Data science is one of the most exciting fields of our time. But why is it so important?

Because companies sit on a treasure trove of data. Data volumes have increased significantly as modern technology has led to the creation and storage of more and more information. It is estimated that 90 percent of the world's data has been created in the last two years. Facebook users, for example, upload 10 million photos every hour. This is where data science and data scientist come into play in this enormous data and meaningful conclusions are drawn from this data by using data science tools. The data scientist's toolkit has two specific skills that have proven to be extremely valuable: data mining and predictive analytics.

To do this, the data scientist uses Machine Learning algorithms. It provides a faster understanding and modeling of the data. These algorithms have provided great advantages for data science applications. Machine Learning combines computer science, mathematics and statistics. Statistics are necessary to make inferences from data. Mathematics is useful for developing machine learning models and lastly computer science is used to implement algorithms.

Data Mining:

Data mining is simply the process of exploring patterns in data that can be translated into insights, whereas predictive analytics based on predictive analytics is the use of data to determine the statistical probability of certain outcomes of a transaction or process. Both of these are essential components of Artificial Intelligence systems. It is also worth noting that the meaning of the term "Artificial Intelligence" has evolved and changed significantly in the last century. Artificial Intelligence refers to machines that are capable of thinking, learning and moving in a similar way to humans. Artificial Intelligence systems can be programmed by humans or created and developed using limited or no human data; which kind of brings us to the third term I’d like to address now, Machine Learning.

The easiest way to define and coincide the relationship between Machine Learning and Artificial Intelligence is that Machine Learning is one of the most up-to-date methods in our experiments to make "learning machines". Artificial Intelligence is a flexible term that can be drawn in different directions used to describe a general concept but Machine Learning is an Artificial Intelligence methodology and thus a subset of Artificial Intelligence.

Machine Learning is achieved by designing structure algorithms that can be trained on data, rather than being specifically trained by a human (usually a data scientist) on how to perform a task. Although this concept is not new (it was first seriously discussed in the mid-20th century), it relies heavily on computing power as well as accessing large amounts of data. Either way, it is necessary to train algorithms until they are good enough in their tasks. And thanks to the emergence of the internet and the falling cost of processor hardware, we can say that this has become a suitable reality for the business world.