The Data Science Course: Complete Data Science Bootcamp 2025
All LevelsDevelopmentData Science

The Data Science Course: Complete Data Science Bootcamp 2025

Complete Data Science Training: Math, Statistics, Python, Advanced Statistics in Python, Machine and Deep Learning

Created by 365 Careers
31.5 hours
Video Content
525
Lectures
765,554
Students
4.6
Rating
4.6
(765,554 students enrolled)

What you'll learn

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

Course Content

66 sections β€’ 525 lectures β€’ 32:07:54 total length

Part 1: Introduction

3 lectures β€’ 19:21

A Practical Example: What You Will Learn in This Course05:05
What Does the Course Cover03:34
Download All Resources and Important FAQ10:42

The Field of Data Science - The Various Data Science Disciplines

7 lectures β€’ 38:57

Data Science and Business Buzzwords: Why are there so Many?05:21
Data Science and Business Buzzwords: Why are there so Many?1 question
What is the difference between Analysis and Analytics03:50
What is the difference between Analysis and Analytics1 question
Business Analytics, Data Analytics, and Data Science: An Introduction06:39
+8 more lectures

The Field of Data Science - Connecting the Data Science Disciplines

1 lectures β€’ 07:19

Applying Traditional Data, Big Data, BI, Traditional Data Science and ML07:19

The Field of Data Science - The Benefits of Each Discipline

1 lectures β€’ 04:44

The Reason Behind These Disciplines04:44
The Reason Behind These Disciplines1 question

The Field of Data Science - Popular Data Science Techniques

12 lectures β€’ 59:56

Techniques for Working with Traditional Data08:13
Techniques for Working with Traditional Data1 question
Real Life Examples of Traditional Data01:44
Techniques for Working with Big Data04:26
Techniques for Working with Big Data1 question
+14 more lectures

The Field of Data Science - Popular Data Science Tools

1 lectures β€’ 05:51

Necessary Programming Languages and Software Used in Data Science05:51
Necessary Programming Languages and Software Used in Data Science4 questions

The Field of Data Science - Careers in Data Science

1 lectures β€’ 03:29

Finding the Job - What to Expect and What to Look for03:29
Finding the Job - What to Expect and What to Look for1 question

The Field of Data Science - Debunking Common Misconceptions

1 lectures β€’ 04:10

Debunking Common Misconceptions04:10
Debunking Common Misconceptions1 question

Part 2: Probability

4 lectures β€’ 23:04

The Basic Probability Formula07:09
The Basic Probability Formula3 questions
Computing Expected Values05:29
Computing Expected Values3 questions
Frequency05:00
+3 more lectures

Probability - Combinatorics

11 lectures β€’ 42:56

Fundamentals of Combinatorics01:04
Fundamentals of Combinatorics1 question
Permutations and How to Use Them03:21
Permutations and How to Use Them2 questions
Simple Operations with Factorials03:35
+15 more lectures

Probability - Bayesian Inference

12 lectures β€’ 54:38

Sets and Events04:25
Sets and Events3 questions
Ways Sets Can Interact03:45
Ways Sets Can Interact2 questions
Intersection of Sets02:06
+17 more lectures

Probability - Distributions

15 lectures β€’ 01:17:12

Fundamentals of Probability Distributions06:29
Fundamentals of Probability Distributions3 questions
Types of Probability Distributions07:32
Types of Probability Distributions2 questions
Characteristics of Discrete Distributions02:00
+24 more lectures

Probability - Probability in Other Fields

3 lectures β€’ 18:51

Probability in Finance07:46
Probability in Statistics06:18
Probability in Data Science04:47

Part 3: Statistics

1 lectures β€’ 04:02

Population and Sample04:02
Population and Sample2 questions

Statistics - Descriptive Statistics

22 lectures β€’ 48:11

Types of Data04:33
Types of Data2 questions
Levels of Measurement03:43
Levels of Measurement2 questions
Categorical Variables - Visualization Techniques04:52
+27 more lectures

Statistics - Practical Example: Descriptive Statistics

2 lectures β€’ 16:18

Practical Example: Descriptive Statistics16:15
Practical Example: Descriptive Statistics Exercise00:03

