Blockchain Technology overcomes almost all issues that the internet and traditional database systems could not solve for good.

Although the internet and traditional database systems were the solution to so many problems and created creative and intelligent solutions. They might be limited and inefficient as a solution to future problems. That is due to several flaws in them.

Flaws of the Internet and Traditional Databases

Money, like mathematics, is a uniquely human creation. Before the idea of Money was invented, people used to barter goods. In other words, they exchanged (goods or services) for other goods or services without using money. The main problem with this method is that you need to find someone who possesses the goods or service you wish to have and this person needs to be willing to exchange it with what you have.

To overcome this issue, people reached to the very basic idea of money by using mediums that have a value that is accepted by everyone such as…

“Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” — Arthur Samuel, 1959

Traditionally, to solve a problem on a computer you need to use rule-based algorithms. However, it is either impossible or not easy to write a rule-based algorithm for every different type of task. For that reason, sometimes it’s more convenient to use the machine learning approach for solving some challenging problems.

To enable the machine to learn you need to train it using a model and data.

Stages of Building a Model in ML

Barton Poulson argues that just a few years ago the terms Big Data and Data Science were practically synonymous. But things are a little different now and it is important to distinguish between the two fields.

What is Big Data?

It is the very large data that is either fast or complex or both. And it is impossible to process it using traditional methods.

Characteristics of Big Data

According to Barton Poulson, the instructor of Data Science Foundations: Fundamentals. It is quite difficult to separate Data Science, Machine Learning, and Artificial Intelligence. That is why there’s no consistent definition, and why there’s so much debate over what one thing is, and what the other one is.

To illustrate, he visualizes the relationships among all of Data Science, Machine Learning, Neural Networks, and Artificial Intelligence in the graph above.

And explains the graph:

  • Between Data Science and ML, there’s a lot of overlap.
  • within ML, there’s a specific approach called Neural Networks NN.
  • AI refers to this not well-defined…

In this story, I will be sharing what I summarized and understood from a chapter of a LinkedIn course (Data Science Foundations: Fundamentals, by Barton Poulson), in addition to other things I researched and studied on my own.

“Data Scientist: The Sexiest Job of the 21st Century” — Thomas H. Davenport and D. J. Patil — Harvard Business Review, October 2012.

Thomas H. Davenport and D. J. Patil argued that Data Scientists have rare qualities that put them in high demand.

These rare qualities of Data Scientist are:

  • They are able to find order, meaning, and value in unstructured data.


Motivated software engineer sharing what I learned about ML, data science, programming languages, and other related topics.

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