Data Science & Deep Learning for Business™ 20 Case Studies

Use Python to solve problems in Retail, Marketing, Product Recommendation, Customer Clustering, NLP, Forecasting & more!

4.14 (1020 reviews)
Udemy
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English
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Data & Analytics
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instructor
11,295
students
21 hours
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Nov 2021
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$69.99
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What you will 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

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:

  1. How Data Science and Solve Many Common Business Problems

  2. The Modern Tools of a Data Scientist - Python, Pandas, Scikit-learn, Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).

  3. Statistics for Data Science in Detail - Sampling, Distributions, Normal Distribution, Descriptive Statistics, Correlation and Covariance, Probability Significance Testing and Hypothesis Testing.

  4. Machine Learning Theory - Linear Regressions, Logistic Regressions, Decision Trees, Random Forests, KNN, SVMs, Model Assessment, Outlier Detection, ROC & AUC and Regularization

  5. Deep Learning Theory and Tools - TensorFlow 2.0 and Keras (Neural Nets, CNNs, RNNs & LSTMs)

  6. Solving problems using Predictive Modeling, Classification, and Deep Learning

  7. Data Science in Marketing - Modeling Engagement Rates and perform A/B Testing

  8. Data Science in Retail - Customer Segmentation, Lifetime Value, and Customer/Product Analytics

  9. Unsupervised Learning - K-Means Clustering, PCA, t-SNE, Agglomerative Hierarchical, Mean Shift, DBSCAN and E-M GMM Clustering

  10. Recommendation Systems - Collaborative Filtering and Content-based filtering + Learn to use LiteFM

  11. Natural Language Processing - Bag of Words, Lemmatizing/Stemming, TF-IDF Vectorizer, and Word2Vec

  12. 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)

  13. 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:

  1. Figuring Out Which Employees May Quit (Retention Analysis)

  2. Figuring Out Which Customers May Leave (Churn Analysis)

  3. Who do we target for Donations?

  4. Predicting Insurance Premiums

  5. Predicting Airbnb Prices

  6. Detecting Credit Card Fraud

Four (4) Data Science in Marketing Case Studies:

  1. Analyzing Conversion Rates of Marketing Campaigns

  2. Predicting Engagement - What drives ad performance?

  3. A/B Testing (Optimizing Ads)

  4. Who are Your Best Customers? & Customer Lifetime Values (CLV)

Four (4) Retail Data Science Case Studies:

  1. Product Analytics (Exploratory Data Analysis Techniques

  2. Clustering Customer Data from Travel Agency

  3. Product Recommendation Systems - Ecommerce Store Items

  4. Movie Recommendation System using LiteFM

Two (2) Time-Series Forecasting Case Studies:

  1. Sales Forecasting for a Store

  2. Stock Trading using Re-Enforcement Learning

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

  1. Summarizing Reviews

  2. Detecting Sentiment in text

  3. Spam Filters

One (1) PySpark Big  Data Case Studies:

  1. 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!

Content

Course Introduction - Why Businesses NEED Data Scientists more than ever!

Introduction - Why do this course? Why Apply Data Science to Business?
Why Data is the new Oil and what most Businesses are doing wrong
Defining Business Problems for Analytic Thinking & Data Driven Decision Making
Analytic Mindset
10 Data Science Projects every Business should do!
Making Sense of Buzz Words, Data Science, Big Data, Machine & Deep Learning
How Deep Learning is Changing Everything!
The Roles in the Data World - Analyst, Engineer, Scientist, Statistician, DevOps
How Data Scientists Approach Problems

Course Setup & Pathways - DOWNLOAD RESOURCES HERE

Course Approach - Different Options for Different Students
Setup Google Colab for your iPython Notebooks (Download Course Code + Slides)
Download Code, Slides and Datasets

Python - A Crash Course

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

Pandas - Beginner to Advanvced

Introduction to Pandas
Pandas 1 - Data Series
Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty cells & Filtering
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 7 - ADVANCED Operations - Iterows, Vectorization and Numpy
Pandas 8 - ADVANCED Operations - More Map, Zip and Apply
Pandas 9 - ADVANCED Operations - Parallel Processing
Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
Map Visualizations with Plotly - Heatmaps, Scatter Plots and Lines

