Data Science & Deep Learning for Business™ 20 Case Studies

Data Science & Deep Learning for Business™ 20 Case Studies

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 20 Hours | 2.64 GB

Use Python for Data Analysis, Data Science in Marketing & Retail, Recommendations, Forecasts, Customer Clustering & NLP

This course takes on Machine Learning and Statistical theory and teaches you to use it in solving 20 real-world Business problems.

Data Scientist is the buzz of the 21st century for good reason! The tech revolution is just starting and Data Science is at the forefront.

As a result, “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 Learning path includes:

  • How Data Science and Solve Many Common Business Problems
  • The Modern Tools of a Data Scientist – Python, Pandas, Scikit-learn, Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).
  • Statistics for Data Science in Detail – Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing and Hypothesis Testing.
  • 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 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
  • 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 AWS to build a Machine Learning API

Our fun and engaging 20 Case Studies include:

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

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

  • Summarizing Reviews
  • Detecting Sentiment in text
  • Spam Filters

One (1) PySpark Big Data Case Studies:

  • News Headline Classification

“Big data is at the foundation of all the megatrends that are happening.”

Businesses NEED Data Scientists more than ever. Those who ignore this trend will be left behind by their competition. In fact, the majority of new Data Science jobs won’t be created by traditional tech companies (Google, Facebook, Microsoft, Amazon, etc.) they’re being created by your traditional non-tech businesses. The big retailers, banks, marketing companies, government institutions, insurances, real estate and more.

“Consumer data will be the biggest differentiator in the next two to three years. Whoever unlocks the reams of data and uses it strategically will win.”

With Data Scientist salaries creeping up higher and higher, this course seeks to take you from a beginner and turn you into a Data Scientist capable of solving challenging real-world problems.

What you’ll learn

  • Understand the value of data for businesses
  • Learn to use Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and more!
  • Apply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time Value
  • Machine Learning from Linear Regressions (polynomial & multivariate), K-NNs, Logistic Regressions, SVMs, Decision Trees & Random Forests
  • Unsupervised Machine Learning with K-Means, Mean-Shift, DBSCAN, EM with GMMs, PCA and t-SNE
  • Build a Product Recommendation Tool using collaborative & item/content based
  • Hypothesis Testing and A/B Testing – Understand t-tests and p values
  • Natural Langauge Processing – Summarize Reviews, Sentiment Analysis on Airline Tweets & Spam Detection
  • To use Google Colab’s iPython notebooks for fast, relaible cloud based data science work
  • Deploy your Machine Learning Models on the cloud using AWS
  • Advanced Pandas techniques from Vectorizing to Parallel Processsng
  • Statistical Theory, Probability Theory, Distributions, Exploratory Data Analysis
  • Predicting Employee Churn, Insurance Premiums, Airbnb prices, credit card fraud and who to target for donations
  • Big Data skills using PySpark for Data Manipulation and Machine Learning
  • Cluster customers based on Exploratory Data Analysis, then using K-Means to detect customer segments
  • Build a Stock Trading Bot using re-inforement learning
  • Apply Data Science & Analytics to Retail, performing segementation, analyzing trends, determining valuable customers and more!
Table of Contents

Course Introduction – Why Businesses NEED Data Scientists more than ever!
1 Introduction – Why do this course Why Apply Data Science to Business
2 Why Data is the new Oil and what most Businesses are doing wrong
3 Defining Business Problems for Analytic Thinking & Data Driven Decision Making
4 Analytic Mindset
5 Data Science Projects every Business should do!
6 Making Sense of Buzz Words, Data Science, Big Data, Machine & Deep Learning
7 How Deep Learning is Changing Everything!
8 The Roles in the Data World – Analyst, Engineer, Scientist, Statistician, DevOps
9 How Data Scientists Approach Problems

Course Setup & Pathways
10 Course Approach – Different Options for Different Students
11 Setup Google Colab for your iPython Notebooks

Python – A Crash Course
12 Why use Python for Data Science
13 Python – Basic Variables
14 Python – Variables (Lists and Dictionaries)
15 Python – Conditional Statements
16 Python – Loops
17 Python – Functions
18 Python – Classes

