The Data Science Course: Complete Data Science Bootcamp

A Practical Example: What You Will Learn in This Course
What Does the Course Cover

Data Science and Business Buzzwords: Why are there so Many?
What is the difference between Analysis and Analytics
Business Analytics, Data Analytics, and Data Science: An Introduction
Continuing with BI, ML, and AI
Traditional AI vs. Generative AI
More Examples of Generative AI
A Breakdown of our Data Science Infographic

Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

The Reason Behind These Disciplines

Techniques for Working with Traditional Data
Real Life Examples of Traditional Data
Techniques for Working with Big Data
Real Life Examples of Big Data
Business Intelligence (BI) Techniques
Real Life Examples of Business Intelligence (BI)
Techniques for Working with Traditional Methods
Real Life Examples of Traditional Methods
Machine Learning (ML) Techniques
Types of Machine Learning
Evolution and Latest Trends of Machine Learning (ML)
Real Life Examples of Machine Learning (ML)

Necessary Programming Languages and Software Used in Data Science

Finding the Job - What to Expect and What to Look for

Debunking Common Misconceptions

The Basic Probability Formula
Computing Expected Values
Frequency
Events and Their Complements

Fundamentals of Combinatorics
Permutations and How to Use Them
Simple Operations with Factorials
Solving Variations with Repetition
Solving Variations without Repetition
Solving Combinations
Symmetry of Combinations
Solving Combinations with Separate Sample Spaces
Combinatorics in Real-Life: The Lottery
A Recap of Combinatorics
A Practical Example of Combinatorics

Sets and Events
Ways Sets Can Interact
Intersection of Sets
Union of Sets
Mutually Exclusive Sets
Dependence and Independence of Sets
The Conditional Probability Formula
The Law of Total Probability
The Additive Rule
The Multiplication Law
Bayes' Law
A Practical Example of Bayesian Inference

Fundamentals of Probability Distributions
Types of Probability Distributions
Characteristics of Discrete Distributions
Discrete Distributions: The Uniform Distribution
Discrete Distributions: The Bernoulli Distribution
Discrete Distributions: The Binomial Distribution
Discrete Distributions: The Poisson Distribution
Characteristics of Continuous Distributions
Continuous Distributions: The Normal Distribution
Continuous Distributions: The Standard Normal Distribution
Continuous Distributions: The Students' T Distribution
Continuous Distributions: The Chi-Squared Distribution
Continuous Distributions: The Exponential Distribution
Continuous Distributions: The Logistic Distribution
A Practical Example of Probability Distributions

Probability in Finance
Probability in Statistics
Probability in Data Science

Population and Sample

Types of Data
Levels of Measurement
Categorical Variables - Visualization Techniques
Numerical Variables - Frequency Distribution Table
The Histogram
Cross Tables and Scatter Plots
Mean, median and mode
Skewness
Variance
Standard Deviation and Coefficient of Variation
Covariance
Correlation Coefficient

Practical Example: Descriptive Statistics

Introduction
What is a Distribution
The Normal Distribution
The Standard Normal Distribution
Central Limit Theorem
Standard error
Estimators and Estimates

What are Confidence Intervals?
Confidence Intervals; Population Variance Known; Z-score
Confidence Interval Clarifications
Student's T Distribution
Confidence Intervals; Population Variance Unknown; T-score
Margin of Error
Confidence intervals. Two means. Dependent samples
Confidence intervals. Two means. Independent Samples (Part 1)
Confidence intervals. Two means. Independent Samples (Part 2)
Confidence intervals. Two means. Independent Samples (Part 3)

Practical Example: Inferential Statistics

Null vs Alternative Hypothesis
Rejection Region and Significance Level
Type I Error and Type II Error
Test for the Mean. Population Variance Known
p-value
Test for the Mean. Population Variance Unknown
Test for the Mean. Dependent Samples
Test for the mean. Independent Samples (Part 1)
Test for the mean. Independent Samples (Part 2)

Practical Example: Hypothesis Testing

Introduction to Programming
Why Python?
Why Jupyter?
Installing Python and Jupyter
Understanding Jupyter's Interface - the Notebook Dashboard
Prerequisites for Coding in the Jupyter Notebooks

Variables
Numbers and Boolean Values in Python
Python Strings

Using Arithmetic Operators in Python
The Double Equality Sign
How to Reassign Values
Add Comments
Understanding Line Continuation
Indexing Elements
Structuring with Indentation

Comparison Operators
Logical and Identity Operators

The IF Statement
The ELSE Statement
The ELIF Statement
A Note on Boolean Values

Defining a Function in Python
How to Create a Function with a Parameter
Defining a Function in Python - Part II
How to Use a Function within a Function
Conditional Statements and Functions
Functions Containing a Few Arguments
Built-in Functions in Python

