# The Data Science Course 2018: Complete Data Science Bootcamp

**Part 1: Introduction : **

A Practical Example: What You Will Learn in This Course

What Does the Course Cover **The Field of Data Science - The Various Data Science Disciplines : **

Data Science and Business Buzzwords: Why are there so many?

Data Science and Business Buzzwords: Why are there so many?

1 question

What is the difference between Analysis and Analytics

What is the difference between Analysis and Analytics

1 question

Business Analytics, Data Analytics, and Data Science: An Introduction

Business Analytics, Data Analytics, and Data Science: An Introduction

3 questions

Continuing with BI, ML, and AI

Continuing with BI, ML, and AI

2 questions

A Breakdown of our Data Science Infographic

A Breakdown of our Data Science Infographic

1 question **The Field of Data Science - Connecting the Data Science Disciplines : **

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

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

1 question **The Field of Data Science - The Benefits of Each Discipline : **

The Reason behind these Disciplines

The Reason behind these Disciplines

1 question **The Field of Data Science - Popular Data Science Techniques : **

Techniques for Working with Traditional Data

Techniques for Working with Traditional Data

1 question

Real Life Examples of Traditional Data

Techniques for Working with Big Data

Techniques for Working with Big Data

1 question

Real Life Examples of Big Data

Business Intelligence (BI) Techniques

Business Intelligence (BI) Techniques

4 questions

Real Life Examples of Business Intelligence (BI)

Techniques for Working with Traditional Methods

Techniques for Working with Traditional Methods

4 questions

Real Life Examples of Traditional Methods

Machine Learning (ML) Techniques

Machine Learning (ML) Techniques

2 questions

Types of Machine Learning

Types of Machine Learning

2 questions

Real Life Examples of Machine Learning (ML)

Real Life Examples of Machine Learning (ML)

5 questions **The Field of Data Science - Popular Data Science Tools : **

Necessary Programming Languages and Software Used in Data Science

Necessary Programming Languages and Software Used in Data Science

4 questions **The Field of Data Science - Careers in Data Science : **

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

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

1 question **The Field of Data Science - Debunking Common Misconceptions : **

Debunking Common Misconceptions

Debunking Common Misconceptions

1 question **Part 2: Statistics : **

Population and Sample

Population and Sample

2 questions **Statistics - Descriptive Statistics : **

Types of Data

Types of Data

2 questions

Levels of Measurement

Levels of Measurement

2 questions

Categorical Variables - Visualization Techniques

Categorical Variables - Visualization Techniques

1 question

Categorical Variables Exercise

Numerical Variables - Frequency Distribution Table

Numerical Variables - Frequency Distribution Table

1 question

Numerical Variables Exercise

The Histogram

The Histogram

1 question

Histogram Exercise

Cross Tables and Scatter Plots

Cross Tables and Scatter Plots

1 question

Cross Tables and Scatter Plots Exercise

Mean, median and mode

Mean, Median and Mode Exercise

Skewness

Skewness

1 question

Skewness Exercise

Variance

Variance Exercise

Standard Deviation and Coefficient of Variation

Standard Deviation

1 question

Standard Deviation and Coefficient of Variation Exercise

Covariance

Covariance

1 question

Covariance Exercise

Correlation Coefficient

Correlation

1 question

Correlation Coefficient Exercise

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Statistics - Practical Example: Descriptive Statistics

2 lectures

Practical Example: Descriptive Statistics

Practical Example: Descriptive Statistics Exercise

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Statistics - Inferential Statistics Fundamentals

8 lectures

Introduction

What is a Distribution

What is a Distribution

1 question

The Normal Distribution

The Normal Distribution

1 question

The Standard Normal Distribution

The Standard Normal Distribution

1 question

The Standard Normal Distribution Exercise

Central Limit Theorem

Central Limit Theorem

1 question

Standard error

Standard Error

1 question

Estimators and Estimates

Estimators and Estimates

1 question

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Statistics - Inferential Statistics: Confidence Intervals

15 lectures

What are Confidence Intervals?

What are Confidence Intervals?

