1. Introduction to the Course FREE CHAPTER

• The Data Science Hype
• About Our Case Studies
• Why Data is the New Oil
• Defining Business Problems for Analytic Thinking and Data-Driven Decision Making
• 10 Data Science Projects Every Business Should Do!
• How Deep Learning is Changing Everything
• The Career Paths of a Data Scientist
• The Data Science Approach to Problems

2. Set Up (Google Colab) and Download Code Files

• Downloading and Running Your Code

3. Introduction to Python

• Why Use Python for Data Science?
• Python Introduction - Part 1 - Variables
• Python - Variables (Lists and Dictionaries)
• Python - Conditional Statements
• Python - Loops
• Python - Functions
• Python - Classes

4. Pandas

• Introduction to Pandas
• Pandas 1 - Data Series
• Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty Cells
• Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty Cells, and Filtering
• Pandas 3A - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
• Pandas 3B - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
• Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggregate Functions
• Feature Engineer, Lambda, and Apply
• Concatenating, Merging, and Joining
• Time Series Data
• Advanced Operations - Iterows, Vectorization, and NumPy
• Advanced Operations - Map, Filter, Apply
• Advanced Operations - Parallel Processing
• Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
• Map Visualizations with Plotly - Heatmaps, Scatter Plots, and Lines

5. Statistics and Visualizations

• Introduction to Statistics
• Descriptive Statistics - Why Statistical Knowledge is So Important
• Descriptive Statistics 1 - Exploratory Data Analysis (EDA) and Visualizations
• Descriptive Statistics 2 - Exploratory Data Analysis (EDA) and Visualizations
• Sampling, Averages, and Variance, and How to Lie and Mislead with Statistics
• Sampling - Sample Sizes and Confidence Intervals - What Can You Trust?
• Types of Variables - Quantitative and Qualitative
• Frequency Distributions
• Frequency Distributions Shapes
• Analyzing Frequency Distributions - What is the Best Type of Wine? Red or White?
• Mean, Mode, and Median - Not as Simple as You Think
• Variance, Standard Deviation, and Bessel’s Correction
• Covariance and Correlation - Do Amazon and Google Know You Better Than Anyone Else?
• Lying with Correlations - Divorce Rates in Maine Caused by Margarine Consumption
• The Normal Distribution and the Central Limit Theorem
• Z-Scores

6. Probability Theory

• Introduction to Probability
• Estimating Probability
• Probability - Addition Rule
• Probability - Permutations and Combinations
• Bayes Theorem

7. Hypothesis Testing

• Introduction to Hypothesis Testing
• Statistical Significance
• Hypothesis Testing - P Value
• Hypothesis Testing - Pearson Correlation

8. A/B Testing - A Worked Example

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• A/B Test Result Analysis
• A/B Testing a Worked Real-Life Example - Designing an A/B Test
• Statistical Power and Significance
• Analysis of A/B Test Results

9. Data Dashboards - Google Data Studio

• Intro to Google Data Studio
• Opening Google Data Studio and Uploading Data
• Your First Dashboard Part 1
• Your First Dashboard Part 2
• Creating New Fields to Our data
• Pivot Tables - Total Profit
• Adding Filters to Tables
• Scorecard KPI Visualizations
• Scorecards with Time Comparison
• Bar Charts (Horizontal, Vertical, and Stacked)
• Line Charts
• Pie Charts, Donut Charts, and Tree Maps
• Time Series and Comparative Time Series Plots
• Scatter Plots
• Geographic Plots
• Bullet and Line Area Plots
• Sharing and Final Conclusions
• Our Executive Sales Dashboard