Lists
Using Methods
List Slicing
Tuples
Dictionaries

For Loops
While Loops and Incrementing
Lists with the range() Function
Conditional Statements and Loops
Conditional Statements, Functions, and Loops
How to Iterate over Dictionaries

Object Oriented Programming
Modules and Packages
What is the Standard Library?
Importing Modules in Python

Introduction to Regression Analysis

The Linear Regression Model
Correlation vs Regression
Geometrical Representation of the Linear Regression Model
Python Packages Installation
First Regression in Python
Using Seaborn for Graphs
How to Interpret the Regression Table
Decomposition of Variability
What is the OLS?
R-Squared

Multiple Linear Regression
Adjusted R-Squared
Test for Significance of the Model (F-Test)
OLS Assumptions
A1: Linearity
A2: No Endogeneity
A3: Normality and Homoscedasticity
A4: No Autocorrelation
A5: No Multicollinearity
Dealing with Categorical Data - Dummy Variables
Making Predictions with the Linear Regression

What is sklearn and How is it Different from Other Packages
How are we Going to Approach this Section?
Simple Linear Regression with sklearn
Simple Linear Regression with sklearn - A StatsModels-like Summary Table
Multiple Linear Regression with sklearn
Calculating the Adjusted R-Squared in sklearn
Feature Selection (F-regression)
Creating a Summary Table with P-values
Feature Scaling (Standardization)
Feature Selection through Standardization of Weights
Predicting with the Standardized Coefficients
Underfitting and Overfitting
Train - Test Split Explained

Practical Example: Linear Regression (Part 1)
Practical Example: Linear Regression (Part 2)
Practical Example: Linear Regression (Part 3)
Practical Example: Linear Regression (Part 4)
Practical Example: Linear Regression (Part 5)

Introduction to Logistic Regression
A Simple Example in Python
Logistic vs Logit Function
Building a Logistic Regression
An Invaluable Coding Tip
Understanding Logistic Regression Tables
What do the Odds Actually Mean
Binary Predictors in a Logistic Regression
Calculating the Accuracy of the Model
Underfitting and Overfitting
Testing the Model

Introduction to Cluster Analysis
Some Examples of Clusters
Difference between Classification and Clustering
Math Prerequisites

K-Means Clustering
A Simple Example of Clustering
Clustering Categorical Data
How to Choose the Number of Clusters
Pros and Cons of K-Means Clustering
To Standardize or not to Standardize
Relationship between Clustering and Regression
Market Segmentation with Cluster Analysis (Part 1)
Market Segmentation with Cluster Analysis (Part 2)
How is Clustering Useful?

Types of Clustering
Dendrogram
Heatmaps

Traditional data science methods and the role of ChatGPT
How to install ChatGPT
How ChatGPT can boost your productivity
Data Preprocessing with ChatGPT
First attempt at machine learning with ChatGPT
Analyzing a client database with ChatGPT in Python
Analyzing a client database with ChatGPT in Python – analyzing top products
Analyzing a client database with ChatGPT in Python – analyzing top clients, RFM
Exploratory data analysis (EDA) with ChatGPT - histogram and scatter plot
Exploratory data analysis (EDA) with ChatGPT - correlation matrix, outlier detec
Hypothesis testing with ChatGPT
Marvels comic book database: Intro to Regular Expressions (RegEx)
Decoding comic book data: Python Regular Expressions and ChatGPT
Algorithm recommendation: Movie Database Analysis with ChatGPT
Algorithm recommendation: recommendation engine for movies with ChatGPT
Ethical principles in data and AI utilization
Using ChatGPT for ethical considerations

Intro to the Case Study
The Naive Bayes Algorithm
Tokenization and Vectorization
Imbalanced Data Sets
Overcome Imbalanced Data in Machine Learning
Loading the Dataset and Preprocessing
Optimizing User Reviews: Data Preprocessing & EDA
Reg Ex for Analyzing Text Review Data
Understanding Differences between Multinomial and Bernouilli Naive Bayes
Machine Learning with Naïve Bayes (First Attempt)
Machine Learning with Naïve Bayes – converting the problem to a binary one
Testing the Model on New Data

What is a Matrix?
Scalars and Vectors
Linear Algebra and Geometry
Arrays in Python - A Convenient Way To Represent Matrices
What is a Tensor?
Addition and Subtraction of Matrices
Errors when Adding Matrices
Transpose of a Matrix
Dot Product
Dot Product of Matrices
Why is Linear Algebra Useful?

What to Expect from this Part?