Statistics - Inferential Statistics Fundamentals

8 lectures β€’ 21:53

Introduction01:00
What is a Distribution04:33
What is a Distribution1 question
The Normal Distribution03:54
The Normal Distribution1 question
+9 more lectures

Statistics - Inferential Statistics: Confidence Intervals

15 lectures β€’ 44:25

What are Confidence Intervals?02:41
What are Confidence Intervals?1 question
Confidence Intervals; Population Variance Known; Z-score08:01
Confidence Intervals; Population Variance Known; Z-score; Exercise00:03
Confidence Interval Clarifications04:38
+13 more lectures

Statistics - Practical Example: Inferential Statistics

2 lectures β€’ 10:08

Practical Example: Inferential Statistics10:05
Practical Example: Inferential Statistics Exercise00:03

Statistics - Hypothesis Testing

15 lectures β€’ 48:24

Null vs Alternative Hypothesis05:51
Further Reading on Null and Alternative Hypothesis01:16
Null vs Alternative Hypothesis2 questions
Rejection Region and Significance Level07:05
Rejection Region and Significance Level2 questions
+15 more lectures

Statistics - Practical Example: Hypothesis Testing

2 lectures β€’ 07:19

Practical Example: Hypothesis Testing07:16
Practical Example: Hypothesis Testing Exercise00:03

Part 4: Introduction to Python

6 lectures β€’ 26:46

Introduction to Programming05:03
Introduction to Programming2 questions
Why Python?05:11
Why Python?2 questions
Why Jupyter?03:28
+5 more lectures

Python - Variables and Data Types

4 lectures β€’ 17:20

Variables03:37
Python Coding Exercises - Part I04:58
Python Variables - Exercise #11 question
Python Variables - Exercise #21 question
Python Variables - Exercise #31 question
+16 more lectures

Python - Basic Python Syntax

7 lectures β€’ 11:29

Using Arithmetic Operators in Python03:23
Using Arithmetic Operators in Python - Exercise #11 question
Using Arithmetic Operators in Python - Exercise #21 question
Using Arithmetic Operators in Python - Exercise #31 question
Using Arithmetic Operators in Python - Exercise #41 question
+25 more lectures

Python - Other Python Operators

2 lectures β€’ 07:45

Comparison Operators02:10
Comparison Operators - Exercise #11 question
Comparison Operators - Exercise #21 question
Comparison Operators - Exercise #31 question
Comparison Operators - Exercise #41 question
+9 more lectures

Python - Conditional Statements

4 lectures β€’ 13:33

The IF Statement03:01
The IF Statement - Exercise #11 question
The IF Statement - Exercise #21 question
The IF Statement1 question
The ELSE Statement02:45
+6 more lectures

Python - Python Functions

7 lectures β€’ 18:34

Defining a Function in Python02:02
How to Create a Function with a Parameter03:49
How to Create a Function with a Parameter - Exercise #11 question
How to Create a Function with a Parameter - Exercise #21 question
Defining a Function in Python - Part II02:36
+17 more lectures

Python - Sequences

5 lectures β€’ 34:49

Lists08:18
Lists - Exercise #11 question
Lists - Exercise #21 question
Lists - Exercise #31 question
Lists - Exercise #41 question
+29 more lectures

Python - Iterations

6 lectures β€’ 32:30

For Loops05:40
For Loops - Exercise #11 question
For Loops - Exercise #21 question
For Loops1 question
While Loops and Incrementing05:10
+15 more lectures

Python - Advanced Python Tools

4 lectures β€’ 12:56

Object Oriented Programming05:00
Object Oriented Programming2 questions
Modules and Packages01:05
Modules and Packages2 questions
What is the Standard Library?02:47
+3 more lectures

Part 5: Advanced Statistical Methods in Python

1 lectures β€’ 01:27

Introduction to Regression Analysis01:27
Introduction to Regression Analysis1 question