Statistics & Probability for Data Scientists

Introdution 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

Probability – An Introduction
Estimating Probability
Addition Rule
Permutations & Combinations
Bayes Theorem

Hypothesis Testing

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

Machine Learning - Regressions, Classifications and Assessing Performance

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 in Detail

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

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

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

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

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

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

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

Case Study 4 - Predicting Insurance Premiums

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

Case Study 5 - Predicting Airbnb Prices

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

Case Study 6 – Credit Card Fraud Detection

Problem and Plan of Attack

Case Study 7 – Analyzing Conversion Rates of Marketing Campaigns

Exploratory Analysis of Understanding Marketing Conversion Rates

Case Study 8 – Predicting Engagement - What drives ad performance?

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

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

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

Case Study 10 – Product Analytics (Exploratory Data Analysis)

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

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

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

Clustering – Unsupervised Learning

Introdution to Unsupervised Learning
K-Means Clustering
Choosing K – Elbow Method & Silhouette Analysis
K-Means in Python - Choosing K using the Elbow Method & Silhoutte Analysis
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

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

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

Recommendation Systems Theory

Introduction to Recommendation Engines
Before recommending, how do we rate or review Items? Thought Experiment
User Collaborative Filtering and Item/Content-based Filtering
The Netflix Prize, Matrix Factorization & Deep Learning as Latent-Factor Methods

Case Study 13 – Build a Product Recommendation System

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

19.1 Case Study 14 - Use LightFM to Build a Movie Recommendation System

Plan and Approach

Natural Language Processing an Introduction

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

Case Study 15 – Summarizing Amazon Reviews

Problem and Plan of Attack

Case Study 16 – Sentiment Analysis of Airline Tweets

Problem and Plan of Attack

Case Study 17 - Spam Filter

Problem and Plan of Attack

Case Study 18 – Demand Forecasting with Facebook's Prophet

Problem and Plan of Attack

Case Study 19 – Stock Trading using Reinforcement Learning

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

Big Data Introduction

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

Case Study 20 - Headline Classification in PySpark

Using PySpark for Headline Classification

Data Science in Production - Deploying on the Cloud (AWS)

Install and Run Flask
Running Your Computer Vision Web App on Flask Locally
Running Your Computer Vision API
Setting Up An AWS Account
Setting Up Your AWS EC2 Instance & Installing Keras, TensorFlow, OpenCV & Flask
Changing your EC2 Security Group
Using FileZilla to transfer files to your EC2 Instance
Running your CV Web App on EC2
Running your CV API on EC2

BONUS - Customer Life Time Values using the BG/NBD and the Gamma-Gamma Model

Customer Lifetime Value (CLV) Theory
Buy-til-you-die (BTYD) models
Customer Lifetime Value Modeling using lifetimes

BONUS - Price Optimization of Airline Tickets

Price Optimization of Airline Tickets

BONUS - Convolution Neural Networks

Convolutional Neural Networks Chapter Overview
Convolutional Neural Networks Introduction
Convolutions & Image Features
Depth, Stride and Padding
ReLU
Pooling
The Fully Connected Layer
Training CNNs
Design Your Own CNN
Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs - Promo
Deep Learning Computer Vision™ CNN, OpenCV, YOLO, SSD & GANs - Introduction

Screenshots

Data Science & Deep Learning for Business™ 20 Case Studies - Screenshot_01Data Science & Deep Learning for Business™ 20 Case Studies - Screenshot_02Data Science & Deep Learning for Business™ 20 Case Studies - Screenshot_03Data Science & Deep Learning for Business™ 20 Case Studies - Screenshot_04

