Data Science Deep Learning for Business 20 Case Studies

Data Science Deep Learning for Business 20 Case Studies

What you’ll learn

Understand the value of data for business
Solve common business problems in Marketing, Sales, Customer Clustering, Banking, Real Estate, Insurance, Travel and more!
Python, Pandas, Matplotlib & Seaborn, SkLearn, Keras, Tensorflow, NLTK, Prophet, PySpark, MLLib and more!
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!
How to apply Data Science in Marketing to improve Conversion Rates, Predict Engagement and Customer Life Time Value

Requirements

Familiar with basic programming concepts
Highschool level math knowledge
Broadband Internet connection

Description

Welcome to the course on Data Science & Deep Learning for Business™ 20 Case Studies!This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies.  Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade!What student reviews of this course are saying, “I’m only half way through this course, but i have to say WOW. It’s so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it’s broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! 6 stars out of 5!””It is pretty different in format, from others. The appraoch taken here is an end-to-end hands-on project execution, while introducing the concepts. A learner with some prior knowledge will definitely feel at home and get to witness the thought process that happens, while executing a real-time project. The case studies cover most of the domains, that are frequently asked by companies. So it’s pretty good and unique, from what i have seen so far. Overall Great learning and great content.”–“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 ProblemsThe 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 RegularizationDeep Learning Theory and Tools – TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs & LSTMs)Solving problems using Predictive Modeling, Classification, and Deep LearningData Science in Marketing – Modeling Engagement Rates and perform A/B TestingData Science in Retail – Customer Segmentation, Lifetime Value, and Customer/Product AnalyticsUnsupervised Learning – K-Means Clustering, PCA, t-SNE, Agglomerative Hierarchical, Mean Shift, DBSCAN and E-M GMM ClusteringRecommendation Systems – Collaborative Filtering and Content-based filtering + Learn to use LiteFM Natural Language Processing – Bag of Words, Lemmatizing/Stemming, TF-IDF Vectorizer, and Word2VecBig 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 APIOur 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 PremiumsPredicting Airbnb PricesDetecting Credit Card FraudFour (4) Data Science in Marketing Case Studies:Analyzing Conversion Rates of Marketing CampaignsPredicting 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 TechniquesClustering Customer Data from Travel AgencyProduct Recommendation Systems – Ecommerce Store ItemsMovie Recommendation System using LiteFMTwo (2) Time-Series Forecasting Case Studies:Sales Forecasting for a StoreStock Trading using Re-Enforcement LearningThree (3) Natural Langauge Processing (NLP) Case Studies:Summarizing ReviewsDetecting Sentiment in textSpam FiltersOne (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.–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. Get a head start applying these techniques to all types of Businesses by taking this course!

Overview

Section 1: Course Introduction – Why Businesses NEED Data Scientists more than ever!

Lecture 1 Introduction – Why do this course? Why Apply Data Science to Business?

Lecture 2 Why Data is the new Oil and what most Businesses are doing wrong

Lecture 3 Defining Business Problems for Analytic Thinking & Data Driven Decision Making

Lecture 4 Analytic Mindset

Lecture 5 10 Data Science Projects every Business should do!

Lecture 6 Making Sense of Buzz Words, Data Science, Big Data, Machine & Deep Learning

Lecture 7 How Deep Learning is Changing Everything!

Lecture 8 The Roles in the Data World – Analyst, Engineer, Scientist, Statistician, DevOps

Lecture 9 How Data Scientists Approach Problems

Section 2: Course Setup & Pathways – DOWNLOAD RESOURCES HERE

Lecture 10 Course Approach – Different Options for Different Students

Lecture 11 Setup Google Colab for your iPython Notebooks (Download Course Code + Slides)

Lecture 12 Download Code, Slides and Datasets

Section 3: Python – A Crash Course

Lecture 13 Why use Python for Data Science?

