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The Data Science Course 2018: Complete Data Science Bootcamp

Video Introducing this tutorial


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
+
Statistics - Practical Example: Descriptive Statistics
2 lectures
Practical Example: Descriptive Statistics
Practical Example: Descriptive Statistics Exercise
+
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
+
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)
+
Statistics - Practical Example: Inferential Statistics
2 lectures
Practical Example: Inferential Statistics
Practical Example: Inferential Statistics Exercise
+
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
+
Statistics - Practical Example: Hypothesis Testing
2 lectures
Practical Example: Hypothesis Testing
Practical Example: Hypothesis Testing Exercise
+
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
+
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
+
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
+
Python - Other Python Operators
2 lectures
Comparison Operators
Comparison Operators
2 questions
Logical and Identity Operators
Logical and Identity Operators
2 questions
+
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
+
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
+
Python - Sequences
5 lectures
Lists
Lists
1 question
Using Methods
Using Methods
1 question
List Slicing
Tuples
Dictionaries
Dictionaries
1 question
+
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
+
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
+
Part 4: Advanced Statistical Methods in Python
1 lecture
Introduction to Regression Analysis
Introduction to Regression Analysis
1 question
+
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
+
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)
+
Advanced Statistical Methods - Other Types of Clustering
3 lectures
Types of Clustering
Dendrogram
Heatmaps
+
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?
+
Part 6: Deep Learning
1 lecture
What to Expect from this Part?
What is Machine Learning
4 questions
+
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
+
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
+
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
+
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
+
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
+
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
+
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
+
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
+
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
+
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
+
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

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