4.55 (84816 reviews)

$99.99

Regular PriceWhat you will 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

Description

**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 2021.*

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.

**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 real-life business cases that will get you the job

**You will become a data scientist from scratch**

We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

**Why wait? Every day is a missed opportunity.**

**Click the “Buy Now” button and become a part of our data scientist program today.
**

** **

Screenshots

Content

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?

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

What is the difference between Analysis and Analytics

What is the difference between Analysis and Analytics

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

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

Continuing with BI, ML, and AI

Continuing with BI, ML, and AI

A Breakdown of our Data Science Infographic

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

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 Reason behind these Disciplines

The Field of Data Science - Popular Data Science Techniques

Techniques for Working with Traditional Data

Techniques for Working with Traditional Data

Real Life Examples of Traditional Data

Techniques for Working with Big Data

Techniques for Working with Big Data

Real Life Examples of Big Data

Business Intelligence (BI) Techniques

Business Intelligence (BI) Techniques

Real Life Examples of Business Intelligence (BI)

Techniques for Working with Traditional Methods

Techniques for Working with Traditional Methods

Real Life Examples of Traditional Methods

Machine Learning (ML) Techniques

Machine Learning (ML) Techniques

Types of Machine Learning

Types of Machine Learning

Real Life Examples of Machine Learning (ML)

Real Life Examples of Machine Learning (ML)

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

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

The Field of Data Science - Debunking Common Misconceptions

Debunking Common Misconceptions

Debunking Common Misconceptions

Part 2: Probability

The Basic Probability Formula

The Basic Probability Formula

Computing Expected Values

Computing Expected Values

Frequency

Frequency

Events and Their Complements

Events and Their Complements

Probability - Combinatorics

Fundamentals of Combinatorics

Fundamentals of Combinatorics

Permutations and How to Use Them

Permutations and How to Use Them

Simple Operations with Factorials

Simple Operations with Factorials

Solving Variations with Repetition

Solving Variations with Repetition

Solving Variations without Repetition

Solving Variations without Repetition

Solving Combinations

Solving Combinations

Symmetry of Combinations

Symmetry of Combinations

Solving Combinations with Separate Sample Spaces

Solving Combinations with Separate Sample Spaces

Combinatorics in Real-Life: The Lottery

Combinatorics in Real-Life: The Lottery

A Recap of Combinatorics

A Practical Example of Combinatorics

Probability - Bayesian Inference

Sets and Events

Sets and Events

Ways Sets Can Interact

Ways Sets Can Interact

Intersection of Sets

Intersection of Sets

Union of Sets

Union of Sets

Mutually Exclusive Sets

Mutually Exclusive Sets

Dependence and Independence of Sets

Dependence and Independence of Sets

The Conditional Probability Formula

The Conditional Probability Formula

The Law of Total Probability

The Additive Rule

The Additive Rule

The Multiplication Law

The Multiplication Law

Bayes' Law

Bayes' Law

A Practical Example of Bayesian Inference

Probability - Distributions

Fundamentals of Probability Distributions

Fundamentals 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

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

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

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

Types of Data

Levels of Measurement

Levels of Measurement

Categorical Variables - Visualization Techniques

Categorical Variables - Visualization Techniques

Categorical Variables Exercise

Numerical Variables - Frequency Distribution Table

Numerical Variables - Frequency Distribution Table

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

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

Correlation Coefficient

Correlation

Correlation Coefficient Exercise

Statistics - Practical Example: Descriptive Statistics

Practical Example: Descriptive Statistics

Practical Example: Descriptive Statistics Exercise

Statistics - Inferential Statistics Fundamentals

Introduction

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

Central Limit Theorem

Standard error

Standard Error

Estimators and Estimates

Estimators and Estimates

Statistics - Inferential Statistics: Confidence Intervals

What are Confidence Intervals?

