About Course
The Problem
Data scientist is one of the best suited professions to thrive this century. It is digital, programmingoriented, 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, timeefficient, and structured data science training available online, we created The Data Science Course 2024.
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 generalpurpose 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 scikitlearn, 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 nontechnical 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.
Why learn it?
Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.
**What you get**
 A $1250 data science training program
 Active Q&A support
 All the knowledge to get hired as a data scientist
 A community of data science learners
 A certificate of completion
 Access to future updates
 Solve reallife business cases that will get you the job
You will become a data scientist from scratch We are happy to offer an unconditional 30day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a nobrainer for us, as we are certain you will love it.
What Will You 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 scikitlearn, Deep learning with TensorFlow
 Impress interviewers by showing an understanding of the data science field
 Learn how to preprocess 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 scikitlearn
 Apply your skills to reallife business cases
 Use stateoftheart 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, nfold 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 reallife situations
Course Content
Part 1: Introduction

A Practical Example What You Will Learn in This Course
00:00 
What Does the Course Cover
00:00
The Fields of Data Science – Various Data Science Disciplines

Data Science and Business Buzzwords Why are there so Many
00:00 
What is the difference between Analysis and Analytics
00:00 
Business Analytics, Data Analytics, and Data Science An Introduction
00:00 
Continuing with BI, ML, and AI
00:00 
A Breakdown of our Data Science Infographic
00:00
The Fields of Data Science – Connecting Data Science Disciplines

Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
00:00
The Fields of Data Science – Benefits of Each Disciplines

The Reason Behind These Disciplines
00:00
The Fields of Data Science – Popular Data Science Techniques

Techniques for Working with Traditional Data
00:00 
Real Life Examples of Traditional Data
00:00 
Techniques for Working with Big Data
00:00 
Real Life Examples of Big Data
00:00 
Business Intelligence (BI) Techniques
00:00 
Real Life Examples of Business Intelligence (BI)
00:00 
Techniques for Working with Traditional Methods
00:00 
Real Life Examples of Traditional Methods
00:00 
Machine Learning (ML) Techniques
00:00 
Types of Machine Learning
00:00 
Real Life Examples of Machine Learning (ML)
00:00
The Fields of Data Science – Popular Data Science Tools

Necessary Programming Languages and Software Used in Data Science
00:00
The Fields of Data Science – Career in Data Science

Finding the Job – What to Expect and What to Look for
00:00
The Fields of Data Science – Debunking Common Misconceptions

Debunking Common Misconceptions
00:00
Part 2: Probability

The Basic Probability Formula
00:00 
Computing Expected Values
00:00 
Frequency
00:00 
Events and Their Complements
00:00
Probability – Combinatorics

Fundamentals of Combinatorics
01:04 
Permutations and How to Use Them
00:00 
Simple Operations with Factorials
00:00 
Solving Variations with Repetition
00:00 
Solving Variations without Repetition
00:00 
Solving Combinations
00:00 
Symmetry of Combinations
00:00 
Solving Combinations with Separate Sample Spaces
00:00 
Combinatorics in RealLife: The Lottery
00:00 
A Recap of Combinatorics
00:00 
A Practical Example of Combinatorics
00:00
Probability – Bayesian Inference

Sets and Events
00:00 
Ways Sets Can Interact
00:00 
Intersection of Sets
00:00 
Union of Sets
00:00 
Mutually Exclusive Sets
00:00 
Dependence and Independence of Sets
00:00 
The Conditional Probability Formula
00:00 
The Law of Total Probability
00:00 
The Additive Rule
00:00 
The Multiplication Law
00:00 
Bayes’ Law
00:00 
A Practical Example of Bayesian Inference
00:00
Probability – Distributions

Fundamentals of Probability Distributions
00:00 
Types of Probability Distributions
00:00 
Characteristics of Discrete Distributions
00:00 
Discrete Distributions: The Uniform Distribution
00:00 
Discrete Distributions: The Bernoulli Distribution
00:00 
Discrete Distributions: The Binomial Distribution
00:00 
Discrete Distributions: The Poisson Distribution
00:00 
Characteristics of Continuous Distributions
00:00 
Continuous Distributions: The Normal Distribution
00:00 
Continuous Distributions: The Standard Normal Distribution
00:00 
Continuous Distributions: The Students’ T Distribution
00:00 
Continuous Distributions: The ChiSquared Distribution
00:00 
Continuous Distributions: The Exponential Distribution
00:00 
Continuous Distributions: The Logistic Distribution
00:00
Probability – Probability in Other Fields