1 question

Confidence Intervals; Population Variance Known; z-score

Confidence Intervals; Population Variance Known; z-score; Exercise

Confidence Interval Clarifications

Student's T Distribution

Student's T Distribution

1 question

Confidence Intervals; Population Variance Unknown; t-score

Confidence Intervals; Population Variance Unknown; t-score; Exercise

Margin of Error

Margin of Error

1 question

Confidence intervals. Two means. Dependent samples

Confidence intervals. Two means. Dependent samples Exercise

Confidence intervals. Two means. Independent samples (Part 1)

Confidence intervals. Two means. Independent samples (Part 1) Exercise

Confidence intervals. Two means. Independent samples (Part 2)

Confidence intervals. Two means. Independent samples (Part 2) Exercise

Confidence intervals. Two means. Independent samples (Part 3)

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Statistics - Practical Example: Inferential Statistics

2 lectures

Practical Example: Inferential Statistics

Practical Example: Inferential Statistics Exercise

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Statistics - Hypothesis Testing

15 lectures

Null vs Alternative Hypothesis

Further Reading on Null and Alternative Hypothesis

Null vs Alternative Hypothesis

3 questions

Rejection Region and Significance Level

Rejection Region and Significance Level

2 questions

Type I Error and Type II Error

Type I Error and Type II Error

4 questions

Test for the Mean. Population Variance Known

Test for the Mean. Population Variance Known Exercise

p-value

p-value

4 questions

Test for the Mean. Population Variance Unknown

Test for the Mean. Population Variance Unknown Exercise

Test for the Mean. Dependent Samples

Test for the Mean. Dependent Samples Exercise

Test for the mean. Independent samples (Part 1)

Test for the mean. Independent samples (Part 1). Exercise

Test for the mean. Independent samples (Part 2)

Test for the mean. Independent samples (Part 2)

1 question

Test for the mean. Independent samples (Part 2) Exercise

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Statistics - Practical Example: Hypothesis Testing

2 lectures

Practical Example: Hypothesis Testing

Practical Example: Hypothesis Testing Exercise

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Part 3: Introduction to Python

6 lectures

Introduction to Programming

Introduction to Programming

2 questions

Why Python?

Why Python?

2 questions

Why Jupyter?

Why Jupyter?

2 questions

Installing Python and Jupyter

Understanding Jupyter's Interface - the Notebook Dashboard

Prerequisites for Coding in the Jupyter Notebooks

Jupyter's Interface

3 questions

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Python - Variables and Data Types

3 lectures

Variables

Variables

1 question

Numbers and Boolean Values in Python

Numbers and Boolean Values in Python

1 question

Python Strings

Python Strings

3 questions

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Python - Basic Python Syntax

7 lectures

Using Arithmetic Operators in Python

Using Arithmetic Operators in Python

1 question

The Double Equality Sign

The Double Equality Sign

1 question

How to Reassign Values

How to Reassign Values

1 question

Add Comments

Add Comments

1 question

Understanding Line Continuation

Indexing Elements

Indexing Elements

1 question

Structuring with Indentation

Structuring with Indentation

1 question

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Python - Other Python Operators

2 lectures

Comparison Operators

Comparison Operators

2 questions

Logical and Identity Operators

Logical and Identity Operators

2 questions

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Python - Conditional Statements

4 lectures

The IF Statement

The IF Statement

1 question

The ELSE Statement

The ELIF Statement

A Note on Boolean Values

A Note on Boolean Values

1 question

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Python - Python Functions

7 lectures

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

Python Functions

2 questions

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Python - Sequences

5 lectures

Lists

Lists

1 question

Using Methods

Using Methods

1 question

List Slicing

Tuples

Dictionaries

Dictionaries

1 question

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Python - Iterations

6 lectures

For Loops

For Loops

1 question

While Loops and Incrementing

Lists with the range() Function

Lists with the range() Function

1 question

Conditional Statements and Loops

Conditional Statements, Functions, and Loops

How to Iterate over Dictionaries

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Python - Advanced Python Tools

4 lectures

Object Oriented Programming

Object Oriented Programming

2 questions

Modules and Packages

Modules and Packages

2 questions

What is the Standard Library?

What is the Standard Library?

1 question

Importing Modules in Python

Importing Modules in Python

2 questions

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Part 4: Advanced Statistical Methods in Python

1 lecture

Introduction to Regression Analysis

Introduction to Regression Analysis

1 question

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Advanced Statistical Methods - Linear regression

11 lectures

The Linear Regression Model

The Linear Regression Model

2 questions

Correlation vs Regression

Correlation vs Regression

1 question

Geometrical Representation of the Linear Regression Model

Geometrical Representation of the Linear Regression Model

1 question

Python Packages Installation

First Regression in Python

First Regression in Python Exercise

Using Seaborn for Graphs

How to Interpret the Regression Table

How to Interpret the Regression Table

3 questions

Decomposition of Variability

Decomposition of Variability

1 question

What is the OLS?

