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

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

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 30 Hours | 12.8 GB

Use Data Science & Statistics To Solve Business Problems & Gain Insights Into Everyday Problems With 35+ Case Studies

Data Science, Analytics & AI for Business & the Real World™ 2020

This is a practical course, the course I wish I had when I first started learning Data Science.

It focuses on understanding all the basic theory and programming skills required as a Data Scientist, but the best part is that it features 35+ Practical Case Studies covering so many common business problems faced by Data Scientists in the real world.

Right now, even in spite of the Covid-19 economic contraction, traditional businesses are hiring Data Scientists in droves!

And they expect new hires to have the ability to apply Data Science solutions to solve their problems. Data Scientists who can do this will prove to be one of the most valuable assets in business over the next few decades!

“Data Scientist has become the top job in the US for the last 4 years running!” according to Harvard Business Review & Glassdoor.

However, Data Science has a difficult learning curve – How does one even get started in this industry awash with mystique, confusion, impossible-looking mathematics, and code? Even if you get your feet wet, applying your newfound Data Science knowledge to a real-world problem is even more confusing.

This course seeks to fill all those gaps in knowledge that scare off beginners and simultaneously apply your knowledge of Data Science and Deep Learning to real-world business problems.

This course has a comprehensive syllabus that tackles all the major components of Data Science knowledge.

Our Complete 2020 Data Science Learning path includes:

  • Using Data Science to Solve Common Business Problems
  • The Modern Tools of a Data Scientist – Python, Pandas, Scikit-learn, NumPy, Keras, prophet, statsmod, scipy and more!
  • Statistics for Data Science in Detail – Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing, and Hypothesis Testing.
  • Visualization Theory for Data Science and Analytics using Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).
  • Dashboard Design using Google Data Studio
  • Machine Learning Theory – Linear Regressions, Logistic Regressions, Decision Trees, Random Forests, KNN, SVMs, Model Assessment, Outlier Detection, ROC & AUC and Regularization
  • Deep Learning Theory and Tools – TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs & LSTMs)
  • Solving problems using Predictive Modeling, Classification, and Deep Learning
  • Data Analysis and Statistical Case Studies – Solve and analyze real-world problems and datasets.
  • Data Science in Marketing – Modeling Engagement Rates and perform A/B Testing
  • Data Science in Retail – Customer Segmentation, Lifetime Value, and Customer/Product Analytics
  • Unsupervised Learning – K-Means Clustering, PCA, t-SNE, Agglomerative Hierarchical, Mean Shift, DBSCAN and E-M GMM Clustering
  • Recommendation Systems – Collaborative Filtering and Content-based filtering + Learn to use LiteFM + Deep Learning Recommendation Systems
  • Natural Language Processing – Bag of Words, Lemmatizing/Stemming, TF-IDF Vectorizer, and Word2Vec
  • Big Data with PySpark – Challenges in Big Data, Hadoop, MapReduce, Spark, PySpark, RDD, Transformations, Actions, Lineage
  • Graphs & Jobs, Data Cleaning and Manipulation, Machine Learning in PySpark (MLLib)
  • Deployment to the Cloud using Heroku to build a Machine Learning API

Our fun and engaging Case Studies include:

Sixteen (16) Statistical and Data Analysis Case Studies:

  • Predicting the US 2020 Election using multiple Polling Datasets
  • Predicting Diabetes Cases from Health Data
  • Market Basket Analysis using the Apriori Algorithm
  • Predicting the Football/Soccer World Cup
  • Covid Analysis and Creating Amazing Flourish Visualisations (Barchart Race)
  • Analyzing Olympic Data
  • Is Home Advantage Real in Soccer or Basketball?
  • IPL Cricket Data Analysis
  • Streaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) – Movie Analysis
  • Pizza Restaurant Analysis – Most Popular Pizzas across the US
  • Micro Brewery and Pub Analysis
  • Supply Chain Analysis
  • Indian Election Analysis
  • Africa Economic Crisis Analysis

Six (6) Predictive Modeling & Classifiers Case Studies:

  • Figuring Out Which Employees May Quit (Retention Analysis)
  • Figuring Out Which Customers May Leave (Churn Analysis)
  • Who do we target for Donations?
  • Predicting Insurance Premiums
  • Predicting Airbnb Prices
  • Detecting Credit Card Fraud

Four (4) Data Science in Marketing Case Studies:

  • Analyzing Conversion Rates of Marketing Campaigns
  • Predicting Engagement – What drives ad performance?
  • A/B Testing (Optimizing Ads)
  • Who are Your Best Customers? & Customer Lifetime Values (CLV)

