We hear more and more about data science and it’s a popular term for companies, the web, and schools. So what is this drill? Data science is nothing more than a multidisciplinary discipline whose goal is to use (digital) data to solve real-world problems or bring specific value called “product data”.
What do data science, big data, and data mining means?
The difference between data science and big data is immediate. Big data is the principle of processing and utilizing large amounts of data, but data science does not define constraints on the amount of data. So, when the amount of data to be processed becomes very important, big data technology can be used in data science. On the other hand, the difference between data mining and data science is not so clear that some people confuse the two. The difference between these two terms stems from the fact that data mining is part of data science. Data mining consists solely of data exploitation, and data science is broader because it considers data collection, for example. This definition may seem ambiguous, but it comes from the fact that the field is broad and requires multiple disciplines.
Data Science Related Fields
It is important to understand that the ultimate goal of data science is to solve problems in a specific field. That said, it is essential to have a very good knowledge of your application before embarking on model development. Additionally, the areas listed below do not represent a full list of fields related to data science. Indeed, the purpose of justifying the means is to allow you to do data science in a variety of ways, as long as you are in the context presented above.
In general, data science includes the following disciplines:
It is understood as an application field, which is the field (environment) that wants to realize a data product or solve a problem. Stock market is one of the example. If you want to build a predictive model for traders based on past stock prices.
Mathematics (statistics, probability, linear algebra, analysis, etc.):
Mathematics is heavily involved in data science. In practice, problems are often transformed into mathematical models before being solved.
Computer science is the foundation of data science in that models are implemented as code and/or computer tools. The collection, storage and all processing of digital data is done through computers.
Machine learning technology is increasingly used in data science.
It is essential to master this science as all modeling is in the form of an algorithm. It is important to understand concepts like complexity.
You need it most when faced with complex problems.
Of course, being a data scientist does not mean that you will be an expert in all of these fields (the more knowledge you have in this field, the better). In fact, data science projects are very complex and consist of several stages. So we can find team members with different profiles who are responsible for the exact steps.