Probability in Finance
00:00 
Probability in Statistics
00:00 
Probability in Data Science
00:00
Part 3 – Statistics

Population and Sample
00:00
Statistics – Descriptive Statistics

Types of Data
00:00 
Levels of Measurement
00:00 
Categorial Variables – Visualization Techniques
00:00 
Numerical Variables – Frequency Distribution Table
00:00 
The Histogram
00:00 
Cross Tables and Scatter Plots
00:00 
Mean, median and mode
00:00 
Skewness
00:00 
Variance
00:00 
Standard Deviation and Coefficient of Variation
00:00 
Covariance
00:00 
Correlation Coefficient
00:00
Statistics – Practical Examples: Descriptive Statistics

Practical Examples: Descriptive Statistics
00:00
Statistics: Inferential Statistics Fundamentals

Introduction
00:00 
What is a Distribution
00:00 
The Normal Distribution
00:00 
The Standard Normal Distribution
00:00 
Central Limit Theorem
00:00 
Standard error
00:00 
Estimators and Estimates
00:00
Statistics: Inferential Statistics: Confidence Intervals

What are Confidence Intervals?
00:00 
Confidence Intervals; Population Variance Known; Zscore
00:00 
Confidence Interval Clarifications
00:00 
Student’s T Distribution
00:00 
Confidence Intervals; Population Variance Unknown; Tscore
00:00 
Margin of Error
00:00 
Confidence intervals. Two means. Dependent samples
00:00 
Confidence intervals. Two means. Independent Samples (Part 1)
00:00 
Confidence intervals. Two means. Independent Samples (Part 2)
00:00 
Confidence intervals. Two means. Independent Samples (Part 3)
00:00
Statistics: Practical Example: Inferential Intervals

Practical Example: Inferential Statistics
00:00
Statistics: Hypothesis Testing

Null vs Alternative Hypothesis
00:00 
Rejection Region and Significance Level
00:00 
Type I Error and Type II Error
00:00 
Test for the Mean. Population Variance Known
00:00 
pvalue
00:00 
Test for the Mean. Population Variance Unknown
00:00 
Test for the Mean. Dependent Samples
00:00 
Test for the mean. Independent Samples (Part 1)
00:00 
Test for the mean. Independent Samples (Part 2)
00:00
Statistics: Practical Example – Hypothesis Testing

Practical Example: Hypothesis Testing
00:00
Part 4: Python

Introduction to Programming
00:00 
Why Python?
00:00 
Why Jupyter?
00:00 
Installing Python and Jupyter
00:00 
Understanding Jupyter’s Interface – the Notebook Dashboard
00:00 
Prerequisites for Coding in the Jupyter Notebooks
00:00
Python – Variables and Data Types

Variables
00:00 
Numbers and Boolean Values in Python
00:00 
Python Strings
00:00
Python: Basic Python Syntax

Using Arithmetic Operators in Python
00:00 
The Double Equality Sign
00:00 
How to Reassign Values
00:00 
Add Comments
00:00 
Understanding Line Continuation
00:00 
Indexing Elements
00:00 
Structuring with Indentation
00:00
Python – Other Python Operators

Comparison Operators
00:00 
Logical and Identity Operators
00:00
Python – Conditional Statements

The IF Statement
00:00 
The ELSE Statement
00:00 
The ELIF Statement
00:00 
A Note on Boolean Values
00:00
Python – Python Functions

Defining a Function in Python
00:00 
How to Create a Function with a Parameter
00:00 
Defining a Function in Python – Part II
00:00 
How to Use a Function within a Function
00:00 
Conditional Statements and Functions
00:00 
Functions Containing a Few Arguments
00:00 
Builtin Functions in Python
00:00
Python – Python Sequences

Lists
00:00 
Using Methods
00:00 
List Slicing
00:00 
Tuples
00:00 
Dictionaries
00:00
Python – Iterations

For Loops
00:00 
While Loops and Incrementing
00:00 
Lists with the range() Function
00:00 
Conditional Statements and Loops
00:00 
How to Iterate over Dictionaries
00:00
Python – Advance Python Tools