Lecture 14 Python – Basic Variables

Lecture 15 Python – Variables (Lists and Dictionaries)

Lecture 16 Python – Conditional Statements

Lecture 17 More information on elif

Lecture 18 Python – Loops

Lecture 19 Python – Functions

Lecture 20 Python – Classes

Section 4: Pandas – Beginner to Advanvced

Lecture 21 Introduction to Pandas

Lecture 22 Pandas 1 – Data Series

Lecture 23 Pandas 2A – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering

Lecture 24 Pandas 2B – DataFrames – Index, Slice, Stats, Finding Empty cells & Filtering

Lecture 25 Pandas 3A – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 26 Pandas 3B – Data Cleaning – Alter Colomns/Rows, Missing Data & String Operations

Lecture 27 Pandas 4 – Data Aggregation – GroupBy, Map, Pivot, Aggreate Functions

Lecture 28 Pandas 5 – Feature Engineer, Lambda and Apply

Lecture 29 Pandas 6 – Concatenating, Merging and Joinining

Lecture 30 Pandas 7 – Time Series Data

Lecture 31 Pandas 7 – ADVANCED Operations – Iterows, Vectorization and Numpy

Lecture 32 Pandas 8 – ADVANCED Operations – More Map, Zip and Apply

Lecture 33 Pandas 9 – ADVANCED Operations – Parallel Processing

Lecture 34 Map Visualizations with Plotly – Cloropeths from Scratch – USA and World

Lecture 35 Map Visualizations with Plotly – Heatmaps, Scatter Plots and Lines

Section 5: Statistics & Probability for Data Scientists

Lecture 36 Introdution to Statistics

Lecture 37 Descriptive Statistics – Why Statistical Knowledge is so Important

Lecture 38 Descriptive Statistics 1 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 39 Descriptive Statistics 2 – Exploratory Data Analysis (EDA) & Visualizations

Lecture 40 Sampling, Averages & Variance And How to lie and Mislead with Statistics

Lecture 41 Sampling – Sample Sizes & Confidence Intervals – What Can You Trust?

Lecture 42 Types of Variables – Quantitive and Qualitative

Lecture 43 Frequency Distributions

Lecture 44 Frequency Distributions Shapes

Lecture 45 Analyzing Frequency Distributions – What is the Best Type of WIne? Red or White?

Lecture 46 Mean, Mode and Median – Not as Simple As You’d Think

Lecture 47 Variance, Standard Deviation and Bessel’s Correction

Lecture 48 Covariance & Correlation – Do Amazon & Google know you better than anyone else?

Lecture 49 Lying with Correlations – Divorce Rates in Maine caused by Margarine Consumption

Lecture 50 The Normal Distribution & the Central Limit Theorem

Lecture 51 Z-Scores

Section 6: Probability Theory

Lecture 52 Probability – An Introduction

Lecture 53 Estimating Probability

Lecture 54 Addition Rule

Lecture 55 Permutations & Combinations

Lecture 56 Bayes Theorem

Section 7: Hypothesis Testing

Lecture 57 Hypothesis Testing Introduction

Lecture 58 Statistical Significance

Lecture 59 Hypothesis Testing – P Value

Lecture 60 Hypothesis Testing – Pearson Correlation

Section 8: Machine Learning – Regressions, Classifications and Assessing Performance

Lecture 61 Introduction to Machine Learning

Lecture 62 How Machine Learning enables Computers to Learn

Lecture 63 What is a Machine Learning Model?

Lecture 64 Types of Machine Learning

Lecture 65 Linear Regression – Introduction to Cost Functions and Gradient Descent

Lecture 66 Linear Regressions in Python from Scratch and using Sklearn

Lecture 67 Polynomial and Multivariate Linear Regression

Lecture 68 Logistic Regression

Lecture 69 Support Vector Machines (SVMs)

Lecture 70 Decision Trees and Random Forests & the Gini Index

Lecture 71 K-Nearest Neighbors (KNN)

Lecture 72 Assessing Performance – Confusion Matrix, Precision and Recall

Lecture 73 Understanding the ROC and AUC Curve

Lecture 74 What Makes a Good Model? Regularization, Overfitting, Generalization & Outliers

Lecture 75 Introduction to Neural Networks

Lecture 76 Types of Deep Learning Algoritms CNNs, RNNs & LSTMs

Section 9: Deep Learning in Detail

Lecture 77 Neural Networks Chapter Overview

Lecture 78 Machine Learning Overview

Lecture 79 Neural Networks Explained

Lecture 80 Forward Propagation

Lecture 81 Activation Functions

Lecture 82 Training Part 1 – Loss Functions

Lecture 83 Training Part 2 – Backpropagation and Gradient Descent

Lecture 84 Backpropagation & Learning Rates – A Worked Example

Lecture 85 Regularization, Overfitting, Generalization and Test Datasets

Lecture 86 Epochs, Iterations and Batch Sizes

Lecture 87 Measuring Performance and the Confusion Matrix

Lecture 88 Review and Best Practices

Section 10: Case Study 1 – Figuring Out Which Employees May Quit – Retention Analysis