Four (4) Retail Data Science Case Studies:

  • Product Analytics (Exploratory Data Analysis Techniques
  • Clustering Customer Data from Travel Agency
  • Product Recommendation Systems – Ecommerce Store Items
  • Movie Recommendation System using LiteFM

Two (2) Time-Series Forecasting Case Studies:

  • Sales Forecasting for a Store
  • Stock Trading using Re-Enforcement Learning
  • Brent Oil Price Forecasting

Three (3) Natural Langauge Processing (NLP) Case Studies:

  • Summarizing Reviews
  • Detecting Sentiment in text
  • Spam Detection

One (1) PySpark Big Data Case Studies:

  • News Headline Classification

One (1) Deployment Project:

  • Deploying your Machine Learning Model to the Cloud using Flask & Heroku

What you’ll learn

  • Pandas to become a Data Analytics & Data Wrangling Whiz
  • The most useful Machine Learning Algorithms with Scikit-learn
  • Statistics and Probability
  • Hypothesis Testing & A/B Testing
  • To create beautiful charts, graphs and Visualisations that tell a Story with Data
  • Understand common business problems and how to apply Data Science in solving them
  • Data Dashboards with Google Data Studio
  • 36 Real World Business Problems and Case Studies
  • Recommendation Engines – Collaborative Filtering, LiteFM and Deep Learning methods
  • Natural Language Processing (NLP) using NLTK and Deep Learning
  • Time Series Forecasting with Facebook’s Prophet
  • Data Science in Marketing (Ad engagemnt & Performance)
  • Consumer Analytics and Clustering
  • Social Media Sentiment Analysis
  • Understand Deep Learning (Keras, Tensorflow) and how to use it in several real world case studies
  • Deployment of Machine Learning Models in Production using Heroku and Flask (CI/CD)
  • Perform Sports, Healthcare, Resturant and Economic Analaytics
  • Big Data Analysis and Machine Learning with PySpark
  • How to use Data Science in Retail (Market Basket Analysis, Sales Analytics and Demand forecasting)
  • You’ll be using pre-configured Jupyter Notebooks in Google Colab (no hassle or setup, extremely simple to get started)
  • All code examples run in your web browser regardless if you’re running Windows, macOS, Linux or Android.

+ Table of Contents

1 The Data Science Hype
2 About Our Case Studies
3 Why Data is the new Oil
4 Defining Business Problems for Analytic Thinking & Data Driven Decision making
5 Data Science Projects every Business should do!
6 How Deep Learning is Changing Everything
7 The Career paths of a Data Scientist
8 The Data Science Approach to Problems

Setup (Google Colab) & Download Code
9 Downloading and Running Your Code

Introduction to Python
10 Why use Python for Data Science
11 Python Introduction – Part 1 – Variables
12 Python – Variables (Lists and Dictionaries)
13 Python – Conditional Statements
14 More information on elif
15 Python – Loops
16 Python – Functions
17 Python – Classes

18 Pandas Introduction
19 Pandas 1 – Data Series
20 Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells
21 Pandas 2B – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering
22 Pandas 3A – Data Cleaning – Alter ColomnsRows, Missing Data & String Operations
23 Pandas 3B – Data Cleaning – Alter ColomnsRows, Missing Data & String Operations
24 Pandas 5 – Feature Engineer, Lambda and Apply
25 Pandas 6 – Concatenating, Merging and Joinining
26 Pandas 7 – Time Series Data
27 Pandas 8 – ADVANCED Operations – Iterows, Vectorization and Numpy
28 Pandas 9 – ADVANCED Operations – Iterows, Vectorization and Numpy
29 Pandas 10 – ADVANCED Operations – Parallel Processing
30 Map Visualizations with Plotly – Cloropeths from Scratch – USA and World
31 Map Visualizations with Plotly – Heatmaps, Scatter Plots and Lines

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

Probability Theory
48 Introduction to Probability
49 Estimating Probability
50 Addition Rule
51 Bayes Theorem

Hypothesis Testing
52 Introduction to Hypothesis Testing
53 Statistical Significance
54 Hypothesis Testing – P Value
55 Hypothesis Testing – Pearson Correlation

AB Testing – A Worked Example
56 Understanding the Problem + Exploratory Data Analysis and Visualizations
57 AB Test Result Analysis
58 AB Testing a Worked Real Life Example – Designing an AB Test
59 Statistical Power and Significance
60 Analysis of AB Test Resutls