Reviews

Benjamin
June 14, 2023
I really like the idea of this class, and it has what I would consider ok information in it. However all the "Case Studies" as far as I could tell, are publicly available notebooks created by other people on sites like Kaggle. I don't have an issue with this necessarily (i.e. aggregating sources and presenting them is still legitimately helpful), however the creator of this course doesn't really do any vetting on whether these are quality notebooks or not. This I do take issue with, since this could lead to early learners getting odd ideas about how to approach these problems. A case study should show you how an "expert" approaches a problem. One of example of this is found in the case study on customer Churn where a neural network is used with something like 2,000 neurons in the first hidden layer for a dataset that only has ~8,000 entries and 40 features.
BARTHI
December 6, 2022
I did not expect that this much content would be covered in this case studies tutorial. Quite satisfied with the lecture.
Naga
May 25, 2022
Looks cool. I am enjoying the learning so far. Thanks for putting all of the coding together. Helps a lot.
Vikas
May 21, 2022
Course is good but I am giving 3 star due to 2 deficiency in the course video. 1st is voice is very low sometimes can not hear what he said, due to that i am watching video repeatedly 2nd is unnecessary scrolling up and down and each case study video. that creates lots of headache while watching the video.
Tshiabu
May 18, 2022
The course is more and more interesting as we move on, but sometimes you are fast. But, I do understand more of the section washing the recording it just need more practice
Thamizharasi
May 13, 2022
very nice and course includes very interactive presentation like picture, diagrams etc, along with teaching making it easier to understand.Thank you so much to the author for this wonderful course
Deepika
March 6, 2022
The Course covers good number of use cases, but they are not very detailed drilled into, like understanding the outcome of models and which features are impacting the result, over all it is a good course for a beginner to get an idea of what all possible use cases can be handled using DS and DL
Monica
October 19, 2021
I have never before understood probability and statistics with such clarity before. I went into this course thinking it would give me cool projects to practice machine learning with but I've come out understanding everything I never understood in my many years of prob and stat courses!
Helena
September 11, 2021
Course was a good match. Received some responses to my questions but still have no more than two fuzzy areas about concepts. Learnt or got reinforcement on a few concepts. Appreciated.
Clara
September 3, 2021
In the middle of the course we stopped getting videos. Although the notebooks are quite comprehensive, I feel like I needed the accompanying videos to walk me through the lessons. Especially because the latter lessons with the more interesting (in my opinion) case studies are the ones missing the commentary. The slides are good, but I would prefer if you were explaining things further when showcasing slides, instead of just reading what's there. Overall is a course with good material, but the presentation of the material could be improved.
Carlos
May 29, 2021
Excellent. I didn't want yet another course teaching me the basics, I needed a portfolio of tools to use in my daily work and this is the closest I've found. Although broad and superficial at times (every single chapter can have it's own course with different case studies), this is exactly the kind of course one needs more of - less of a tutorial than a real hands-on exercise. Hopefully the author makes many more, diving in on particular subjects (although that would seem hard for him to do alone, to pass from the basics to the complex in a subject one needs true industry expertise...hopefully a colab can help?). Great job.
Karan
April 29, 2021
The instructor is not clear in his approach and does not explains things fully For example at one instance he says "cross tab is little difficult to understand" and he did not even explain it afterwards . Most of the times he is just running through the pre-written lines of codes without proper explanation.
Scott
March 2, 2021
I'm wanting to better my data science skills to find and present ideas for cost savings and for revenue growth.
Krisna
December 30, 2020
Great introduction. Showing all the content I would expect form this kind of course. I could use a bit of a clearer and louder voice but that's a really minor thing.
Yang-Sheng
December 17, 2020
Instructor doesn't seem to know that as an instructor, his objective is to communicate knowledge and insight to the student. Often, he mumbles or talks as if he is his only intended audience. He is also very prone to being "mouse-happy" (derived from "trigger-happy), scrolling his computer screen up/down/left/right very quickly and with no clear objective. With online classes such as these, screen motion should be deliberate--everywhere the screen scrolls to must have a purpose: 1. the instructor should explain what on that screen he is looking for and evaluating, and 2. the instructor should stay long enough on that screen for the student to read what is on the screen and tie that in with the instructor's message.

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