Learn to use data science and statistics to solve business problems and gain insights into everyday problems with 35+ case studies
About This Video
Learn to use data science and statistics to solve business problems and gain insights into everyday problems with 35+ case studies
About This Video
In Detail
Right now, despite the Covid-19 economic contraction, traditional businesses are hiring data scientists in droves! Therefore, data scientist has become the top job in the U.S. for the last four years running.
However, data science has a difficult learning curve. This course seeks to fill all those gaps and has a comprehensive syllabus that tackles all the major components of data science knowledge.
You will be using data science to solve common business problems throughout this course. You will start with the basics of Python, Pandas, Scikit-learn, NumPy, Keras, Prophet, statsmod, SciPy, and more. You will learn statistics and probability for data science in detail. Then, you will learn visualization theory for data science and analytics using Seaborn, Matplotlib, and Plotly.
You will look at dashboard design using Google Data Studio along with machine learning and deep learning theory/tools.
Then, you will be solving problems using predictive modeling, classification, and deep learning. After this, you will move your focus to data analysis and statistical case studies, data science in marketing, and data science in retail.
Finally, you will see deployment to the cloud using Heroku to build a machine learning API.
By the end of this course, you will learn all the major components of data science and gain the confidence to enter the world of data science.
Audience
This course is designed for beginners in data science; business analysts who wish to do more with their data; college graduates who lack real-world experience; business-oriented persons who would like to use data to enhance their business; software developers or engineers who would like to start learning data science. Anyone looking to become more employable as a data scientist and with an interest in using data to solve real-world problems will enjoy this course thoroughly.
No need to be a programming or math whiz; basic high school math will be sufficient.
Chapter 1 : Introduction to the Course
The Data Science Hype
About Our Case Studies
Why Data is the New Oil
Defining Business Problems for Analytic Thinking and 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
Chapter 2 : Set Up (Google Colab) and Download Code Files
Downloading and Running Your Code
Chapter 3 : Introduction to Python
Why Use Python for Data Science?
Python Introduction - Part 1 - Variables
Python - Variables (Lists and Dictionaries)
Python - Conditional Statements
Python - Loops
Python - Functions
Python - Classes
Chapter 4 : Pandas
Introduction to Pandas
Pandas 1 - Data Series
Pandas 2A - DataFrames - Index, Slice, Stats, Finding Empty Cells
Pandas 2B - DataFrames - Index, Slice, Stats, Finding Empty Cells, and Filtering
Pandas 3A - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
Pandas 3B - Data Cleaning - Alter Columns/Rows, Missing Data, and String Operations
Pandas 4 - Data Aggregation - GroupBy, Map, Pivot, Aggregate Functions
Feature Engineer, Lambda, and Apply
Concatenating, Merging, and Joining
Time Series Data
Advanced Operations - Iterows, Vectorization, and NumPy
Advanced Operations - Map, Filter, Apply
Advanced Operations - Parallel Processing
Map Visualizations with Plotly - Cloropeths from Scratch - USA and World
Map Visualizations with Plotly - Heatmaps, Scatter Plots, and Lines
Chapter 5 : Statistics and Visualizations
Introduction to Statistics
Descriptive Statistics - Why Statistical Knowledge is So Important
Descriptive Statistics 1 - Exploratory Data Analysis (EDA) and Visualizations
Descriptive Statistics 2 - Exploratory Data Analysis (EDA) and Visualizations
Sampling, Averages, and Variance, and How to Lie and Mislead with Statistics
Sampling - Sample Sizes and Confidence Intervals - What Can You Trust?
Types of Variables - Quantitative 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 Think
Variance, Standard Deviation, and Bessel’s Correction
Covariance and Correlation - Do Amazon and Google Know You Better Than Anyone Else?
Lying with Correlations - Divorce Rates in Maine Caused by Margarine Consumption
The Normal Distribution and the Central Limit Theorem
Z-Scores
Chapter 6 : Probability Theory
Introduction to Probability
Estimating Probability
Probability - Addition Rule
Probability - Permutations and Combinations
Bayes Theorem
Chapter 7 : Hypothesis Testing
Introduction to Hypothesis Testing
Statistical Significance
Hypothesis Testing - P Value
Hypothesis Testing - Pearson Correlation
Chapter 8 : 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 Results
Chapter 9 : 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 to Our data
Pivot Tables - Total Profit
Adding Filters to Tables
Scorecard KPI Visualizations
Scorecards with Time Comparison
Bar Charts (Horizontal, Vertical, and Stacked)
Line Charts
Pie Charts, Donut Charts, and Tree Maps
Time Series and Comparative Time Series Plots
Scatter Plots
Geographic Plots
Bullet and Line Area Plots
Sharing and Final Conclusions
Our Executive Sales Dashboard
Chapter 10 : 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, and 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, and Outliers
Introduction to Neural Networks
Types of Deep Learning Algorithms CNNs, RNNs, and LSTMs
Chapter 11 : 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 and 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
Chapter 12 : Unsupervised Learning - Clustering
