The Data Science Course 2020: Complete Data Science Bootcamp

The Data Science Course 2020: Complete Data Science Bootcamp
The Data Science Course 2020: Complete Data Science Bootcamp
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 28.5 Hours | 15.2 GB

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

The Problem

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.

However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

And how can you do that?

Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture

The Solution

Data science is a multidisciplinary field. It encompasses a wide range of topics.

  • Understanding of the data science field and the type of analysis carried out
  • Mathematics
  • Statistics
  • Python
  • Applying advanced statistical techniques in Python
  • Data Visualization
  • Machine Learning
  • Deep Learning

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2020.

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).

The Skills

1. Intro to Data and Data Science

Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?

Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.

2. Mathematics

Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.

We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.

Why learn it?

Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

3. Statistics

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.

Why learn it?

This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

4. Python

Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.

Why learn it?

When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.

5. Tableau

Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.

Why learn it?

A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.

6. Advanced Statistics

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.

Why learn it?

Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.

7. Machine Learning

The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.

What you’ll learn

  • The course provides the entire toolbox you need to become a data scientist
  • Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau,
  • Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
  • Impress interviewers by showing an understanding of the data science field
  • Learn how to pre-process data
  • Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
  • Start coding in Python and learn how to use it for statistical analysis
  • Perform linear and logistic regressions in Python
  • Carry out cluster and factor analysis
  • Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
  • Apply your skills to real-life business cases
  • Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
  • Unfold the power of deep neural networks
  • Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
  • Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
Table of Contents

Part 1 Introduction
A Practical Example What You Will Learn in This Course
What Does the Course Cover
Download All Resources and Important FAQ

The Field of Data Science – The Various Data Science Disciplines
Data Science and Business Buzzwords Why are there so Many
A Breakdown of our Data Science Infographic
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
A Breakdown of our Data Science Infographic

The Field of Data Science – Connecting the Data Science Disciplines
Applying Traditional Data, Big Data, BI, Traditional Data Science and ML

The Field of Data Science – The Benefits of Each Discipline
The Reason Behind These Disciplines

The Field of Data Science – Popular Data Science Techniques
Techniques for Working with Traditional Data
Techniques for Working with Traditional Methods
Real Life Examples of Traditional Methods
Machine Learning (ML) Techniques
Types of Machine Learning
Real Life Examples of Machine Learning (ML)
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)

The Field of Data Science – Popular Data Science Tools
Necessary Programming Languages and Software Used in Data Science

The Field of Data Science – Careers in Data Science
Finding the Job – What to Expect and What to Look for

The Field of Data Science – Debunking Common Misconceptions
Debunking Common Misconceptions

Part 2 Probability
The Basic Probability Formula
Computing Expected Values
Frequency
Events and Their Complements

Probability – Combinatorics
Fundamentals of Combinatorics
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
Fundamentals of Combinatorics
A Practical Example of Combinatorics
Permutations and How to Use Them
Simple Operations with Factorials
Solving Variations with Repetition
Solving Variations without Repetition

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

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

Probability – Probability in Other Fields
Probability in Finance
Probability in Statistics
Probability in Data Science

Part 3 Statistics
Population and Sample
Population and Sample

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

Statistics – Practical Example Descriptive Statistics
Practical Example Descriptive Statistics
Practical Example Descriptive Statistics Exercise

Statistics – Inferential Statistics Fundamentals
Introduction
Central Limit Theorem
Standard error
Standard Error
Estimators and Estimates
Estimators and Estimates
What is a Distribution
What is a Distribution
The Normal Distribution
The Normal Distribution
The Standard Normal Distribution
The Standard Normal Distribution
The Standard Normal Distribution Exercise
Central Limit Theorem

Statistics – Inferential Statistics Confidence Intervals
What are Confidence Intervals
Margin of Error
Margin of Error
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)
What are Confidence Intervals
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
Confidence Intervals; Population Variance Unknown; T-score
Confidence Intervals; Population Variance Unknown; T-score; Exercise

Statistics – Practical Example Inferential Statistics
Practical Example Inferential Statistics
Practical Example Inferential Statistics Exercise

Statistics – Hypothesis Testing
Null vs Alternative Hypothesis
p-value
p-value
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)
Further Reading on Null and Alternative Hypothesis
Test for the mean. Independent Samples (Part 2). Exercise
Null vs Alternative Hypothesis
Rejection Region and Significance Level
Rejection Region and Significance Level
Type I Error and Type II Error
Type I Error and Type II Error
Test for the Mean. Population Variance Known
Test for the Mean. Population Variance Known Exercise

Statistics – Practical Example Hypothesis Testing
Practical Example Hypothesis Testing
Practical Example Hypothesis Testing Exercise

Part 4 Introduction to Python
Introduction to Programming
Jupyter’s Interface
Python 2 vs Python 3
Introduction to Programming
Why Python
Why Python
Why Jupyter
Why Jupyter
Installing Python and Jupyter
Understanding Jupyter’s Interface – the Notebook Dashboard
Prerequisites for Coding in the Jupyter Notebooks

Python – Variables and Data Types
Variables
Variables
Numbers and Boolean Values in Python
Numbers and Boolean Values in Python
Python Strings
Python Strings

