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

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

4.57 (454 reviews)
Udemy
platform
English
language
Data & Analytics
category
instructor
4,431
students
30.5 hours
content
Nov 2021
last update
$69.99
regular price

What you will learn

Pandas to become a Data Analytics & Data Wrangling Whiz ensuring Data Quality

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.

Description

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:

  1. Using Data Science to Solve Common Business Problems

  2. The Modern Tools of a Data Scientist - Python, Pandas, Scikit-learn, NumPy, Keras, prophet, statsmod, scipy and more!

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

  4. Visualization Theory for Data Science and Analytics using Seaborn, Matplotlib & Plotly (Manipulate Data and Create Information Captivating Visualizations and Plots).

  5. Dashboard Design using Google Data Studio

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

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

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

  9. Data Analysis and Statistical Case Studies - Solve and analyze real-world problems and datasets.

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

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

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

  13. Recommendation Systems - Collaborative Filtering and Content-based filtering + Learn to use LiteFM  + Deep Learning Recommendation Systems

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

  15. 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)

  16. 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:

  1. Predicting the US 2020 Election using multiple Polling Datasets

  2. Predicting Diabetes Cases from Health Data

  3. Market Basket Analysis using the Apriori Algorithm

  4. Predicting the Football/Soccer World Cup

  5. Covid Analysis and Creating Amazing Flourish Visualisations (Barchart Race)

  6. Analyzing Olympic Data

  7. Is Home Advantage Real in Soccer or Basketball?

  8. IPL Cricket Data Analysis

  9. Streaming Services (Netflix, Hulu, Disney Plus and Amazon Prime) - Movie Analysis

  10. Pizza Restaurant Analysis - Most Popular Pizzas across the US

  11. Micro Brewery and Pub Analysis

  12. Supply Chain Analysis

  13. Indian Election Analysis

  14. Africa Economic Crisis Analysis

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

  3. Brent Oil Price Forecasting

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

  1. Summarizing Reviews

  2. Detecting Sentiment in text

  3. Spam Detection

One (1) PySpark Big  Data Case Studies:

  1. News Headline Classification

One (1) Deployment Project:

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

Content

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

Introduction to Python

Why use Python for Data Science?
Python Introduction - Part 1 - Variables
Python - Variables (Lists and Dictionaries)
Python - Conditional Statements
More information on elif
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 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 - Iterows, Vectorization and Numpy
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 – Elbow Method & Silhouette Analysis
K-Means in Python - Choosing K using the Elbow Method & Silhoutte Analysis
Agglomerative Hierarchical 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

Screenshots

Data Science, Analytics & AI for Business & the Real World™ - Screenshot_01Data Science, Analytics & AI for Business & the Real World™ - Screenshot_02Data Science, Analytics & AI for Business & the Real World™ - Screenshot_03Data Science, Analytics & AI for Business & the Real World™ - Screenshot_04

Reviews

Seyi
September 18, 2023
I am happy that I purchased this course. It took me a lot of time searching in Udemy to finally arrive at this course, which happens to be the most comprehensive among its peers. With so many projects to handle in this course coupled with other learning, it's obviously a great buy at little or 'no' price paid for this course.
Grzegorz
August 27, 2023
Perfectly explained everything. After going through 1/4 of the course, I think it deserves a maximum rating. I only hope that the author will maintain such a high level....
Alanis
August 21, 2023
Exciting all the expectation created, I hope it will be fulfilled both on my part facing this rest, as well as the course offered
Mike
April 22, 2023
So far so good. Rajeev does a great job describing the course content and gets the student excited for what's to come. However, not editing out mistakes makes this course feel less polished and professional.
Armando
February 2, 2023
I love this class an simple to understand the process of learning this tremendous tool. I'm really happy because I bought this course even long time ago however is time to start learning now!!!
Carlos
November 20, 2022
I bought this course only for the projects, I see a litte bit from the first part so I can't make a full coment from the theory part. For the projects the instructor doesn't explain only reads the screen. Lucky to me I only see the Section 20 World Cup Prediction and is the same as these project https://www.kaggle.com/code/agostontorok/soccer-world-cup-2018-winner, Probably there are a lot more of Kaggle copies. I bought yesterday the course, so I'm going request the refund to Udemy.
Djordje
August 21, 2022
DISCLAIMER: I already know statistics and Python required for this course, I just came here for case studies. My feedback is solely about that (starting at Section 17) There is some useful info here. However, most of the time the instructor just reads code without explaining his train of thought. I bought this course to learn how data analysts approach problems, but he just reads code assuming we know everything. He also uses such code that pandas often give warnings and makes a lot of mistakes while speaking. All in all, I learned something out of this course, I didn't waste my money completely, but don't expect that you will learn much.
Marcel
May 21, 2022
I really like the unique compilation of business and real-world examples. As I'm working in a business environment, the course content matched pretty much the case studies I was looking for. Unfortunately, the teacher is scrolling a lot without a reason and there's some frenetic cursor movement which makes it hard to focus on the content. If this would be my really first course on Data Science I'm afraid I would get lost due to the "hectic" screen motions, but it's OK to grasp some refreshers and business-specific details. It'd be also appreciated if coughing and other annoying sounds would have been removed since it is a bit unpleasant while following the course for a longer time. Good and well-structured content, but teaching style needs some improvement which will be hopefully considered for any updates.
Alireza
April 22, 2022
The teacher, unfortunately, burps a lot such that it makes it very unpleasant to listen and watch the course.
Rabin
September 30, 2021
Wants to hone skills how to leverage data science and AI and ML knowledge , particularlt the applications . so far ir matched my expectation
Evelien
September 13, 2021
I noticed some parts were the teacher could have done some cutting in the videos. "Wait, let me start over." "I will do this again." etc. Would come across more professional. (So far everything on the information is clear)
Duncan
August 13, 2021
There's a lot of great content in this course but don't expect in depth explanations of any of it. It covers a lot of what you need to know, but just the surface level. The whole probability section was 20 minutes - doesn't really seem like he understands Bayes Theorem himself. That said, all of the project examples are really great resources to have as reference points for certain code blocks.
Lil
July 1, 2021
It appears it would be great, so far trainers voice clarity is superb and introduction to course is seamless
Proshanto
June 24, 2021
Can i raise questions or doubts? How? Q1. what is the purpose of putting "self" Q2. what does three quotes """ mean.
George
June 9, 2021
This instructor's lectures are better than other Udemy courses I've taken, but there are no introductory exercises that I can see. (For a better introduction for Python, stats, etc. check out 365 Careers' Data Science Course 2021). I do like using Colab in this course, and the instructor's videos are easier to follow along than some of the other Udemy courses. Very often the instructor makes references to real-life scenarios and imparts wisdom and recommendations about data science, which is very welcome for newbies like me. Overall, the instructor's notebooks are easy to follow, he's very knowledgeable and his delivery style better than most. Well done!

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3588918
udemy ID
10/23/2020
course created date
11/11/2020
course indexed date
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course submited by