Lecture 89 Figuring Out Which Employees May Quit –Understanding the Problem & EDA

Lecture 90 Data Cleaning and Preparation

Lecture 91 Machine Learning Modeling + Deep Learning

Section 11: Case Study 2 – Figuring Out Which Customers May Leave – Churn Analysis

Lecture 92 Understanding the Problem

Lecture 93 Exploratory Data Analysis & Visualizations

Lecture 94 Data Preprocessing

Lecture 95 Machine Learning Modeling + Deep Learning

Section 12: Case Study 3 – Who Do We Target For Donations? Finding the highest incomes

Lecture 96 Understanding the Problem

Lecture 97 Exploratory Data Analysis and Visualizations

Lecture 98 Preparing our Dataset for Machine Learning

Lecture 99 Modeling using Grid Search for finding the best parameters

Section 13: Case Study 4 – Predicting Insurance Premiums

Lecture 100 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 101 Data Preparation and Machine Learning Modeling

Section 14: Case Study 5 – Predicting Airbnb Prices

Lecture 102 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 103 Machine Learning Modeling

Lecture 104 Using our Model for Value Estimation for New Clients

Section 15: Case Study 6 – Credit Card Fraud Detection

Lecture 105 Understanding our Dataset

Lecture 106 Exploratory Analysis

Lecture 107 Feature Extraction

Lecture 108 Creating and Validating Our Model

Section 16: Case Study 7 – Analyzing Conversion Rates of Marketing Campaigns

Lecture 109 Exploratory Analysis of Understanding Marketing Conversion Rates

Section 17: Case Study 8 – Predicting Engagement – What drives ad performance?

Lecture 110 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 111 Data Preparation and Machine Learning Modeling

Section 18: Case Study 9 – A/B Testing (Optimizing Ads)

Lecture 112 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 113 A/B Test Result Analysis

Lecture 114 A/B Testing a Worked Real Life Example – Designing an A/B Test

Lecture 115 Statistical Power and Significance

Lecture 116 Analysis of A/B Test Resutls

Section 19: Case Study 10 – Product Analytics (Exploratory Data Analysis)

Lecture 117 Problem and Plan of Attack

Lecture 118 Sales and Revenue Analysis

Lecture 119 Analysis per Country, Repeat Customers and Items

Section 20: Case Study 11 – Determine Your Best Customers & Customer Lifetime Values

Lecture 120 Understanding the Problem + Exploratory Data Analysis and Visualizations

Lecture 121 Customer Lifetime Value Modeling

Section 21: Clustering – Unsupervised Learning

Lecture 122 Introdution to Unsupervised Learning

Lecture 123 K-Means Clustering

Lecture 124 Choosing K – Elbow Method & Silhouette Analysis

Lecture 125 K-Means in Python – Choosing K using the Elbow Method & Silhoutte Analysis

Lecture 126 Agglomerative Hierarchical Clustering

Lecture 127 Mean-Shift Clustering

Lecture 128 DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

Lecture 129 DBSCAN in Python

Lecture 130 Expectation–Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

Section 22: Dimensionality Reduction

Lecture 131 Principal Component Analysis

Lecture 132 t-Distributed Stochastic Neighbor Embedding (t-SNE)

Lecture 133 PCA & t-SNE in Python with Visualization Comparisons

Section 23: Case Study 12 – Customer Clustering (K-means, Hierarchial)

Lecture 134 Data Exploration & Description

Lecture 135 Simple Exploratory Data Analysis and Visualizations

Lecture 136 Feature Engineering

Lecture 137 K-Means Clustering of Customer Data

Lecture 138 Cluster Analysis

Section 24: Recommendation Systems Theory

Lecture 139 Introduction to Recommendation Engines

Lecture 140 Before recommending, how do we rate or review Items? Thought Experiment