10. Machine Learning

• Introduction to Machine Learning
• How Machine Learning enables Computers to Learn
• What is a Machine Learning Model?
• Types of Machine Learning
• Linear Regression - Introduction to Cost Functions and Gradient Descent
• Linear Regressions in Python from Scratch and Using Sklearn
• Polynomial and Multivariate Linear Regression
• Logistic Regression
• Support Vector Machines (SVMs)
• Decision Trees and Random Forests, and the Gini Index
• K-Nearest Neighbors (KNN)
• Assessing Performance - Confusion Matrix, Precision, and Recall
• Understanding the ROC and AUC Curve
• What Makes a Good Model? Regularization, Overfitting, Generalization, and Outliers
• Introduction to Neural Networks
• Types of Deep Learning Algorithms CNNs, RNNs, and LSTMs

11. Deep Learning

• Neural Networks Chapter Overview
• Machine Learning Overview
• Neural Networks Explained
• Forward Propagation
• Activation Functions
• Training Part 1 - Loss Functions
• Training Part 2 - Backpropagation and Gradient Descent
• Backpropagation and Learning Rates - A Worked Example
• Regularization, Overfitting, Generalization, and Test Datasets
• Epochs, Iterations, and Batch Sizes
• Measuring Performance and the Confusion Matrix
• Review and Best Practices

12. Unsupervised Learning - Clustering

• Introduction to Unsupervised Learning
• K-Means Clustering
• Choosing K
• K-Means - Elbow and Silhouette Method
• Agglomerative Hierarchical Clustering
• Mean Shift Clustering
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• DBSCAN in Python
• Expectation-Maximization (EM) Clustering Using Gaussian Mixture Models (GMM)

13. Dimensionality Reduction

• Principal Component Analysis
• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• PCA and t-SNE in Python with Visualization Comparisons

14. Recommendation Systems

• Introduction to Recommendation Engines
• Before Recommending, How Do We Rate or Review Items?
• User Collaborative Filtering and Item/Content-Based Filtering
• The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me

15. Natural Language Processing

• Introduction to Natural Language Processing
• Modeling Language - The Bag of Words Model
• Normalization, Stop Word Removal, Lemmatizing/Stemming
• TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency)
• Word2Vec - Efficient Estimation of Word Representations in Vector Space

16. Big Data

• Introduction to Big Data
• Challenges in Big Data
• Hadoop, MapReduce, and Spark
• Introduction to PySpark
• RDDs, Transformations, Actions, Lineage Graphs, and Jobs

17. Predicting the US 2020 Election

• Understanding Polling Data
• Cleaning and Exploring Our Dataset
• Data Wrangling Our Dataset
• Understanding the US Electoral System
• Visualizing Our Polling Data
• Statistical Analysis of Polling Data
• Polling Simulations
• Polling Simulation Result Analysis
• Visualizing Our results on a US Map

18. Predicting Diabetes Cases

• Understanding and Preparing Our Healthcare Data
• First Attempt - Trying a Naive Model
• Trying Different Models and Comparing the Results

19. Market Basket Analysis

• Understanding Our Dataset
• Data Preparation
• Visualizing Our Frequent Sets

20. Predicting the World Cup Winner (Soccer/Football)

• Understanding and Preparing Our Soccer Datasets - Part 1
• Understanding and Preparing Our Soccer Datasets - Part 2
• Predicting Game Outcomes with Our Model
• Simulating the World Cup Outcome with Our Model

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

• Understanding Our Covid-19 Data
• Analysis of the Most Recent Data
• World Visualizations
• Analyzing Confirmed Cases in Each Country
• Mapping Covid-19 Cases
• Animating Our Maps
• Comparing Countries and Continents
• Flourish Bar Chart Race - 1
• Flourish Bar Chart Race - 2

22. Analyzing Olympic Winners

• Understanding Our Olympic Dataset
• Getting the Medals Per Country
• Analyzing the Winter Olympic Data and Viewing Medals Won Over Time

23. Is Home Advantage Real in Soccer and Basketball

• Understanding Our Dataset and EDA
• Goal Difference Ratios Home Versus Away
• How Home Advantage Have Evolved Over Time