Pandas – Beginner to Advanvced
19 Introduction to Pandas
20 Pandas 1 – Data Series
21 Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering
22 Pandas 2B – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering
23 Pandas 3A – Data Cleaning – Alter ColomnsRows, Missing Data & String Operations
24 Pandas 3B – Data Cleaning – Alter ColomnsRows, Missing Data & String Operations
25 Pandas 4 – Data Aggregation – GroupBy, Map, Pivot, Aggreate Functions
26 Pandas 5 – Feature Engineer, Lambda and Apply
27 Pandas 6 – Concatenating, Merging and Joinining
28 Pandas 7 – Time Series Data
29 Pandas 7 – ADVANCED Operations – Iterows, Vectorization and Numpy
30 Pandas 8 – ADVANCED Operations – More Map, Zip and Apply
31 Pandas 9 – ADVANCED Operations – Parallel Processing
32 Map Visualizations with Plotly – Cloropeths from Scratch – USA and World
33 Map Visualizations with Plotly – Heatmaps, Scatter Plots and Lines

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

Probability Theory
50 Probability – An Introduction
51 Estimating Probability
52 Addition Rule
53 Permutations & Combinations
54 Bayes Theorem

Hypothesis Testing
55 Hypothesis Testing Introduction
56 Statistical Significance
57 Hypothesis Testing – P Value
58 Hypothesis Testing – Pearson Correlation

Machine Learning – Regressions, Classifications and Assessing Performance
59 Introduction to Machine Learning
60 How Machine Learning enables Computers to Learn
61 What is a Machine Learning Model
62 Types of Machine Learning
63 Linear Regression – Introduction to Cost Functions and Gradient Descent
64 Linear Regressions in Python from Scratch and using Sklearn
65 Polynomial and Multivariate Linear Regression
66 Logistic Regression
67 Support Vector Machines (SVMs)
68 Assessing Performance – Confusion Matrix, Precision and Recall
69 Understanding the ROC and AUC Curve
70 Decision Trees and Random Forests & the Gini Index
71 K-Nearest Neighbors (KNN)
72 What Makes a Good Model Regularization, Overfitting, Generalization & Outliers
73 Introduction to Neural Networks
74 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs

Deep Learning in Detail
75 Neural Networks Chapter Overview
76 Machine Learning Overview
77 Neural Networks Explained
78 Forward Propagation
79 Activation Functions
80 Training Part 1 – Loss Functions
81 Training Part 2 – Backpropagation and Gradient Descent
82 Backpropagation & Learning Rates – A Worked Example
83 Regularization, Overfitting, Generalization and Test Datasets
84 Epochs, Iterations and Batch Sizes
85 Measuring Performance and the Confusion Matrix
86 Review and Best Practices

Case Study 1 – Figuring Out Which Employees May Quit – Retention Analysis
87 Understanding the Problem + Exploratory Data Analysis & Visualizations
88 Data Cleaning and Preparation
89 Machine Learning Modeling + Deep Learning

Case Study 2 – Figuring Out Which Customers May Leave – Churn Analysis
90 Understanding the Problem
91 Exploratory Data Analysis & Visualizations
92 Data Preprocessing
93 Machine Learning Modeling + Deep Learning

Case Study 3 – Who Do We Target For Donations Finding the highest incomes
94 Understanding the Problem
95 Exploratory Data Analysis and Visualizations
96 Preparing our Dataset for Machine Learning
97 Modeling using Grid Search for finding the best parameters

Case Study 4 – Predicting Insurance Premiums
98 Understanding the Problem + Exploratory Data Analysis and Visualizations
99 Data Preparation and Machine Learning Modeling

Case Study 5 – Predicting Airbnb Prices
100 Understanding the Problem + Exploratory Data Analysis and Visualizations
101 Machine Learning Modeling
102 Using our Model for Value Estimation for New Clients

Case Study 6 – Credit Card Fraud Detection
103 Problem and Plan of Attack

Case Study 7 – Analyzing Conversion Rates of Marketing Campaigns
104 Exploratory Analysis of Understanding Marketing Conversion Rates

Case Study 8 – Predicting Engagement – What drives ad performance
105 Understanding the Problem + Exploratory Data Analysis and Visualizations
106 Data Preparation and Machine Learning Modeling