Lecture 141 User Collaborative Filtering and Item/Content-based Filtering

Lecture 142 The Netflix Prize, Matrix Factorization & Deep Learning as Latent-Factor Methods

Section 25: Case Study 13 – Build a Product Recommendation System

Lecture 143 Dataset Description and Data Cleaning

Lecture 144 Making a Customer-Item Matrix

Lecture 145 User-User Matrix – Getting Recommended Items for each Customer

Lecture 146 Item-Item Collaborative Filtering – Finding the Most Similar Items

Section 26: 19.1 Case Study 14 – Use LightFM to Build a Movie Recommendation System

Lecture 147 Plan and Approach

Section 27: Natural Language Processing an Introduction

Lecture 148 Introduction to Natural Language Processing

Lecture 149 Modeling Language – The Bag of Words Model

Lecture 150 Normalization, Stop Word Removal, Lemmatizing/Stemming

Lecture 151 TF-IDF Vectorizer (Term Frequency — Inverse Document Frequency)

Lecture 152 Word2Vec – Efficient Estimation of Word Representations in Vector Space

Section 28: Case Study 15 – Summarizing Amazon Reviews

Lecture 153 Problem and Plan of Attack

Section 29: Case Study 16 – Sentiment Analysis of Airline Tweets

Lecture 154 Problem and Plan of Attack

Section 30: Case Study 17 – Spam Filter

Lecture 155 Problem and Plan of Attack

Section 31: Case Study 18 – Demand Forecasting with Facebook’s Prophet

Lecture 156 Problem and Plan of Attack

Section 32: Case Study 19 – Stock Trading using Reinforcement Learning

Lecture 157 Reinforcement Learning an Introduction

Lecture 158 Using Q-Learning and Reinforcement Learning to Build a Trading Bot

Section 33: Big Data Introduction

Lecture 159 Introduction to Big Data

Lecture 160 Challenges in Big Data

Lecture 161 Hadoop, MapReduce and Spark

Lecture 162 Introduction to PySpark

Lecture 163 RDDs, Transformations, Actions, Lineage Graphs & Jobs

Lecture 164 Simple Data Cleaning in PySpark

Lecture 165 Machine Learning in PySpark

Section 34: Case Study 20 – Headline Classification in PySpark

Lecture 166 Using PySpark for Headline Classification

Section 35: Data Science in Production – Deploying on the Cloud (AWS)

Lecture 167 Install and Run Flask

Lecture 168 Running Your Computer Vision Web App on Flask Locally

Lecture 169 Running Your Computer Vision API

Lecture 170 Setting Up An AWS Account

Lecture 171 Setting Up Your AWS EC2 Instance & Installing Keras, TensorFlow, OpenCV & Flask

Lecture 172 Changing your EC2 Security Group

Lecture 173 Using FileZilla to transfer files to your EC2 Instance

Lecture 174 Running your CV Web App on EC2

Lecture 175 Running your CV API on EC2

Section 36: BONUS – Customer Life Time Values using the BG/NBD and the Gamma-Gamma Model

Lecture 176 Customer Lifetime Value (CLV) Theory

Lecture 177 Buy-til-you-die (BTYD) models

Lecture 178 Customer Lifetime Value Modeling using lifetimes

Section 37: BONUS – Price Optimization of Airline Tickets

Lecture 179 Price Optimization of Airline Tickets

Section 38: BONUS – Convolution Neural Networks

Lecture 180 Convolutional Neural Networks Chapter Overview

Lecture 181 Convolutional Neural Networks Introduction

Lecture 182 Convolutions & Image Features

Lecture 183 Depth, Stride and Padding

Lecture 184 ReLU

Lecture 185 Pooling

Lecture 186 The Fully Connected Layer

Lecture 187 Training CNNs

Lecture 188 Design Your Own CNN

Lecture 189 Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs – Promo

Lecture 190 Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs – Introduction

Begineers to Data Science,Business Analysts who wish to do more with their data,College graduates who lack real world experience,Business oriented persons (Management or MBAs) who’d like to use data to enhance their business,Software Developers or Engineers who’d like to start learning Data Science,Anyone looking to become more employable as a Data Scientist

Course Information:

Udemy | English | 21h 3m | 9.66 GB
Created by: Rajeev D. Ratan

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