Data Dashboards – Google Data Studio
61 Intro to Google Data Studio
62 Opening Google Data Studio and Uploading Data
63 Your First Dashboard Part 1
64 Your First Dashboard Part 2
65 Creating New Fields
66 Adding Filters to Tables
67 Scorecard KPI Visalizations
68 Scorecards with Time Comparison
69 Bar Charts (Horizontal, Vertical & Stacked)
70 Line Charts
71 Pie Charts, Donut Charts and Tree Maps
72 Time Series and Comparitive Time Series Plots
73 Scatter Plots
74 Geographic Plots
75 Bullet and Line Area Plots
76 Sharing and Final Conclusions
77 Our Executive Sales Dashboard

Machine Learning
78 Introduction to Machine Learning
79 How Machine Learning enables Computers to Learn
80 What is a Machine Learning Model
81 Types of Machine Learning
82 Linear Regression – Introduction to Cost Functions and Gradient Descent
83 Linear Regressions in Python from Scratch and using Sklearn
84 Polynomial and Multivariate Linear Regression
85 Logistic Regression
86 Support Vector Machines (SVMs)
87 Decision Trees and Random Forests & the Gini Index
88 K-Nearest Neighbors (KNN)
89 Assessing Performance – Confusion Matrix, Precision and Recall
90 Understanding the ROC and AUC Curve
91 What Makes a Good Model Regularization, Overfitting, Generalization & Outliers
92 Introduction to Neural Networks
93 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs

Deep Learning
94 Neural Networks Chapter Overview
95 Machine Learning Overview
96 Neural Networks Explained
97 Forward Propagation
98 Activation Functions
99 Training Part 1 – Loss Functions
100 Training Part 2 – Backpropagation and Gradient Descent
101 Backpropagation & Learning Rates – A Worked Example
102 Regularization, Overfitting, Generalization and Test Datasets
103 Epochs, Iterations and Batch Sizes
104 Measuring Performance and the Confusion Matrix
105 Review and Best Practices

Unsupervised Learning – Clustering
106 Introduction to Unsupervised Learning
107 K-Means Clustering
108 Choosing K – Elbow Method & Silhouette Analysis
109 K-Means in Python – Choosing K using the Elbow Method & Silhoutte Analysis
110 Agglomerative Hierarchical Clustering
111 DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
112 DBSCAN in Python
113 Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

Dimensionality Reduction
114 Principal Component Analysis
115 t-Distributed Stochastic Neighbor Embedding (t-SNE)
116 PCA & t-SNE in Python with Visualization Comparisons

Recommendation Systems
117 Introduction to Recommendation Engines
118 Before recommending, how do we rate or review Items
119 User Collaborative Filtering and ItemContent-based Filtering
120 The Netflix Prize and Matrix Factorization and Deep Learning as Latent-Factor Me

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

Big Data
126 Introduction to Big Data
127 Challenges in Big Data
128 Hadoop, MapReduce and Spark
129 Introduction to PySpark
130 RDDs, Transformations, Actions, Lineage Graphs & Jobs

Predicting the US 2020 Election
131 Understanding Polling Data
132 Cleaning & Exploring our Dataset
133 Data Wrangling our Dataset
134 Understanding the US Electoral System
135 Visualizing our Polling Data
136 Statistical Analysis of Polling Data
137 Polling Simulations
138 Polling Simulation Result Analysis
139 Visualizing our results on a US Map

Predicting Diabetes Cases
140 Understanding and Preparing Our Healthcare Data
141 First Attempt – Trying a Naive Model
142 Trying Different Models and Comparing the Results

Market Basket Analysis
143 Understanding our Dataset
144 Data Preparation
145 Visualizing Our Frequent Sets

Predicting the World Cup Winner (SoccerFootball)
146 Understanding and Preparing Our Soccer Datasets
147 Understanding and Preparing Our Soccer Datasets
148 Predicting Game Outcomes with our Model
149 Simulating the World Cup Outcome with Our Model

Covid-19 Data Analysis and Flourish Bar Chart Race Visualization
150 Understanding Our Covid-19 Data
151 Analysis of the most Recent Data
152 World Visualizations
153 Analyzing Confirmed Cases in each Country
154 Mapping Covid-19 Cases
155 Animating our Maps
156 Comparing Countries and Continents
157 Flourish Bar Chart Race – 1
158 Flourish Bar Chart Race – 2

Analyzing Olmypic Winners
159 Understanding our Olympic Datasets
160 Getting The Medals Per Country
161 Analyzing the Winter Olympic Data and Viewing Medals Won Over Time