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

Python – Other Python Operators
Comparison Operators
Comparison Operators
Logical and Identity Operators
Logical and Identity Operators

Python – Conditional Statements
The IF Statement
The IF Statement
The ELSE Statement
The ELIF Statement
A Note on Boolean Values
A Note on Boolean Values

Python – Python Functions
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

Python – Sequences
Lists
Lists
Using Methods
Using Methods
List Slicing
Tuples
Dictionaries
Dictionaries

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

Python – Advanced Python Tools
Object Oriented Programming
Object Oriented Programming
Modules and Packages
Modules and Packages
What is the Standard Library
What is the Standard Library
Importing Modules in Python
Importing Modules in Python

Part 5 Advanced Statistical Methods in Python
Introduction to Regression Analysis
Introduction to Regression Analysis

Advanced Statistical Methods – Linear Regression with StatsModels
The Linear Regression Model
Using Seaborn for Graphs
How to Interpret the Regression Table
How to Interpret the Regression Table
Decomposition of Variability
Decomposition of Variability
What is the OLS
What is the OLS
R-Squared
R-Squared
The Linear Regression Model
Correlation vs Regression
Correlation vs Regression
Geometrical Representation of the Linear Regression Model
Geometrical Representation of the Linear Regression Model
Python Packages Installation
First Regression in Python
First Regression in Python Exercise

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

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

Advanced Statistical Methods – Practical Example Linear Regression
Practical Example Linear Regression (Part 1)
Practical Example Linear Regression (Part 2)
A Note on Multicollinearity
Practical Example Linear Regression (Part 3)
Dummies and Variance Inflation Factor – Exercise
Practical Example Linear Regression (Part 4)
Dummy Variables – Exercise
Practical Example Linear Regression (Part 5)
Linear Regression – Exercise

Advanced Statistical Methods – Logistic Regression
Introduction to Logistic Regression
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
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

Advanced Statistical Methods – Cluster Analysis
Introduction to Cluster Analysis
Some Examples of Clusters
Difference between Classification and Clustering
Math Prerequisites

Advanced Statistical Methods – K-Means Clustering
K-Means Clustering
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)
A Simple Example of Clustering
A Simple Example of Clustering – Exercise
Clustering Categorical Data
Clustering Categorical Data – Exercise
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

Advanced Statistical Methods – Other Types of Clustering
Types of Clustering
Dendrogram
Heatmaps

Part 6 Mathematics
What is a Matrix
Addition and Subtraction of Matrices
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 is a Matrix
Scalars and Vectors
Scalars and Vectors
Linear Algebra and Geometry
Linear Algebra and Geometry
Arrays in Python – A Convenient Way To Represent Matrices
What is a Tensor
What is a Tensor

Part 7 Deep Learning
What to Expect from this Part

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

Deep Learning – How to Build a Neural Network from Scratch with NumPy
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 2.0 Introduction
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
Basic NN with TensorFlow Exercises

Deep Learning – Digging Deeper into NNs Introducing Deep Neural Networks
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
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
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
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
Preprocessing Introduction
Types of Basic Preprocessing
Standardization
Preprocessing Categorical Data
Binary and One-Hot Encoding

Deep Learning – Classifying on the MNIST Dataset
MNIST The Dataset
MNIST Learning
MNIST – Exercises
MNIST Testing the Model
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 – Scale the Test Data – Exercise
MNIST Preprocess the Data – Shuffle and Batch
MNIST Preprocess the Data – Shuffle and Batch – Exercise
MNIST Outline the Model
MNIST Select the Loss and the Optimizer

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

Deep Learning – Conclusion
Summary on What You’ve Learned
What’s Further out there in terms of Machine Learning
DeepMind and Deep Learning
An overview of CNNs
An Overview of RNNs
An Overview of non-NN Approaches

Appendix Deep Learning – TensorFlow 1 Introduction
READ ME!!!!
Basic NN Example with TF Exercises
How to Install TensorFlow 1
A Note on Installing Packages in Anaconda
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

Appendix Deep Learning – TensorFlow 1 Classifying on the MNIST Dataset
MNIST What is the MNIST Dataset
MNIST Solutions
MNIST Exercises
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

Appendix Deep Learning – TensorFlow 1 Business Case
Business Case Getting Acquainted with the Dataset
Business Case Testing the Model
Business Case A Comment on the Homework
Business Case Final Exercise
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

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

Case Study – What’s Next in the Course
Game Plan for this Python, SQL, and Tableau Business Exercise
The Business Task
Introducing the Data Set
Introducing the Data Set

Case Study – Preprocessing the ‘Absenteeism data’
What to Expect from the Following Sections
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 .concat() in Python
EXERCISE – Using .concat() in Python
SOLUTION – Using .concat() in Python
Importing the Absenteeism Data 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
Checking the Content of the Data Set
Analyzing Several Straightforward Columns for this Exercise
Working on Education, Children, and Pets
Final Remarks of this Section
A Note on Exporting Your Data as a .csv File
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

Case Study – Applying Machine Learning to Create the ‘absenteeism module’
Exploring the Problem with a Machine Learning Mindset
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
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)

Case Study – Loading the ‘absenteeism module’
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
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

Bonus Lecture
Bonus Lecture Next Steps