Object Oriented Programming
00:00 
Modules and Packages
00:00 
What is the Standard Library?
00:00 
Importing Modules in Python
00:00
Additional Statistical Methods in Python

Introduction to Regression Analysis
00:00
Additional Statistical Methods – Linear Regression with StatsModels

The Linear Regression Model
00:00 
Correlation vs Regression
00:00 
Geometrical Representation of the Linear Regression Model
00:00 
Python Packages Installation
00:00 
First Regression in Python
00:00 
Using Seaborn for Graphs
00:00 
How to Interpret the Regression Table
00:00 
Decomposition of Variability
00:00 
What is the OLS?
00:00 
RSquared
00:00
Additional Statistical Methods – Multiple Linear Regression with StatsModels

Multiple Linear Regression
00:00 
Adjusted RSquared
00:00 
Test for Significance of the Model (FTest)
00:00 
OLS Assumptions
00:00 
A1: Linearity
00:00 
A2: No Endogeneity
00:00 
A3: Normality and Homoscedasticity
00:00 
A4: No Autocorrelation
00:00 
A5: No Multicollinearity
00:00 
Dealing with Categorical Data – Dummy Variables
00:00 
Making Predictions with the Linear Regression
00:00
Additional Statistical Methods – Linear Regression with sklearn

What is sklearn and How is it Different from Other Packages
00:00 
How are we Going to Approach this Section?
00:00 
Simple Linear Regression with sklearn
00:00 
Simple Linear Regression with sklearn – A StateModels like Summary Table
00:00 
Multiple Linear Regression with sklearn
00:00 
Calculating the Adjusted RSquared in sklearn
00:00 
Feature Selection (Fregression)
00:00 
Creating a Summary Table with Pvalues
00:00 
Feature Scaling (Standardization)
00:00 
Feature Selection through Standardization of Weights
00:00 
Predicting with the Standardized Coefficients
00:00 
Underfitting and Overfitting
00:00 
Train – Test Split Explained
00:00
Additional Statistical Methods – Practical Example: Linear Regression

Practical Example: Linear Regression (Part 1)
00:00 
Practical Example: Linear Regression (Part 2)
00:00 
Practical Example: Linear Regression (Part 3)
00:00 
Practical Example: Linear Regression (Part 4)
00:00 
Practical Example: Linear Regression (Part 5)
00:00
Additional Statistical Methods – Logistic Regression

Introduction to Logistic Regression
00:00 
A Simple Example in Python
00:00 
Logistic vs Logit Function
00:00 
Building a Logistic Regression
00:00 
An Invaluable Coding Tip
00:00 
Understanding Logistic Regression Tables
00:00 
What do the Odds Actually Mean
00:00 
Binary Predictors in a Logistic Regression
00:00 
Calculating the Accuracy of the Model
00:00 
Underfitting and Overfitting
00:00 
Testing the Model
00:00
Additional Statistical Methods – Cluster Analysis

Introduction to Cluster Analysis
00:00 
Some Examples of Clusters
00:00 
Difference between Classification and Clustering
00:00 
Math Prerequisites
00:00
Additional Statistical Methods – KMean Clustering

KMeans Clustering
00:00 
A Simple Example of Clustering
00:00 
Clustering Categorical Data
00:00 
How to Choose the Number of Clusters
00:00 
Pros and Cons of KMeans Clustering
00:00 
To Standardize or not to Standardize
00:00 
Relationship between Clustering and Regression
00:00 
Market Segmentation with Cluster Analysis (Part 1)
00:00 
Market Segmentation with Cluster Analysis (Part 2)
00:00 
How is Clustering Useful?
00:00
Additional Statistical Methods – Other Types of Clustering

Types of Clustering
00:00 
Dendrogram
00:00 
Heatmaps
00:00
Part 5: Mathematics

What is a Matrix?
00:00 
Scalars and Vectors
02:59 
Linear Algebra and Geometry
00:00 
Arrays in Python – A Convenient Way To Represent Matrices
00:00 
What is a Tensor?
00:00 
Addition and Subtraction of Matrices
00:00 
Errors when Adding Matrices
00:00 
Transpose of a Matrix
00:00 
Dot Product
00:00 
Dot Product of Matrices
00:00 
Why is Linear Algebra Useful?
00:00
Part 6: Deep Learning

What to Expect from this Part?
00:00
Deep Learning – Introduction to Neural Networks

