What should data science mean, please?
I often get this question from my clients (mostly managers of large companies). And, surprisingly, many IT professionals who have dealt with business intelligence, for example, can not begin with that term. Data science is a relatively new term and comes from the Anglo-Saxon language area as well as intelligence and analysis of business. of big data.
In my opinion, data science is a comparatively appropriate name (much better than the misleading big data term). Although a look at Wikipedia on the subject says that information science has existed as a term for almost half a century, but in reality is in use, in reality only half a decade has passed, at best. data as applied science Science in data Science clearly points to science, although in my opinion the term is scientifically used in German somewhat more strictly than in English.
Data science actually has its origins in science, and it is, for example, that astronomy, biology, medicine, and the most varied social sciences have become indispensable for a long time, but it has also come into the world. business world.
The methods of data science come from the science of computing or mathematics and are developed within the scope of university research projects. The methods can be used with a little prior knowledge, so to speak, by anyone. Above all, data science is an applied science, in which every user can dive as deeply as he wants.

Data Science and Interdisciplinarity

• An important discipline in data science is mathematics, especially stochastic (data theory).
  probability and statistics).The basic concepts of data analysis for the description of facts are made with descriptive statistics methods.
• By generating new knowledge directly from data sets (data extraction or exploratory data analysis), exploratory statistics are activated.
• Inductive statistics go one step further and allow estimation methods and predictions of future events (predictive analysis).
• However, in addition to stochastic methods, other areas of mathematics also play a role, such as linear optimization or artificial intelligence systems.
• However, mathematics is far from all that plays a role in data science because at least as important is knowledge about data processing (formerly known as EDP).
• To analyze the data, they must first be accessible, if necessary, they must first be collected.
• At the very least, the basic knowledge of relational databases and the structured query language (SQL) are part of data science.
• However, especially in the current big data context, different types of databases (so-called NoSQL databases) play an increasingly important role, since these databases are suitable for storing large amounts of data or not The best insights often provide data analysis on data from a variety of data sources, which are combined using Extract Transform Load Lines (ETL).
• Actual analysis can be done with a variety of tools or dozens of programming languages.
• Knowing ETL tools and data analysis speeds up your daily work, but quickly reaches the limits of the programming language.Software engineering plays no role in data science.
• Therefore, a data scientist usually does not have to worry about software architecture or GUI projects, nor software security or ergonomics is important to him.
• Data science mostly uses scripting languages (eg, R, Perl, or Python) In addition to mathematics and IT knowledge, there is still a third area that plays a really important role in data science: the science of science. the real substance. Needless to say, z.

For example, data analysis for medical purposes can only be effected effectively by someone who has adequate medical experience. Similarly, knowledge of current business and business management is extremely important when it comes to analyzing data for business analysis purposes.

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