Courses & Tutorials

The Data Science Course – Complete Data Science Bootcamp


The problem

Data scientist is one of the most suitable professions to thrive in this century. It is digital, programming-oriented and analytical. Therefore, it is not surprising that the demand for data scientists in the job market has surged.

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

How can you do this?

Universities are making slow progress in creating specialized data science projects. (Not to mention the ones that exist are very expensive and time-consuming)

Most online courses focus on specific topics, and it is difficult to understand how the skills they teach fit the overall picture

The solution

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

  • Understand the field of data science and the type of analysis performed
  • Math
  • Statistical data
  • Python
  • Apply advanced statistical techniques in Python
  • Data visualization
  • Machine learning
  • Deep learning

Each of these themes builds on previous themes. If you do not acquire these skills in the correct order, you may get lost in the process. For example, before understanding basic mathematics, people will struggle with the application of machine learning technology. Or, studying regression analysis in Python before understanding what regression is can be overwhelming.

Therefore, in order to create the most effective, time-saving, and structured online data science training, we created the 2021 Data Science Course.

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

In addition, our focus is on teaching fluent and complementary topics. This course teaches you everything you need to become a data scientist at a fraction of the cost of a traditional course (not to mention the time you will save).

The Skills

1. Introduction to Data and Data Science

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

Why should I learn? As a candidate data scientist, you must understand the ins and outs of each field and recognize the appropriate method to solve the problem. This “Introduction to Data and Data Science” will give you a comprehensive understanding of all these buzzwords and their place in the field of data science.

2. Mathematics

Learning tools is the first step in data science. You have to look at the big picture first, and then check the parts in detail.

We specifically studied calculus and linear algebra in detail because they are subfields that data science relies on.

Why should I learn?

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

3. Statistics

Before becoming a scientist, you need to think like a scientist. Statistics trains your mind to frame questions as hypotheses and provides you with techniques to test those hypotheses, just like a scientist.

Why should I learn?

This course not only provides you with the tools you need, but also teaches you how to use them. Statistics train you to think like a scientist.

4. Python

Python is a relatively new programming language. Unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games, and data science are among its many functions. This is why it has successfully subverted many disciplines in a short period of time. An extremely powerful library has been developed to implement data manipulation, conversion, and visualization. However, where Python really shines is that it handles machine learning and deep learning.

Why should I learn?

When developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn and TensorFlow, Python is an indispensable programming language.

5. Screen

Data scientists don’t just need to process data and solve data-driven problems. They also need to convince company executives to make the right decision. These executives may not be proficient in data science, so data scientists must be able to present and visualize data stories in a way they can understand. This is where Tableau comes in-we will help you become an expert in storytelling using the leading visualization software in business intelligence and data science.

Why should I learn?

Data scientists rely on business intelligence tools such as Tableau to communicate complex results to non-technical decision makers.

6. Advanced Statistics

Regression, clustering and factor analysis are all disciplines invented before machine learning. However, these statistical methods are now performed through machine learning to provide predictions with unparalleled accuracy. This section will introduce these technologies in detail.

Why should I learn?

Data science is all about predictive modeling, and you can become an expert in these methods through the “Advanced Statistics” section.

7. Machine learning

The last part of the plan and the content of each part are deep learning. Being able to use machine learning and deep learning in their work is usually the difference between data scientists and data analysts. This section covers all common machine learning techniques and deep learning methods that use TensorFlow.

Why should I learn?

Machine learning is everywhere. Companies such as Facebook, Google, and Amazon have been using machines that can learn on their own for years. It’s time for you to control the machine.

This course is suitable for:

  • If you want to be a data scientist or you want to understand this field, you should take this course
  • If you want a great career, this course is for you
  • This course is also ideal for beginners as it starts from the basics and gradually builds up your skills

What you will learn

  • This course provides all the toolboxes needed to become a data scientist
  • Fill out your resume with the required data science skills: statistical analysis, Python programming using NumPy, pandas, matplotlib and Seaborn, advanced statistical analysis, Tableau, machine learning using statistical models and scikit-learn, and deep learning using TensorFlow
  • Impress interviewers by showing their understanding of the field of data science
  • Learn how to preprocess data
  • Understand the mathematics behind machine learning (absolutely necessary for other courses not taught!)
  • Start coding in Python and learn how to use it for statistical analysis
  • Perform linear and logistic regression in Python
  • Perform clustering and factor analysis
  • Ability to create machine learning algorithms in Python using NumPy, statsmodels and scikit-learn
  • Apply your skills to real-life business cases
  • Use the most advanced deep learning frameworks, such as Google’s TensorFlow to develop business intuition while using big data to encode and solve tasks
  • Show the power of deep neural networks
  • Improve machine learning algorithms by studying how underfitting, overfitting, training, validation, n-fold cross-validation, testing, and hyperparameters can improve performance
  • Warm up your fingers, because you will be eager to apply everything you learn here to more and more real life


  • Course Length:  27 Hours
  • Course Size: 13.3 Go
  • Number of sections: 56

How to Get the Course

Write an email to for more detail

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