Introduction to Neural Networks
Training the Model
Types of Machine Learning
The Linear Model (Linear Algebraic Version)
The Linear Model with Multiple Inputs
The Linear model with Multiple Inputs and Multiple Outputs
Graphical Representation of Simple Neural Networks
What is the Objective Function?
Common Objective Functions: L2-norm Loss
Common Objective Functions: Cross-Entropy Loss
Optimization Algorithm: 1-Parameter Gradient Descent
Optimization Algorithm: n-Parameter Gradient Descent

Basic NN Example (Part 1)
Basic NN Example (Part 2)
Basic NN Example (Part 3)
Basic NN Example (Part 4)

How to Install TensorFlow 2.0
TensorFlow Outline and Comparison with Other Libraries
TensorFlow 1 vs TensorFlow 2
A Note on TensorFlow 2 Syntax
Types of File Formats Supporting TensorFlow
Outlining the Model with TensorFlow 2
Interpreting the Result and Extracting the Weights and Bias
Customizing a TensorFlow 2 Model

What is a Layer?
What is a Deep Net?
Digging into a Deep Net
Non-Linearities and their Purpose
Activation Functions
Activation Functions: Softmax Activation
Backpropagation
Backpropagation Picture

What is Overfitting?
Underfitting and Overfitting for Classification
What is Validation?
Training, Validation, and Test Datasets
N-Fold Cross Validation
Early Stopping or When to Stop Training

What is Initialization?
Types of Simple Initializations
State-of-the-Art Method - (Xavier) Glorot Initialization

Stochastic Gradient Descent
Problems with Gradient Descent
Momentum
Learning Rate Schedules, or How to Choose the Optimal Learning Rate
Learning Rate Schedules Visualized
Adaptive Learning Rate Schedules (AdaGrad and RMSprop)
Adam (Adaptive Moment Estimation)

Preprocessing Introduction
Types of Basic Preprocessing
Standardization
Preprocessing Categorical Data
Binary and One-Hot Encoding

MNIST: The Dataset
MNIST: How to Tackle the MNIST
MNIST: Importing the Relevant Packages and Loading the Data
MNIST: Preprocess the Data - Create a Validation Set and Scale It
MNIST: Preprocess the Data - Shuffle and Batch
MNIST: Outline the Model
MNIST: Select the Loss and the Optimizer
MNIST: Learning
MNIST: Testing the Model

Business Case: Exploring the Dataset and Identifying Predictors
Business Case: Outlining the Solution
Business Case: Balancing the Dataset
Business Case: Preprocessing the Data
Business Case: Load the Preprocessed Data
Business Case: Learning and Interpreting the Result
Business Case: Setting an Early Stopping Mechanism
Business Case: Testing the Model

Summary on What You've Learned
What's Further out there in terms of Machine Learning
An overview of CNNs
An Overview of RNNs
An Overview of non-NN Approaches

How to Install TensorFlow 1
TensorFlow Intro
Actual Introduction to TensorFlow
Types of File Formats, supporting Tensors
Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
Basic NN Example with TF: Loss Function and Gradient Descent
Basic NN Example with TF: Model Output

MNIST: What is the MNIST Dataset?
MNIST: How to Tackle the MNIST
MNIST: Relevant Packages
MNIST: Model Outline
MNIST: Loss and Optimization Algorithm
Calculating the Accuracy of the Model
MNIST: Batching and Early Stopping
MNIST: Learning
MNIST: Results and Testing

Business Case: Getting Acquainted with the Dataset
Business Case: Outlining the Solution
The Importance of Working with a Balanced Dataset
Business Case: Preprocessing
Creating a Data Provider
Business Case: Model Outline
Business Case: Optimization
Business Case: Interpretation
Business Case: Testing the Model
Business Case: A Comment on the Homework

What are Data, Servers, Clients, Requests, and Responses
What are Data Connectivity, APIs, and Endpoints?
Taking a Closer Look at APIs
Communication between Software Products through Text Files
Software Integration - Explained

Game Plan for this Python, SQL, and Tableau Business Exercise
The Business Task
Introducing the Data Set

Importing the Absenteeism Data in Python
Checking the Content of the Data Set
Introduction to Terms with Multiple Meanings
Using a Statistical Approach towards the Solution to the Exercise
Dropping a Column from a DataFrame in Python
Analyzing the Reasons for Absence
Obtaining Dummies from a Single Feature
More on Dummy Variables: A Statistical Perspective
Classifying the Various Reasons for Absence
Using .concat() in Python
Reordering Columns in a Pandas DataFrame in Python
Creating Checkpoints while Coding in Jupyter
Analyzing the Dates from the Initial Data Set
Extracting the Month Value from the "Date" Column
Extracting the Day of the Week from the "Date" Column
Analyzing Several "Straightforward" Columns for this Exercise
Working on "Education", "Children", and "Pets"
Final Remarks of this Section