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

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)

Statistics - Practical Example: Inferential Statistics

Practical Example: Inferential Statistics

Practical Example: Inferential Statistics Exercise

Statistics - Hypothesis Testing

Null vs Alternative Hypothesis

Further Reading on Null and Alternative Hypothesis

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

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)

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

Statistics - Practical Example: Hypothesis Testing

Practical Example: Hypothesis Testing

Practical Example: Hypothesis Testing Exercise

Part 4: Introduction to Python

Introduction to Programming

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

Jupyter's Interface

Python 2 vs Python 3

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

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

Indexing Elements

Indexing Elements

Structuring with Indentation

Structuring with Indentation

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

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

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

Advanced Statistical Methods - Multiple Linear Regression with StatsModels

Multiple Linear Regression

Multiple 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

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

Making Predictions with the Linear Regression

Advanced Statistical Methods - Linear Regression with sklearn

What is sklearn and How is it Different from Other Packages

How are 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

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

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

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

Introduction to Cluster Analysis

Some Examples of Clusters

Difference between Classification and Clustering

Math Prerequisites

Advanced Statistical Methods - K-Means Clustering

K-Means Clustering

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

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

Types of Clustering

Dendrogram

Heatmaps

Part 6: Mathematics

What is a matrix?

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?

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?

Part 7: Deep Learning

What to Expect from this Part?

What is Machine Learning

Deep Learning - Introduction to Neural Networks

Introduction to Neural Networks

Introduction to Neural Networks

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

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

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

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: 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

MNIST: Learning

MNIST - Exercises

MNIST: Testing the Model

Deep Learning - Business Case Example

Business Case: Exploring the Dataset and Identifying Predictors

Business Case: Outlining the Solution

Business Case: Balancing the Dataset

Business Case: Preprocessing the Data

Business Case: 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

Setting an Early Stopping Mechanism - Exercise

Business Case: Testing the Model

Business Case: Final Exercise

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!!!!

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

Basic NN Example with TF Exercises

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

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: Solutions

MNIST: Exercises

Appendix: Deep Learning - TensorFlow 1: Business Case

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

Software Integration

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

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

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?

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 .concat() in Python

EXERCISE - Using .concat() in Python

SOLUTION - Using .concat() 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

A Note on Exporting Your Data as a *.csv File

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

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'

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

Reviews

J

Joyeuse9 October 2020

I love the content but it would be great if for quizzes I had more than one question or two to test at which level I understood that chapter

D

Dmitry9 October 2020

The course material is being presented in a very interesting, engaging and clear manner. I very much like the step by step approach!

L

Leandro9 October 2020

Excellent course and I recommend to anyone who has interest in the Data Science field. The content presented was very well developed and easy to understand. It was definitely a good investment. Thanks!!!

D

Dhaval9 October 2020

Until now I am feeling awesome for the way the course is designed from the very basics. I am very exited for the course.

N

Novecento9 October 2020

Il linguaggio utilizzato é molto semplice e chiaro. Sono molto soddisfatto. I concetti vengono spiegati in modo chiarissimo.

V

Vibhav27 February 2020

its an amazing coarse for all the beginners who wants to start a career in the data science field as all the topics are covered from the very scratch and deep insights in every section is helpful.

F

Fernando26 February 2020

Por ahora sí, me gustaría tener accesible y visible en todo momento lo siguiente: 1. Un temario 2. Conocer el tiempo que debo dedicar a cada sección del curso, para poder organizarme y saber si comienzo o no una nueva sección

K

Kevin26 February 2020

Enjoying the course so far - can't wait to complete it! I have spent about 2 hours and I have already learned so much! Highly recommended!

D

Daniel25 February 2020

I liked it, it is more complete than I expected... I was surprised by the mathematical and statistical component... it is not common to see them in a data science course, even with their approach to the subject.

M

Maija25 February 2020

Very well-presented and well-explained, detailed course. Takes a while to go through because it's so packed with useful information. Advised it to my friends who are interested in data science. It is difficult to imagine that there can be anything better online than this to help progress within the data science field!

K

Ksemba25 February 2020

Covers lots of topics in a very clear way. Gives lots of examples that makes sense. Best course so far that I've tried on ML / AI

J

Jessica24 February 2020

This course is good match for me and so nice to give basic concepts and history. I like this course and is expecting further courses.

J

Joanne23 February 2020

Yes, by far it gives me a good introduction to what data science is about and the presentation slides are easy to understand for a complete beginners.

S

Steve22 February 2020

Information is good but sometimes it’s hard to practice in the tool. Part of me wants to be forced to get a better understanding.

S

Shridutt22 February 2020

I was always confused with so many buzz words - data analytics, BI, business analytics, etc. The details covered in this course so far are just amazing, explains the difference very clearly and to the point. Five Stars!

Coupons

Status | Date | Discount | ||
---|---|---|---|---|

Valid | 1/27/2021 | 95% OFF | ||

Bot

Course Submitted by