Case Study 9 – AB Testing (Optimizing Ads)
107 Understanding the Problem + Exploratory Data Analysis and Visualizations
108 AB Test Result Analysis
109 AB Testing a Worked Real Life Example – Designing an AB Test
110 Statistical Power and Significance
111 Analysis of AB Test Resutls

Case Study 10 – Product Analytics (Exploratory Data Analysis)
112 Problem and Plan of Attack
113 Sales and Revenue Analysis
114 Analysis per Country, Repeat Customers and Items

Case Study 11 – Determine Your Best Customers & Customer Lifetime Values
115 Understanding the Problem + Exploratory Data Analysis and Visualizations
116 Customer Lifetime Value Modeling

Clustering – Unsupervised Learning
117 Introdution to Unsupervised Learning
118 K-Means Clustering
119 Choosing K – Elbow Method & Silhouette Analysis
120 K-Means in Python – Choosing K using the Elbow Method & Silhoutte Analysis
121 Agglomerative Hierarchical Clustering
122 Mean-Shift Clustering
123 DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
124 DBSCAN in Python
125 Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

Dimensionality Reduction
126 Principal Component Analysis
127 t-Distributed Stochastic Neighbor Embedding (t-SNE)
128 PCA & t-SNE in Python with Visualization Comparisons

Case Study 12 – Customer Clustering (K-means, Hierarchial)
129 Data Exploration & Description
130 Simple Exploratory Data Analysis and Visualizations
131 Feature Engineering
132 K-Means Clustering of Customer Data
133 Cluster Analysis

Recommendation Systems Theory
134 Introduction to Recommendation Engines
135 Before recommending, how do we rate or review Items Thought Experiment
136 User Collaborative Filtering and ItemContent-based Filtering
137 The Netflix Prize, Matrix Factorization & Deep Learning as Latent-Factor Methods

Case Study 13 – Build a Product Recommendation System
138 Dataset Description and Data Cleaning
139 Making a Customer-Item Matrix
140 User-User Matrix – Getting Recommended Items for each Customer
141 Item-Item Collaborative Filtering – Finding the Most Similar Items

Case Study 14 – Use LightFM to Build a Movie Recommendation System
142 Plan and Approach

Natural Language Processing an Introduction
143 Introduction to Natural Language Processing
144 Modeling Language – The Bag of Words Model
145 Normalization, Stop Word Removal, LemmatizingStemming
146 TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency)
147 Word2Vec – Efficient Estimation of Word Representations in Vector Space

Case Study 15 – Summarizing Amazon Reviews
148 Problem and Plan of Attack

Case Study 16 – Sentiment Analysis of Airline Tweets
149 Problem and Plan of Attack

Case Study 17 – Spam Filter
150 Problem and Plan of Attack

Case Study 18 – Demand Forecasting with Facebook’s Prophet
151 Problem and Plan of Attack

Case Study 19 – Stock Trading using Reinforcement Learning
152 Using Q-Learning and Reinforcement Learning to Build a Trading Bot

Case Study 20 – Headline Classification in PySpark
153 Using PySpark for Headline Classification

Data Science in Production – Deploying on the Cloud (AWS)
154 Install and Run Flask
155 Running Your Computer Vision Web App on Flask Locally
156 Running Your Computer Vision API
157 Setting Up An AWS Account
158 Setting Up Your AWS EC2 Instance & Installing Keras, TensorFlow, OpenCV & Flask
159 Changing your EC2 Security Group
160 Using FileZilla to transfer files to your EC2 Instance
161 Running your CV Web App on EC2
162 Running your CV API on EC2

BONUS – Customer Life Time Values using the BGNBD and the Gamma-Gamma Model
163 Customer Lifetime Value Modeling using lifetimes

BONUS – Price Optimization of Airline Tickets
164 Price Optimization of Airline Tickets

BONUS – Convolution Neural Networks
165 Convolutional Neural Networks Chapter Overview
166 Convolutional Neural Networks Introduction
167 Convolutions & Image Features
168 Depth, Stride and Padding
169 ReLU
170 Pooling
171 The Fully Connected Layer
172 Training CNNs
173 Design Your Own CNN
174 Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs – Promo
175 Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs – Introduction