lecture 3 Visualizing Gender Turnout 00:10:59 Duration

Section 30 : Africa Economic Crisis Data Analysis
Lecture 1 Economic Dataset Understanding 00:05:15 Duration
Lecture 2 Visualizations and Correlations 00:07:43 Duration

Section 31 : Predicting Which Employees May Quit
Lecture 1 Figuring Out Which Employees May Quit –Understanding the Problem & EDA 00:07:06 Duration
Lecture 2 Data Cleaning and Preparation 00:07:27 Duration
Lecture 3 Machine Learning Modeling + Deep Learning 00:17:44 Duration

Section 32 : Figuring Out Which Customers May Leave
Lecture 1 Understanding the Problem 00:03:39 Duration
Lecture 2 Exploratory Data Analysis & Visualizations 00:06:50 Duration
Lecture 3 Data Preprocessing 00:05:57 Duration
Lecture 4 Machine Learning Modeling + Deep Learning 00:14:52 Duration

Section 33 : Who to Target For Donations
Lecture 1 Understanding the Problem 00:03:31 Duration
Lecture 2 Exploratory Data Analysis & Visualizations 00:07:07 Duration
Lecture 3 Preparing our Dataset for Machine Learning 00:13:18 Duration
Lecture 4 Modeling using Grid Search for finding the best parameters 00:05:04 Duration

Section 34 : Predicting Insurance Premiums
Lecture 1 Understanding the Problem + Exploratory Data Analysis and Visualizations 00:10:11 Duration
Lecture 2 Data Preparation and Machine Learning Modeling 00:12:44 Duration

Section 35 : Predicting Airbnb Prices
Lecture 1 Understanding the Problem + Exploratory Data Analysis 00:19:53 Duration
Lecture 2 Machine Learning Modeling 00:18:07 Duration
Lecture 3 Using our Model for Value Estimation for New Clients 00:04:03 Duration

Section 36 : Detecting Credit Card Fraud
Lecture 1 Understanding our Dataset
Lecture 2 Exploratory Analysis 00:04:20 Duration
Lecture 3 Feature Extraction 00:06:20 Duration
Lecture 4 Creating and Validating Our Model 00:10:43 Duration

Section 37 : Analyzing Conversion Rates in Marketing Campaigns
Lecture 1 Exploratory Analysis of Understanding Marketing Conversion Rates 00:17:51 Duration

Section 38 : Predicting Advertising Engagement
Lecture 1 Understanding the Problem + Exploratory Data Analysis and Visualizations 00:08:42 Duration
Lecture 2 Data Preparation and Machine Learning Modeling 00:08:32 Duration

Section 39 : Product Sales Analysis
Lecture 1 Problem and Plan of Attack 00:08:44 Duration
Lecture 2 Sales and Revenue Analysis 00:10:34 Duration
Lecture 3 Analysis per Country, Repeat Customers and Items 00:11:49 Duration

Section 40 : Determing Your Most Valuable Customers
Lecture 1 Understanding the Problem + Exploratory Data Analysis and Visualizations 00:07:06 Duration
Lecture 2 Customer Lifetime Value Modeling 00:06:42 Duration

Section 41 : Customer Clustering (K-means, Hierarchial) - Train Passenger
Lecture 1 Data Exploration & Description 00:06:48 Duration
Lecture 2 Simple Exploratory Data Analysis and Visualizations 00:09:46 Duration
Lecture 3 Feature Engineering 00:08:35 Duration
Lecture 4 K-Means Clustering of Customer 00:13:05 Duration
Lecture 5 Cluster Analysis 00:04:15 Duration

Section 42 : Build a Product Recommendation System
Lecture 1 Dataset Description and Data Cleaning 00:04:34 Duration
Lecture 2 Making a Customer-Item Matrix 00:04:14 Duration
Lecture 3 User-User Matrix - Getting Recommended Items 00:15:18 Duration
Lecture 4 Item-Item Collaborative Filtering - Finding the Most Similar Items 00:06:31 Duration

Section 43 : Movie Recommendation System - LiteFM
Lecture 1 Intro

Section 44 : Deep Learning Recommendation System
Lecture 1 Understanding Our Wikipedia Movie Dataset 00:05:40 Duration
Lecture 2 Creating Our Dataset 00:05:55 Duration
Lecture 3 Deep Learning Embeddings and Training 00:04:08 Duration
Lecture 4 Getting Recommendations based on Movie Similarity 00:04:20 Duration

Section 45 : Predicting Brent Oil Prices
Lecture 1 Understanding our Dataset and it's Time Series Nature 00:05:09 Duration
Lecture 2 Creating our Prediction Model 00:05:08 Duration
Lecture 3 Making Future Predictions 00:12:56 Duration

Section 46 : Stock Trading using Reinforcement Learning
Lecture 1 Introduction to Reinforcement Learning
Lecture 2 Using Q-Learning and Reinforcement Learning to Build a Trading Bot

Section 47 : SalesDemand Forecasting
Lecture 1 Problem and Plan of Attack

Section 48 : Detecting Sentiment in Tweets
Lecture 1 Understanding our Dataset and Word Clouds 00:07:01 Duration
Lecture 2 Visualizations and Feature Extraction 00:10:17 Duration
Lecture 3 Training our Model 00:06:31 Duration

Section 49 : Spam or Ham Detection
Lecture 1 Loading and Understanding our SpamHam Dataset 00:06:53 Duration
Lecture 2 Training our Spam Detector 00:07:46 Duration

Section 50 : Explore Data with PySpark and Titanic Surival Prediction
Lecture 1 Exploratory Analysis of our Titantic Dataset 00:13:35 Duration
Lecture 2 Transformation Operations 00:06:48 Duration
Lecture 3 Machine Learning with PySpark 00:05:00 Duration

Section 51 : Newspaper Headline Classification using PySpark
Lecture 1 Loading and Understanding our Dataset 00:03:56 Duration
Lecture 2 Building our Model with PySpark 00:04:59 Duration

Section 52 : Deployment into Production
Lecture 1 Introduction to Production Deployment Systems 00:06:00 Duration
Lecture 2 Creating the Model 00:04:49 Duration
Lecture 3 Introduction to Flask 00:04:41 Duration
Lecture 4 About our WebApp 00:06:08 Duration
Lecture 5 Deploying our WebApp on Heroku 00:13:16 Duration