What is the OLS

1 question

R-Squared

R-Squared

2 questions

+

Advanced Statistical Methods - Multiple Linear Regression

13 lectures

Multiple Linear Regression

Multiple Linear Regression

1 question

Adjusted R-Squared

Adjusted R-Squared

3 questions

Multiple Linear Regression Exercise

Test for Significance of the Model (F-Test)

OLS Assumptions

OLS Assumptions

1 question

A1: Linearity

A1: Linearity

2 questions

A2: No Endogeneity

A2: No Endogeneity

1 question

A3: Normality and Homoscedasticity

A4: No Autocorrelation

A4: No autocorrelation

2 questions

A5: No Multicollinearity

A5: No Multicollinearity

1 question

Dealing with Categorical Data - Dummy Variables

Dealing with Categorical Data - Dummy Variables

Making Predictions with the Linear Regression

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Advanced Statistical Methods - Logistic Regression

16 lectures

Introduction to Logistic Regression

A Simple Example in Python

Logistic vs Logit Function

Building a Logistic Regression

Building a Logistic Regression - Exercise

An Invaluable Coding Tip

Understanding Logistic Regression Tables

Understanding Logistic Regression Tables - Exercise

What do the Odds Actually Mean

Binary Predictors in a Logistic Regression

Binary Predictors in a Logistic Regression - Exercise

Calculating the Accuracy of the Model

Calculating the Accuracy of the Model

Underfitting and Overfitting

Testing the Model

Testing the Model - Exercise

+

Advanced Statistical Methods - Cluster Analysis

4 lectures

Introduction to Cluster Analysis

Some Examples of Clusters

Difference between Classification and Clustering

Math Prerequisites

+

Advanced Statistical Methods - K-Means Clustering

15 lectures

K-Means Clustering

A Simple Example of Clustering

A Simple Example of Clustering - Exercise

Clustering Categorical Data

Clustering Categorical Data

How to Choose the Number of Clusters

How to Choose the Number of Clusters - Exercise

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?

EXERCISE: Species Segmentation with Cluster Analysis (Part 1)

EXERCISE: Species Segmentation with Cluster Analysis (Part 2)

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Advanced Statistical Methods - Other Types of Clustering

3 lectures

Types of Clustering

Dendrogram

Heatmaps

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Part 5: Mathematics

11 lectures

What is a matrix?

What is a Matrix?

6 questions

Scalars and Vectors

Scalars and Vectors

5 questions

Linear Algebra and Geometry

Linear Algebra and Geometry

3 questions

Arrays in Python - A Convenient Way To Represent Matrices

What is a Tensor?

What is a Tensor?

2 questions

Addition and Subtraction of Matrices

Addition and Subtraction of Matrices

3 questions

Errors when Adding Matrices

Transpose of a Matrix

Dot Product

Dot Product of Matrices

Why is Linear Algebra Useful?

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Part 6: Deep Learning

1 lecture

What to Expect from this Part?

What is Machine Learning

4 questions

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Deep Learning - Introduction to Neural Networks

12 lectures

Introduction to Neural Networks

Introduction to Neural Networks

1 question

Training the Model

Training the Model

3 questions

Types of Machine Learning

Types of Machine Learning

4 questions

The Linear Model (Linear Algebraic Version)

The Linear Model

2 questions

The Linear Model with Multiple Inputs

The Linear Model with Multiple Inputs

2 questions

The Linear model with Multiple Inputs and Multiple Outputs

The Linear model with Multiple Inputs and Multiple Outputs

3 questions

Graphical Representation of Simple Neural Networks

Graphical Representation of Simple Neural Networks

1 question

What is the Objective Function?

What is the Objective Function?

2 questions

Common Objective Functions: L2-norm Loss

Common Objective Functions: L2-norm Loss

3 questions

Common Objective Functions: Cross-Entropy Loss

Common Objective Functions: Cross-Entropy Loss

4 questions

Optimization Algorithm: 1-Parameter Gradient Descent

Optimization Algorithm: 1-Parameter Gradient Descent

4 questions

Optimization Algorithm: n-Parameter Gradient Descent

Optimization Algorithm: n-Parameter Gradient Descent

3 questions

+

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

5 lectures

Basic NN Example (Part 1)

Basic NN Example (Part 2)

Basic NN Example (Part 3)

Basic NN Example (Part 4)

Basic NN Example Exercises

+

Deep Learning - TensorFlow: Introduction

9 lectures

How to Install TensorFlow

A Note on Installing Packages in Anaconda

TensorFlow Outline and Logic

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

Basic NN Example with TF Exercises

+

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

9 lectures

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

Backpropagation - A Peek into the Mathematics of Optimization

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Deep Learning - Overfitting

6 lectures

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

+

Deep Learning - Initialization

3 lectures

What is Initialization?