Introduction to Neural Networks
00:00 
Training the Model
00:00 
Types of Machine Learning
00:00 
The Linear Model (Linear Algebraic Version)
00:00 
The Linear Model with Multiple Inputs
00:00 
The Linear model with Multiple Inputs and Multiple Outputs
00:00 
Graphical Representation of Simple Neural Networks
00:00 
What is the Objective Function?
00:00 
Common Objective Functions: L2norm Loss
00:00 
Common Objective Functions: CrossEntropy Loss
00:00 
Optimization Algorithm: 1Parameter Gradient Descent
00:00 
Optimization Algorithm: nParameter Gradient Descent
00:00
Deep Learning – How to Build a Neural Network from Scratch

Basic NN Example (Part 1)
00:00 
Basic NN Example (Part 2)
00:00 
Basic NN Example (Part 3)
00:00 
Basic NN Example (Part 4)
00:00
Deep Learning – TensorFlow 2.0: Introduction

How to Install TensorFlow 2.0
00:00 
TensorFlow Outline and Comparison with Other Libraries
00:00 
TensorFlow 1 vs TensorFlow 2
00:00 
A Note on TensorFlow 2 Syntax
00:00 
Types of File Formats Supporting TensorFlow
00:00 
Outlining the Model with TensorFlow 2
00:00 
Interpreting the Result and Extracting the Weights and Bias
00:00 
Customizing a TensorFlow 2 Model
00:00
Deep Learning: Diving Deep into NNs: Introducing Deep Neural Networks

What is a Layer?
00:00 
What is a Deep Net?
00:00 
Digging into a Deep Net
00:00 
NonLinearities and their Purpose
00:00 
Activation Functions
00:00 
Activation Functions: Softmax Activation
00:00 
Backpropagation
00:00 
Backpropagation Picture
00:00
Deep Learning: Overfitting

What is Overfitting?
00:00 
Underfitting and Overfitting for Classification
00:00 
What is Validation?
00:00 
Training, Validation, and Test Datasets
00:00 
NFold Cross Validation
00:00 
Early Stopping or When to Stop Training
00:00
Deep Learning: Initialization

What is Initialization?
00:00 
Types of Simple Initializations
00:00 
StateoftheArt Method – (Xavier) Glorot Initialization
00:00
Deep Learning: Digging into Gradient Descent and Learning Rate Schedule

Stochastic Gradient Descent
00:00 
Problems with Gradient Descent
00:00 
Momentum
02:30 
Learning Rate Schedules, or How to Choose the Optimal Learning Rate
00:00 
Learning Rate Schedules Visualized
00:00 
Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
00:00 
Adam (Adaptive Moment Estimation)
00:00
Deep Learning: Preprocessing

Preprocessing Introduction
00:00 
Types of Basic Preprocessing
00:00 
Standardization
00:00 
Preprocessing Categorical Data
00:00 
Binary and OneHot Encoding
00:00
Deep Learning – Classifying on the MNIST Dataset

MNIST: The Dataset
00:00 
MNIST: How to Tackle the MNIST
00:00 
MNIST: Importing the Relevant Packages and Loading the Data
00:00 
MNIST: Preprocess the Data – Create a Validation Set and Scale It
00:00 
MNIST: Preprocess the Data – Shuffle and Batch
00:00 
MNIST: Outline the Model
00:00 
MNIST: Select the Loss and the Optimizer
00:00 
MNIST: Learning
00:00 
MNIST: Testing the Model
00:00
Deep Learning – Business Case Example

Business Case: Exploring the Dataset and Identifying Predictors
00:00 
Business Case: Outlining the Solution
00:00 
Business Case: Balancing the Dataset
00:00 
Business Case: Preprocessing the Data
00:00 
Business Case: Load the Preprocessed Data
00:00 
Business Case: Learning and Interpreting the Result
00:00 
Business Case: Setting an Early Stopping Mechanism
00:00 
Business Case: Testing the Model
00:00
Deep Learning – Conslusion

Summary on What You’ve Learned
00:00 
What’s Further out there in terms of Machine Learning
00:00 
An overview of CNNs
00:00 
An Overview of RNNs
00:00 
An Overview of nonNN Approaches
00:00
Appendix – Deep Learning – TensorFlow 1: Introduction

