Data Science, Analytics & AI for Business & the Real World™

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Gain a comprehensive insight into the core principles of data science, data analytics, and artificial intelligence for ...Read more

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                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

Introduction

The Data Science Hype
About Our Case Studies
Why Data is the new Oil
Defining Business Problems for Analytic Thinking & 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

Setup (Google Colab) & Download Code

Downloading and Running Your Code
Colab Setup

Introduction to Python

Why use Python for Data Science?
Python Introduction - Part 1 - Variables
Python - Variables (Lists and Dictionaries)
More information on elif
Python - Conditional Statements
Python - Loops
Python - Functions
Python - Classes

Pandas

Pandas Introduction
Pandas 1 - Data Series
Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty cells
Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering
Pandas 3A - Data Cleaning - Alter Colomns/Rows, Missing Data & String Operations
Pandas 3B - Data Cleaning - Alter Colomns/Rows, Missing Data & String Operations
Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggreate Functions
Pandas 5 - Feature Engineer, Lambda and Apply
Pandas 6 - Concatenating, Merging and Joinining
Pandas 7 - Time Series Data
Pandas 8 - ADVANCED Operations - Iterows, Vectorization and Numpy
Pandas 9 - ADVANCED Operations - Map, Filter, Apply
Pandas 10 - ADVANCED Operations - Parallel Processing
Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines

Statistics & Visualizations

Introduction to Statistics
Descriptive Statistics - Why Statistical Knowledge is so Important
Descriptive Statistics 1 - Exploratory Data Analysis (EDA) & Visualizations
Descriptive Statistics 2 - Exploratory Data Analysis (EDA) & Visualizations
Sampling, Averages & Variance And How to lie and Mislead with Statistics
Sampling - Sample Sizes & Confidence Intervals - What Can You Trust?
Types of Variables - Quantitive 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'd Think
Variance, Standard Deviation and Bessel’s Correction
Covariance & Correlation - Do Amazon & Google know you better than anyone else?
Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption
The Normal Distribution & the Central Limit Theorem
Z-Scores

Probability Theory

Introduction to Probability
Estimating Probability
Addition Rule
Bayes Theorem

Hypothesis Testing

Introduction to Hypothesis Testing
Statistical Significance
Hypothesis Testing – P Value
Hypothesis Testing – Pearson Correlation

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 Resutls

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
Adding Filters to Tables
Scorecard KPI Visalizations
Scorecards with Time Comparison
Bar Charts (Horizontal, Vertical & Stacked)
Line Charts
Pie Charts, Donut Charts and Tree Maps
Time Series and Comparitive Time Series Plots
Scatter Plots
Geographic Plots
Bullet and Line Area Plots
Sharing and Final Conclusions
Our Executive Sales Dashboard

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 & 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 & Outliers
Introduction to Neural Networks
Types of Deep Learning Algoritms CNNs, RNNs & LSTMs

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 & 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

Unsupervised Learning - Clustering

Introduction to Unsupervised Learning
K-Means Clustering
Choosing K
Kmeans - Elbow and Silhoutte 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)

Dimensionality Reduction

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

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

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

Big Data

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

Predicting the US 2020 Election

Understanding Polling Data
Cleaning & 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

Predicting Diabetes Cases

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

Market Basket Analysis

Understanding our Dataset
Data Preparation
Visualizing Our Frequent Sets

Predicting the World Cup Winner (Soccer/Football)

Understanding and Preparing Our Soccer Datasets
Understanding and Preparing Our Soccer Datasets
Predicting Game Outcomes with our Model
Simulating the World Cup Outcome with Our Model

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

Analyzing Olmypic Winners

Understanding our Olympic Datasets
Getting The Medals Per Country
Analyzing the Winter Olympic Data and Viewing Medals Won Over Time

Is Home Advantage Real in Soccer and Basketball?

Understanding Our Dataset and EDA
Goal Difference Ratios Home versus Away
How Home Advantage Has Evolved Over. Time

IPL Cricket Data Analysis

Loading and Understanding our Cricket Datasets
Man of Match and Stadium Analysis
Do Toss Winners Win More? And Team vs Team Comparisons

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

Understanding our Dataset
EDA and Visualizations
Best Movies Per Genre Platform Comparisons

Micro Brewery and Pub Data Analysis

EDA, Visualizations and Map

Pizza Resturant Data Analysis

EDA and Visualizations
Analysis Per State
Pizza Maps

Supply Chain Data Analysis

Understanding our Dataset
Visualizations and EDA
More Visualizations

Indian Election Result Analysis

Intro
Visualizations of Election Results
Visualizing Gender Turnout

Africa Economic Crisis Data Analysis

Economic Dataset Understanding
Visualizations and Correlations

Predicting Which Employees May Quit

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

Figuring Out Which Customers May Leave

Understanding the Problem
Exploratory Data Analysis & Visualizations
Data Preprocessing
Machine Learning Modeling + Deep Learning

Who to Target For Donations?

Understanding the Problem
Exploratory Data Analysis & Visualizations
Preparing our Dataset for Machine Learning
Modeling using Grid Search for finding the best parameters

Predicting Insurance Premiums

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

Predicting Airbnb Prices

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

Detecting Credit Card Fraud

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

Analyzing Conversion Rates in Marketing Campaigns

Exploratory Analysis of Understanding Marketing Conversion Rates

Predicting Advertising Engagement

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

Product Sales Analysis

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

Determing Your Most Valuable Customers

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

Customer Clustering (K-means, Hierarchial) - Train Passenger

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

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

Movie Recommendation System - LiteFM

Intro

Deep Learning Recommendation System

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

Predicting Brent Oil Prices

Understanding our Dataset and it's Time Series Nature
Creating our Prediction Model
Making Future Predictions

Stock Trading using Reinforcement Learning

Introduction to Reinforcement Learning
Using Q-Learning and Reinforcement Learning to Build a Trading Bot

Sales/Demand Forecasting

Problem and Plan of Attack

Detecting Sentiment in Tweets

Understanding our Dataset and Word Clouds
Visualizations and Feature Extraction
Training our Model

Spam or Ham Detection

Loading and Understanding our Spam/Ham Dataset
Training our Spam Detector

Explore Data with PySpark and Titanic Surival Prediction

Exploratory Analysis of our Titantic Dataset
Transformation Operations
Machine Learning with PySpark

Newspaper Headline Classification using PySpark

Loading and Understanding our Dataset
Building our Model with PySpark

Deployment into Production

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

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