We hear more and more about Data Science. It is the buzzword in companies, data science training institute in Hyderabad, on the web, and in schools. What is this discipline?
Data Science is nothing more than a multidisciplinary field whose goal is to use (Digital) data to solve real-life problems or bring a particular value called “Product Data.”
Data science is the extraction of knowledge from data sets. It employs techniques and theories drawn from several other broader areas of mathematics, mainly statistics, information theory, and information technology, including signal processing, probabilistic models, machine learning, statistical learning, computer programming, data engineering, pattern recognition, and learning, visualization, predictive analytics, uncertainty modeling, data storage, data compression, and high computation performance.
What Is The Difference Between Data Science, Big Data, And Data Mining?
The difference between Data Science and Big Data is immediate. Big Data is the discipline that consists of processing and exploiting a large amount of data, while in Data Science, we do not define a constraint on the amount of data. It is, therefore, that we can have recourse to Big Data techniques in Data Science when the quantity of our data to be processed becomes very large.
The difference between Data Mining and Data Science, on the other hand, is a little less obvious to the point that some people confuse the two. If there is any difference between these two terms, Data Mining is a part of Data Science. Since Data Mining only consists of data exploitation, Data Science is broader since it considers the acquisition of data, for example.
This definition may seem vague, but it comes from the fact that the discipline is broad and calls for several disciplines.
Fields Involved In Data Science
It’s important to understand that data science’s end goal is to solve a problem in a specific area. That said, it is essential to have an excellent knowledge of the application domain before embarking on developing a model.
It should also be noted that the areas listed below do not represent an exhaustive list of disciplines involved in data science. Indeed, justifying the means, we can do data science in various ways as long as we are in the context presented above.