24. IPL Cricket Data Analysis

• Loading and Understanding Our Cricket Dataset
• Man of the Match and Stadium Analysis
• Do Toss Winners Win More? And Team Versus Team Comparisons

25. Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)

• Understanding Our Dataset
• EDA and Visualizations
• Best Movies Per Genre Platform Comparisons

26. Micro Brewery and Pub Data Analysis

• EDA, Visualizations, and Map

27. Pizza Restaurant Data Analysis

• EDA and Visualizations
• Analysis Per State
• Pizza Maps

28. Supply Chain Data Analysis

• Understanding Our Dataset
• Visualizations and EDA
• More Visualizations

29. Indian Election Result Analysis

• Introduction
• Visualizations of Election Results
• Visualizing Gender Turnout

30. Africa Economic Crisis Data Analysis

• Economic Dataset Understanding
• Visualizations and Correlations

31. Predicting Which Employees May Quit

• Figuring Out Which Employees May Quit - Understanding the Problem and EDA
• Data Cleaning and Preparation
• Machine Learning Modeling + Deep Learning

32. Figuring Out Which Customers May Leave

• Understanding the Problem
• Exploratory Data Analysis and Visualizations
• Data Pre-Processing
• Machine Learning Modeling + Deep Learning

33. Who to Target for Donations?

• Understanding the Problem
• Exploratory Data Analysis and Visualizations
• Preparing Our Dataset for Machine Learning
• Modeling Using Grid Search to Find the best parameters

34. Predicting Insurance Premiums

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Data Preparation and Machine Learning Modeling

35. Predicting Airbnb Prices

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Machine Learning Modeling
• Using Our Model for Value Estimation for New Clients

36. Detecting Credit Card Fraud

• Understanding Our Dataset
• Exploratory Analysis
• Feature Extraction
• Creating and Validating Our Model

37. Analyzing Conversion Rates in Marketing Campaigns

• Exploratory Analysis of Understanding Marketing Conversion Rates

38. Predicting Advertising Engagement

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Data Preparation and Machine Learning Modeling

39. Product Sales Analysis

• Problem and Plan of Attack
• Sales and Revenue Analysis
• Analysis Per Country, Repeat Customers, and Items

40. Determining Your Most Valuable Customers

• Understanding the Problem + Exploratory Data Analysis and Visualizations
• Customer Lifetime Value Modeling

41. Customer Clustering (K-Means, Hierarchical) - Train Passenger

• Data Exploration and Description
• Simple Exploratory Data Analysis and Visualizations
• Feature Engineering
• K-Means Clustering of Customer Data
• Cluster Analysis

42. Build a Product Recommendation System

• Dataset Description and Data Cleaning
• Making a Customer-Item Matrix
• User-User Matrix - Getting Recommended Items
• Item-Item Collaborative Filtering - Finding the Most Similar Items

43. Deep Learning Recommendation System

• Understanding Our Wikipedia Movie Dataset
• Creating Our Dataset
• Deep Learning Embeddings and Training
• Getting Recommendations Based on Movie Similarity

44. Predicting Brent Oil Prices

• Understanding Our Dataset and Its Time Series Nature
• Creating Our Prediction Model
• Making Future Predictions

45. Detecting Sentiment in Tweets

• Understanding Our Dataset and Word Clouds
• Visualizations and Feature Extraction
• Training Our Model

46. Spam or Ham Detection

• Loading and Understanding Our Spam/Ham Dataset
• Training Our Spam Detector

47. Explore Data with PySpark and Titanic Survival Prediction

• Exploratory Analysis of Our Titanic Dataset
• Transformation Operations
• Machine Learning with PySpark

48. Newspaper Headline Classification Using PySpark

• Loading and Understanding Our Dataset
• Building Our Model with PySpark

49. Deployment into Production

• Introduction to Production Deployment Systems
• Creating the Model
• Introduction to Flask
• About Our WebApp
• Deploying Our WebApp on Heroku