Types of Simple Initializations

State-of-the-Art Method - (Xavier) Glorot Initialization

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Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

7 lectures

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)

+

Deep Learning - Preprocessing

5 lectures

Preprocessing Introduction

Types of Basic Preprocessing

Standardization

Preprocessing Categorical Data

Binary and One-Hot Encoding

+

Deep Learning - Classifying on the MNIST Dataset

11 lectures

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

MNIST: Exercises

MNIST: Solutions

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Deep Learning - Business Case Example

12 lectures

Business Case: Getting acquainted with the dataset

Business Case: Outlining the Solution

The Importance of Working with a Balanced Dataset

Business Case: Preprocessing

Business Case: Preprocessing Exercise

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

Business Case: Final Exercise

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Deep Learning - Conclusion

7 lectures

Summary on What You've Learned

What's Further out there in terms of Machine Learning

An overview of CNNs

DeepMind and Deep Learning

An Overview of RNNs

An Overview of non-NN Approaches

Download All Resources

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Software Integration

5 lectures

What are Data, Servers, Clients, Requests, and Responses

What are Data, Servers, Clients, Requests, and Responses

2 questions

What are Data Connectivity, APIs, and Endpoints?

What are Data Connectivity, APIs, and Endpoints?

2 questions

Taking a Closer Look at APIs

Taking a Closer Look at APIs

2 questions

Communication between Software Products through Text Files

Communication between Software Products through Text Files

1 question

Software Integration - Explained

Software Integration - Explained

2 questions

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Case Study - What's Next in the Course?

3 lectures

Game Plan for this Python, SQL, and Tableau Business Exercise

The Business Task

Introducing the Data Set

Introducing the Data Set

1 question

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Case Study - Preprocessing the 'Absenteeism_data'

32 lectures

What to Expect from the Following Sections?

Importing the Absenteeism Data in Python

Checking the Content of the Data Set

Introduction to Terms with Multiple Meanings

What's Regression Analysis - a Quick Refresher

Using a Statistical Approach towards the Solution to the Exercise

Dropping a Column from a DataFrame in Python

EXERCISE - Dropping a Column from a DataFrame in Python

SOLUTION - Dropping a Column from a DataFrame in Python

Analyzing the Reasons for Absence

Obtaining Dummies from a Single Feature

EXERCISE - Obtaining Dummies from a Single Feature

SOLUTION - Obtaining Dummies from a Single Feature

Dropping a Dummy Variable from the Data Set

More on Dummy Variables: A Statistical Perspective

Classifying the Various Reasons for Absence

Using .concatenate() in Python

EXERCISE - Using .concatenate() in Python

SOLUTION - Using .concatenate() in Python

Reordering Columns in a Pandas DataFrame in Python

EXERCISE - Reordering Columns in a Pandas DataFrame in Python

SOLUTION - Reordering Columns in a Pandas DataFrame in Python

Creating Checkpoints while Coding in Jupyter

EXERCISE - Creating Checkpoints while Coding in Jupyter

SOLUTION - 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

EXERCISE - Removing the "Date" Column

Analyzing Several "Straightforward" Columns for this Exercise

Working on "Education", "Children", and "Pets"

Final Remarks of this Section

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

16 lectures

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

ARTICLE - A Note on 'pickling'

EXERCISE - Saving the Model (and Scaler)

Preparing the Deployment of the Model through a Module

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Case Study - Loading the 'absenteeism_module'

4 lectures

Are You Sure You're All Set?

Deploying the 'absenteeism_module' - Part I

Deploying the 'absenteeism_module' - Part II

Exporting the Obtained Data Set as a *.csv

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Case Study - Analyzing the Predicted Outputs in Tableau

6 lectures

EXERCISE - Age vs Probability

Analyzing Age vs Probability in Tableau

EXERCISE - Reasons vs Probability

Analyzing Reasons vs Probability in Tableau

EXERCISE - Transportation Expense vs Probability

Analyzing Transportation Expense vs Probability in Tableau