Advanced Statistical Methods - Linear Regression with StatsModels

11 lectures β€’ 40:55

The Linear Regression Model05:50
The Linear Regression Model2 questions
Correlation vs Regression01:43
Correlation vs Regression1 question
Geometrical Representation of the Linear Regression Model01:25
+13 more lectures

Advanced Statistical Methods - Multiple Linear Regression with StatsModels

13 lectures β€’ 42:18

Multiple Linear Regression02:55
Multiple Linear Regression1 question
Adjusted R-Squared06:00
Adjusted R-Squared3 questions
Multiple Linear Regression Exercise00:03
+15 more lectures

Advanced Statistical Methods - Linear Regression with sklearn

19 lectures β€’ 54:25

What is sklearn and How is it Different from Other Packages02:14
How are we Going to Approach this Section?01:55
Simple Linear Regression with sklearn05:38
Simple Linear Regression with sklearn - A StatsModels-like Summary Table04:48
A Note on Normalization00:09
+14 more lectures

Advanced Statistical Methods - Practical Example: Linear Regression

9 lectures β€’ 37:57

Practical Example: Linear Regression (Part 1)11:59
Practical Example: Linear Regression (Part 2)06:12
A Note on Multicollinearity00:14
Practical Example: Linear Regression (Part 3)03:15
Dummies and Variance Inflation Factor - Exercise00:03
+4 more lectures

Advanced Statistical Methods - Logistic Regression

16 lectures β€’ 40:49

Introduction to Logistic Regression01:19
A Simple Example in Python04:42
Logistic vs Logit Function04:00
Building a Logistic Regression02:48
Building a Logistic Regression - Exercise00:03
+11 more lectures

Advanced Statistical Methods - Cluster Analysis

4 lectures β€’ 14:03

Introduction to Cluster Analysis03:41
Some Examples of Clusters04:31
Difference between Classification and Clustering02:32
Math Prerequisites03:19

Advanced Statistical Methods - K-Means Clustering

15 lectures β€’ 49:01

K-Means Clustering04:41
A Simple Example of Clustering07:48
A Simple Example of Clustering - Exercise00:03
Clustering Categorical Data02:50
Clustering Categorical Data - Exercise00:03
+10 more lectures

Advanced Statistical Methods - Other Types of Clustering

3 lectures β€’ 13:34

Types of Clustering03:39
Dendrogram05:21
Heatmaps04:34

ChatGPT for Data Science

19 lectures β€’ 01:04:57

Traditional data science methods and the role of ChatGPT05:02
How to install ChatGPT01:43
How ChatGPT can boost your productivity01:57
Data Preprocessing with ChatGPT04:38
First attempt at machine learning with ChatGPT04:21
+14 more lectures

Case Study: Train a Naive Bayes Classifier with ChatGPT for Sentiment Analysis

12 lectures β€’ 46:40

Intro to the Case Study02:32
The Naive Bayes Algorithm04:10
Tokenization and Vectorization05:25
Imbalanced Data Sets02:18
Overcome Imbalanced Data in Machine Learning03:32
+7 more lectures

Part 6: Mathematics

11 lectures β€’ 51:01

What is a Matrix?03:37
What is a Matrix?6 questions
Scalars and Vectors02:58
Scalars and Vectors5 questions
Linear Algebra and Geometry03:06
+11 more lectures

Part 7: Deep Learning

1 lectures β€’ 03:07

What to Expect from this Part?03:07

Deep Learning - Introduction to Neural Networks

12 lectures β€’ 42:38

Introduction to Neural Networks04:09
Introduction to Neural Networks1 question
Training the Model02:54
Training the Model3 questions
Types of Machine Learning03:43
+19 more lectures

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

5 lectures β€’ 20:35

Basic NN Example (Part 1)03:06
Basic NN Example (Part 2)04:58
Basic NN Example (Part 3)03:25
Basic NN Example (Part 4)08:15
Basic NN Example Exercises00:51

Deep Learning - TensorFlow 2.0: Introduction

9 lectures β€’ 28:09

How to Install TensorFlow 2.005:02
TensorFlow Outline and Comparison with Other Libraries03:28
TensorFlow 1 vs TensorFlow 202:32
A Note on TensorFlow 2 Syntax00:58
Types of File Formats Supporting TensorFlow02:34
+4 more lectures