How to Install TensorFlow 1
00:00 
TensorFlow Intro
00:00 
Actual Introduction to TensorFlow
00:00 
Types of File Formats, supporting Tensors
00:00 
Basic NN Example with TF: Inputs, Outputs, Targets, Weights, Biases
00:00 
Basic NN Example with TF: Loss Function and Gradient Descent
00:00 
Basic NN Example with TF: Model Output
00:00
Appendix – Deep Learning – TensorFlow 1: Classifying on MNIST Dataset

MNIST: What is the MNIST Dataset?
00:00 
MNIST: How to Tackle the MNIST
00:00 
MNIST: Relevant Packages
00:00 
MNIST: Model Outline
00:00 
MNIST: Loss and Optimization Algorithm
00:00 
Calculating the Accuracy of the Model
00:00 
MNIST: Batching and Early Stopping
00:00 
MNIST: Learning
00:00 
MNIST: Results and Testing
00:00
Appendix – Deep Learning – TensorFlow 1: Business Case

Business Case: Getting Acquainted with the Dataset
00:00 
Business Case: Outlining the Solution
00:00 
The Importance of Working with a Balanced Dataset
00:00 
Business Case: Preprocessing
00:00 
Business Case: Model Outline
01:58 
Business Case: Optimization
00:00 
Business Case: Interpretation
00:00 
Business Case: Testing the Model
00:00 
Business Case: A Comment on the Homework
00:00
Software Integration

What are Data, Servers, Clients, Requests, and Responses
00:00 
What are Data Connectivity, APIs, and Endpoints?
00:00 
Taking a Closer Look at APIs
00:00 
Communication between Software Products through Text Files
00:00 
Software Integration – Explained
00:00
Case Study – What’s Next in this Course

Game Plan for this Python, SQL, and Tableau Business Exercise
00:00 
The Business Task
00:00 
Introducing the Data Set
00:00
Case Study – Preprocessing the ‘Absenteesim_data’

Importing the Absenteeism Data in Python
00:00 
Checking the Content of the Data Set
00:00 
Introduction to Terms with Multiple Meanings
00:00 
Using a Statistical Approach towards the Solution to the Exercise
00:00 
Dropping a Column from a DataFrame in Python
00:00 
Analyzing the Reasons for Absence
00:00 
Obtaining Dummies from a Single Feature
00:00 
More on Dummy Variables: A Statistical Perspective
00:00 
Classifying the Various Reasons for Absence
00:00 
Using .concat() in Python
00:00 
Reordering Columns in a Pandas DataFrame in Python
00:00 
Creating Checkpoints while Coding in Jupyter
00:00 
Analyzing the Dates from the Initial Data Set
00:00 
Extracting the Month Value from the “Date” Column
00:00 
Extracting the Day of the Week from the “Date” Column
00:00 
Analyzing Several “Straightforward” Columns for this Exercise
00:00 
Working on “Education”, “Children”, and “Pets”
00:00 
Final Remarks of this Section
00:00
Case Study – Applying Machine Learning to ‘Absenteesim_Module’

Exploring the Problem with a Machine Learning Mindset
00:00 
Creating the Targets for the Logistic Regression
00:00 
Selecting the Inputs for the Logistic Regression
00:00 
Standardizing the Data
00:00 
Splitting the Data for Training and Testing
00:00 
Fitting the Model and Assessing its Accuracy
00:00 
Creating a Summary Table with the Coefficients and Intercept
00:00 
Interpreting the Coefficients for Our Problem
00:00 
Standardizing only the Numerical Variables (Creating a Custom Scaler)
00:00 
Interpreting the Coefficients of the Logistic Regression
00:00 
Backward Elimination or How to Simplify Your Model
00:00 
Testing the Model We Created
00:00 
Saving the Model and Preparing it for Deployment
00:00 
Preparing the Deployment of the Model through a Module
00:00
Case Study – Loading the ‘absenteesim_module’

Deploying the ‘absenteeism_module’ – Part I
00:00 
Deploying the ‘absenteeism_module’ – Part II
00:00
Case Study – Analyzing the Predicted Output in Tableu

Analyzing Age vs Probability in Tableau
00:00 
Analyzing Reasons vs Probability in Tableau
00:00 
Analyzing Transportation Expense vs Probability in Tableau
00:00 
Assignment: EndtoEnd Data Science Project: Predictive Analytics
00:00