There are a number of disciplines that share a common goal: To use data and turn it into insights or added value; for example, by visualising or analysing the data. These disciplines are Business Intelligence (BI), Data Analytics, Predictive/Advanced Analytics, Data Mining, Data Science, Statistics and Machine Learning. All of these areas are interconnected and overlap to a large extent, so it can be difficult to distinguish them from each other. Different people therefore define them differently.
Data science is probably the fuzziest concept of these and has become a kind of overarching concept that encompasses all other concepts. This should come as no surprise, however, as the concept refers to the use of scientific methods and the transformation of data into knowledge and added value.
Typically, data science methods are applied in business. Data science draws from a number of disciplines, most notably statistics, mathematics, business analysis, computer science and machine learning. Due to the broad definition, data science methods cover a wide field.
This includes both many different types of analysis methods and Machine Learningbut also the data visualisation of structured and unstructured data. Within companies, the application of data science also includes business process analysis and data preparation. Data science has come a long way since 2012, when Harvard Business Review magazine called data scientists "the most attractive job of the 21st century".
Some say that data science is evolving from BI or Data mining as it is based on a similar idea; in fact, however, it applies a larger and more developed number of methods, for example machine learning. Other, more critical voices claim that Data Science is just a new name for statistics, BI or data mining.