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

9 lectures β€’ 25:44

What is a Layer?01:53
What is a Deep Net?02:18
Digging into a Deep Net04:58
Non-Linearities and their Purpose02:59
Activation Functions03:37
+4 more lectures

Deep Learning - Overfitting

6 lectures β€’ 19:36

What is Overfitting?03:51
Underfitting and Overfitting for Classification01:52
What is Validation?03:22
Training, Validation, and Test Datasets02:30
N-Fold Cross Validation03:07
+1 more lectures

Deep Learning - Initialization

3 lectures β€’ 08:04

What is Initialization?02:32
Types of Simple Initializations02:47
State-of-the-Art Method - (Xavier) Glorot Initialization02:45

Deep Learning - Digging into Gradient Descent and Learning Rate Schedules

7 lectures β€’ 20:40

Stochastic Gradient Descent03:24
Problems with Gradient Descent02:02
Momentum02:30
Learning Rate Schedules, or How to Choose the Optimal Learning Rate04:25
Learning Rate Schedules Visualized01:32
+2 more lectures

Deep Learning - Preprocessing

5 lectures β€’ 14:33

Preprocessing Introduction02:51
Types of Basic Preprocessing01:17
Standardization04:31
Preprocessing Categorical Data02:15
Binary and One-Hot Encoding03:39

Deep Learning - Classifying on the MNIST Dataset

12 lectures β€’ 36:34

MNIST: The Dataset02:25
MNIST: How to Tackle the MNIST02:44
MNIST: Importing the Relevant Packages and Loading the Data02:11
MNIST: Preprocess the Data - Create a Validation Set and Scale It04:43
MNIST: Preprocess the Data - Scale the Test Data - Exercise00:03
+7 more lectures

Deep Learning - Business Case Example

12 lectures β€’ 39:19

Business Case: Exploring the Dataset and Identifying Predictors07:54
Business Case: Outlining the Solution01:31
Business Case: Balancing the Dataset03:39
Business Case: Preprocessing the Data11:32
Business Case: Preprocessing the Data - Exercise00:12
+7 more lectures

Deep Learning - Conclusion

6 lectures β€’ 17:26

Summary on What You've Learned03:41
What's Further out there in terms of Machine Learning01:47
DeepMind and Deep Learning00:21
An overview of CNNs04:55
An Overview of RNNs02:50
+1 more lectures

Appendix: Deep Learning - TensorFlow 1: Introduction

10 lectures β€’ 28:52

READ ME!!!!00:21
How to Install TensorFlow 102:20
A Note on Installing Packages in Anaconda01:14
TensorFlow Intro03:46
Actual Introduction to TensorFlow01:40
+5 more lectures

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

11 lectures β€’ 39:31

MNIST: What is the MNIST Dataset?02:26
MNIST: How to Tackle the MNIST02:48
MNIST: Relevant Packages01:34
MNIST: Model Outline06:51
MNIST: Loss and Optimization Algorithm02:39
+6 more lectures

Appendix: Deep Learning - TensorFlow 1: Business Case

12 lectures β€’ 50:57

Business Case: Getting Acquainted with the Dataset07:55
Business Case: Outlining the Solution01:57
The Importance of Working with a Balanced Dataset03:39
Business Case: Preprocessing11:35
Business Case: Preprocessing Exercise00:13
+7 more lectures

Software Integration

5 lectures β€’ 29:38

What are Data, Servers, Clients, Requests, and Responses04:43
What are Data, Servers, Clients, Requests, and Responses2 questions
What are Data Connectivity, APIs, and Endpoints?07:05
What are Data Connectivity, APIs, and Endpoints?2 questions
Taking a Closer Look at APIs08:05
+5 more lectures

Case Study - What's Next in the Course?