Introduction to Unsupervised Learning
K-Means Clustering
Choosing K
K-Means - Elbow and Silhouette Method
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)
Chapter 13 : Dimensionality Reduction
Principal Component Analysis
t-Distributed Stochastic Neighbor Embedding (t-SNE)
PCA and t-SNE in Python with Visualization Comparisons
Chapter 14 : 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
Chapter 15 : 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
Chapter 16 : Big Data
Introduction to Big Data
Challenges in Big Data
Hadoop, MapReduce, and Spark
Introduction to PySpark
RDDs, Transformations, Actions, Lineage Graphs, and Jobs
Chapter 17 : Predicting the US 2020 Election
Understanding Polling Data
Cleaning and 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
Chapter 18 : Predicting Diabetes Cases
Understanding and Preparing Our Healthcare Data
First Attempt - Trying a Naive Model
Trying Different Models and Comparing the Results
Chapter 19 : Market Basket Analysis
Understanding Our Dataset
Data Preparation
Visualizing Our Frequent Sets
Chapter 20 : Predicting the World Cup Winner (Soccer/Football)
Understanding and Preparing Our Soccer Datasets - Part 1
Understanding and Preparing Our Soccer Datasets - Part 2
Predicting Game Outcomes with Our Model
Simulating the World Cup Outcome with Our Model
Chapter 21 : 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
Chapter 22 : Analyzing Olympic Winners
Understanding Our Olympic Dataset
Getting the Medals Per Country
Analyzing the Winter Olympic Data and Viewing Medals Won Over Time
Chapter 23 : Is Home Advantage Real in Soccer and Basketball
Understanding Our Dataset and EDA
Goal Difference Ratios Home Versus Away
How Home Advantage Have Evolved Over Time
Chapter 24 : IPL Cricket Data Analysis
Loading and Understanding Our Cricket Dataset
Man of the Match and Stadium Analysis
Do Toss Winners Win More? And Team Versus Team Comparisons
Chapter 25 : Streaming Services (Netflix, Hulu, Disney Plus, and Amazon Prime)
Understanding Our Dataset
EDA and Visualizations
Best Movies Per Genre Platform Comparisons
Chapter 26 : Micro Brewery and Pub Data Analysis
EDA, Visualizations, and Map
Chapter 27 : Pizza Restaurant Data Analysis
EDA and Visualizations
Analysis Per State
Pizza Maps
Chapter 28 : Supply Chain Data Analysis
Understanding Our Dataset
Visualizations and EDA
More Visualizations
Chapter 29 : Indian Election Result Analysis
Introduction
Visualizations of Election Results
Visualizing Gender Turnout
Chapter 30 : Africa Economic Crisis Data Analysis
Economic Dataset Understanding
Visualizations and Correlations
Chapter 31 : Predicting Which Employees May Quit
Figuring Out Which Employees May Quit - Understanding the Problem and EDA
Data Cleaning and Preparation
Machine Learning Modeling + Deep Learning
Chapter 32 : Figuring Out Which Customers May Leave
Understanding the Problem
Exploratory Data Analysis and Visualizations
Data Pre-Processing
Machine Learning Modeling + Deep Learning
Chapter 33 : Who to Target for Donations?
Understanding the Problem
Exploratory Data Analysis and Visualizations
Preparing Our Dataset for Machine Learning
Modeling Using Grid Search to Find the best parameters
Chapter 34 : Predicting Insurance Premiums
Understanding the Problem + Exploratory Data Analysis and Visualizations
Data Preparation and Machine Learning Modeling
Chapter 35 : Predicting Airbnb Prices
Understanding the Problem + Exploratory Data Analysis and Visualizations
Machine Learning Modeling
Using Our Model for Value Estimation for New Clients
Chapter 36 : Detecting Credit Card Fraud
Understanding Our Dataset
Exploratory Analysis
Feature Extraction
Creating and Validating Our Model
Chapter 37 : Analyzing Conversion Rates in Marketing Campaigns
Exploratory Analysis of Understanding Marketing Conversion Rates
Chapter 38 : Predicting Advertising Engagement
Understanding the Problem + Exploratory Data Analysis and Visualizations
Data Preparation and Machine Learning Modeling
Chapter 39 : Product Sales Analysis
Problem and Plan of Attack
Sales and Revenue Analysis
Analysis Per Country, Repeat Customers, and Items
Chapter 40 : Determining Your Most Valuable Customers
Understanding the Problem + Exploratory Data Analysis and Visualizations
Customer Lifetime Value Modeling
Chapter 41 : Customer Clustering (K-Means, Hierarchical) - Train Passenger
Data Exploration and Description
Simple Exploratory Data Analysis and Visualizations
Feature Engineering
K-Means Clustering of Customer Data
Cluster Analysis
Chapter 42 : 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
Chapter 43 : Deep Learning Recommendation System
Understanding Our Wikipedia Movie Dataset
Creating Our Dataset
Deep Learning Embeddings and Training
Getting Recommendations Based on Movie Similarity
Chapter 44 : Predicting Brent Oil Prices
Understanding Our Dataset and Its Time Series Nature
Creating Our Prediction Model
Making Future Predictions
Chapter 45 : Detecting Sentiment in Tweets
Understanding Our Dataset and Word Clouds
Visualizations and Feature Extraction
Training Our Model
Chapter 46 : Spam or Ham Detection
Loading and Understanding Our Spam/Ham Dataset
Training Our Spam Detector
Chapter 47 : Explore Data with PySpark and Titanic Survival Prediction
Exploratory Analysis of Our Titanic Dataset
Transformation Operations
Machine Learning with PySpark
Chapter 48 : Newspaper Headline Classification Using PySpark
Loading and Understanding Our Dataset
Building Our Model with PySpark
Chapter 49 : Deployment into Production
Introduction to Production Deployment Systems
Creating the Model
Introduction to Flask
About Our WebApp
Deploying Our WebApp on Heroku