Is Home Advantage Real in Soccer and Basketball
162 Understanding Our Dataset and EDA
163 Goal Difference Ratios Home versus Away
164 How Home Advantage Has Evolved Over. Time

IPL Cricket Data Analysis
165 Loading and Understanding our Cricket Datasets
166 Man of Match and Stadium Analysis
167 Do Toss Winners Win More And Team vs Team Comparisons

Streaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) – Movie Analysi
168 Understanding our Dataset
169 EDA and Visualizations
170 Best Movies Per Genre Platform Comparisons

Micro Brewery and Pub Data Analysis
171 EDA, Visualizations and Map

Pizza Resturant Data Analysis
172 EDA and Visualizations
173 Analysis Per State
174 Pizza Maps

Supply Chain Data Analysis
175 Understanding our Dataset
176 Visualizations and EDA
177 More Visualizations

Indian Election Result Analysis
178 Intro
179 Visualizations of Election Results
180 Visualizing Gender Turnout

Africa Economic Crisis Data Analysis
181 Economic Dataset Understanding
182 Visualizations and Correlations

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

Figuring Out Which Customers May Leave
186 Understanding the Problem
187 Exploratory Data Analysis & Visualizations
188 Data Preprocessing
189 Machine Learning Modeling + Deep Learning

Who to Target For Donations
190 Understanding the Problem
191 Exploratory Data Analysis & Visualizations
192 Preparing our Dataset for Machine Learning
193 Modeling using Grid Search for finding the best parameters

Predicting Insurance Premiums
194 Understanding the Problem + Exploratory Data Analysis and Visualizations
195 Data Preparation and Machine Learning Modeling

Predicting Airbnb Prices
196 Understanding the Problem + Exploratory Data Analysis and Visualizations
197 Machine Learning Modeling
198 Using our Model for Value Estimation for New Clients

Detecting Credit Card Fraud
199 Understanding our Dataset
200 Exploratory Analysis
201 Feature Extraction
202 Creating and Validating Our Model

Analyzing Conversion Rates in Marketing Campaigns
203 Exploratory Analysis of Understanding Marketing Conversion Rates

Predicting Advertising Engagement
204 Understanding the Problem + Exploratory Data Analysis and Visualizations
205 Data Preparation and Machine Learning Modeling

Product Sales Analysis
206 Problem and Plan of Attack
207 Sales and Revenue Analysis
208 Analysis per Country, Repeat Customers and Items

Determing Your Most Valuable Customers
209 Understanding the Problem + Exploratory Data Analysis and Visualizations
210 Customer Lifetime Value Modeling

Customer Clustering (K-means, Hierarchial) – Train Passenger
211 Data Exploration & Description
212 Simple Exploratory Data Analysis and Visualizations
213 Feature Engineering
214 K-Means Clustering of Customer Data
215 Cluster Analysis

Build a Product Recommendation System
216 Dataset Description and Data Cleaning
217 Making a Customer-Item Matrix
218 User-User Matrix – Getting Recommended Items
219 Item-Item Collaborative Filtering – Finding the Most Similar Items

Movie Recommendation System – LiteFM
220 Intro

Deep Learning Recommendation System
221 Understanding Our Wikipedia Movie Dataset
222 Creating Our Dataset
223 Deep Learning Embeddings and Training
224 Getting Recommendations based on Movie Similarity

Predicting Brent Oil Prices
225 Understanding our Dataset and it’s Time Series Nature
226 Creating our Prediction Model
227 Making Future Predictions

Stock Trading using Reinforcement Learning
228 Introduction to Reinforcement Learning
229 Using Q-Learning and Reinforcement Learning to Build a Trading Bot

SalesDemand Forecasting
230 Problem and Plan of Attack

Detecting Sentiment in Tweets
231 Understanding our Dataset and Word Clouds
232 Visualizations and Feature Extraction
233 Training our Model

Spam or Ham Detection
234 Loading and Understanding our SpamHam Dataset
235 Training our Spam Detector

Explore Data with PySpark and Titanic Surival Prediction
236 Exploratory Analysis of our Titantic Dataset
237 Transformation Operations
238 Machine Learning with PySpark

Newspaper Headline Classification using PySpark
239 Loading and Understanding our Dataset
240 Building our Model with PySpark

Deployment into Production
241 Introduction to Production Deployment Systems
242 Creating the Model
243 Introduction to Flask
244 About our WebApp
245 Deploying our WebApp on Heroku