3 lectures β€’ 10:14

Game Plan for this Python, SQL, and Tableau Business Exercise04:08
The Business Task02:48
Introducing the Data Set03:18
Introducing the Data Set1 question

Case Study - Preprocessing the 'Absenteeism_data'

33 lectures β€’ 01:29:34

What to Expect from the Following Sections?01:28
Importing the Absenteeism Data in Python03:23
Checking the Content of the Data Set05:53
Introduction to Terms with Multiple Meanings03:27
What's Regression Analysis - a Quick Refresher01:50
+28 more lectures

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

16 lectures β€’ 01:07:05

Exploring the Problem with a Machine Learning Mindset03:20
Creating the Targets for the Logistic Regression06:32
Selecting the Inputs for the Logistic Regression02:41
Standardizing the Data03:26
Splitting the Data for Training and Testing06:12
+11 more lectures

Case Study - Loading the 'absenteeism_module'

4 lectures β€’ 10:58

Are You Sure You're All Set?00:14
Deploying the 'absenteeism_module' - Part I03:50
Deploying the 'absenteeism_module' - Part II06:23
Exporting the Obtained Data Set as a *.csv00:31

Case Study - Analyzing the Predicted Outputs in Tableau

6 lectures β€’ 23:29

EXERCISE - Age vs Probability00:14
Analyzing Age vs Probability in Tableau08:49
EXERCISE - Reasons vs Probability00:15
Analyzing Reasons vs Probability in Tableau07:49
EXERCISE - Transportation Expense vs Probability00:22
+1 more lectures

Appendix - Additional Python Tools

7 lectures β€’ 46:00

Using the .format() Method09:02
Python Coding Exercises - Part II05:35
Using .format() - Exercise #11 question
Using .format() - Exercise #21 question
Using .format() - Exercise #31 question
+23 more lectures

Appendix - pandas Fundamentals

13 lectures β€’ 59:55

Introduction to pandas Series08:33
A Note on Completing the Upcoming Coding Exercises01:22
Introduction to pandas Series - Exercise #11 question
Introduction to pandas Series - Exercise #21 question
Introduction to pandas Series - Exercise #31 question
+20 more lectures

Bonus Lecture

1 lectures β€’ 01:07

Bonus Lecture: Next Steps01:07

Description

*Update 2025:Β Intro to Data Science module updated for recent AI developments*

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.Β  Β  Β 

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

And how can you do that? Β 

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

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

The SolutionΒ  Β 

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

  • Understanding of the data science field and the type of analysis carried out Β 

  • Mathematics Β 

  • StatisticsΒ  Β 

  • PythonΒ  Β 

  • Applying advanced statistical techniques in PythonΒ  Β 

  • Data Visualization Β 

  • Machine Learning Β 

  • Deep Learning Β 

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.Β  Β 

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2024.Β  Β 

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. Β 

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).Β  Β 

The Skills

Β  Β 1.Β 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?Β  Β  Β 

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 β€˜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. Β 

Β  Β 2. MathematicsΒ 

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.Β  Β 

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

Why learn it? Β 

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.

Β  Β 3. StatisticsΒ 

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. Β 

Why learn it? Β 

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.

Β  Β 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?Β  Β 

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. Β 

Β  Β 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?Β  Β 

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

Β  Β 6. Advanced StatisticsΒ 

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. Β 

Why learn it? Β 

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

Β  Β 7. Machine LearningΒ 

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. Β 

Why learn it?Β  Β 

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. Β 

**What you get**

  • A $1250 data science training programΒ  Β 

  • Active Q&A support Β 

  • All the knowledge to get hired as a data scientist Β 

  • A community of data science learners Β 

  • A certificate of completionΒ  Β 

  • Access to future updates Β 

  • Solve real-life business cases that will get you the jobΒ  Β 

You will become a data scientist from scratch Β  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 β€œBuy Now” button and become a part of our data scientist program today. Β 

Β 

Who this course is for:

  • You should take this course if you want to become a Data Scientist or if you want to learn about the field
  • This course is for you if you want a great career
  • The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

This course includes:

  • 31.5 hours on-demand video
  • 93 articles
  • 542 downloadable resources
  • Access on mobile and TV
  • ∞Full lifetime access
  • Certificate of completion

Instructor

365 Careers

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