Exploring the Problem with a Machine Learning Mindset
Creating the Targets for the Logistic Regression
Selecting the Inputs for the Logistic Regression
Standardizing the Data
Splitting the Data for Training and Testing
Fitting the Model and Assessing its Accuracy
Creating a Summary Table with the Coefficients and Intercept
Interpreting the Coefficients for Our Problem
Standardizing only the Numerical Variables (Creating a Custom Scaler)
Interpreting the Coefficients of the Logistic Regression
Backward Elimination or How to Simplify Your Model
Testing the Model We Created
Saving the Model and Preparing it for Deployment
Preparing the Deployment of the Model through a Module

Deploying the 'absenteeism_module' - Part I
Deploying the 'absenteeism_module' - Part II

Analyzing Age vs Probability in Tableau
Analyzing Reasons vs Probability in Tableau
Analyzing Transportation Expense vs Probability in Tableau

Using the .format() Method
Iterating Over Range Objects
Introduction to Nested For Loops
Triple Nested For Loops
List Comprehensions
Anonymous (Lambda) Functions

Introduction to pandas Series
Working with Methods in Python - Part I
Working with Methods in Python - Part II
Parameters and Arguments in pandas
Using .unique() and .nunique()
Using .sort_values()
Introduction to pandas DataFrames - Part I
Introduction to pandas DataFrames - Part II
pandas DataFrames - Common Attributes
Data Selection in pandas DataFrames
pandas DataFrames - Indexing with .iloc[]
pandas DataFrames - Indexing with .loc[]

The Data Science Course: Complete Data Science Bootcamp

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The Data Science Course: Complete Data Science Bootcamp

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Part 1: Introduction

The Field of Data Science - The Various Data Science Disciplines

The Field of Data Science - Connecting the Data Science Disciplines

The Field of Data Science - The Benefits of Each Discipline

The Field of Data Science - Popular Data Science Techniques

The Field of Data Science - Popular Data Science Tools

The Field of Data Science - Careers in Data Science

The Field of Data Science - Debunking Common Misconceptions

Part 2: Probability

Probability - Combinatorics

Probability - Bayesian Inference

Probability - Distributions

Probability - Probability in Other Fields

Part 3: Statistics

Statistics - Descriptive Statistics

Statistics - Practical Example: Descriptive Statistics

Statistics - Inferential Statistics Fundamentals

Statistics - Inferential Statistics: Confidence Intervals

Statistics - Practical Example: Inferential Statistics

Statistics - Hypothesis Testing

Statistics - Practical Example: Hypothesis Testing

Part 4: Introduction to Python

Python - Variables and Data Types

Python - Basic Python Syntax

Python - Other Python Operators

Python - Conditional Statements

Python - Python Functions

Python - Sequences

Python - Iterations

Python - Advanced Python Tools

Part 5: Advanced Statistical Methods in Python

Advanced Statistical Methods - Linear Regression with StatsModels

Advanced Statistical Methods - Multiple Linear Regression with StatsModels

Advanced Statistical Methods - Linear Regression with sklearn

Advanced Statistical Methods - Practical Example: Linear Regression

Advanced Statistical Methods - Logistic Regression

Advanced Statistical Methods - Cluster Analysis

Advanced Statistical Methods - K-Means Clustering

Advanced Statistical Methods - Other Types of Clustering

ChatGPT for Data Science

Case Study: Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis

Part 6: Mathematics

Part 7: Deep Learning

Deep Learning - Introduction to Neural Networks

Deep Learning - How to Build a Neural Network from Scratch with NumPy

Deep Learning - TensorFlow 2.0: Introduction

Deep Learning - Digging Deeper into NNs: Introducing Deep Neural Networks

Deep Learning - Overfitting

Deep Learning - Initialization

Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

Deep Learning - Preprocessing

Deep Learning - Classifying on the MNIST Dataset

Deep Learning - Business Case Example

Deep Learning - Conclusion

Appendix: Deep Learning - TensorFlow 1: Introduction

Appendix: Deep Learning - TensorFlow 1: Classifying on the MNIST Dataset

Appendix: Deep Learning - TensorFlow 1: Business Case

Software Integration

Case Study - What's Next in the Course?

Case Study - Preprocessing the 'Absenteeism_data'

Case Study - Applying Machine Learning to Create the 'absenteeism_module'

Case Study - Loading the 'absenteeism_module'

Case Study - Analyzing the Predicted Outputs in Tableau

Appendix - Additional Python Tools

Appendix - pandas Fundamentals

Bonus Lecture

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