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The Mystery Behind Data science Technology

The data science is a profession or skill that companies are increasingly demanding. Its primary objective is to generate useful information for companies. Data science is an exciting field to work, combining advanced statistical and quantitative skills with real-world programming capabilities. There are many programming languages in which an aspiring data scientist may consider specializing. The data science is the last link when converting data into information. It uses large quantities and several databases, some derived from data mining processes. First, it is good to define what a data scientist is and the characteristics that, ideally, it should have.

To do this, it combines mathematics, programming, statistics, data visualization, and other disciplines. With this information, brands gain an advantage when competing. With digitalization still in process, it is, in fact, more fashionable than current. Big data, data mining and data science are often confused, although they are different and complementary techniques. Excellent range of open source and high-quality packages. R has a package for almost every quantitative application and imaginable statistics. This includes neural networks, nonlinear regression, phylogeny, cartography, maps, and many, many others. 

Data science has been present in almost any context that can be thought of: in the mass media, in our daily experience when we use Netflix or take the subway and in the talk with colleagues or even family and friends explore detailed information from data science training institute in Bangalore providers. The big data is the collection process large volumes of data, its storage and real-time analysis in search of patterns. They are usually structured data of which we know the format. We can compare it to an open oil well. 

Data mining includes a series of technical – oriented analysis. It helps us to understand the content of a database, filter it, debug it and eliminate what it doesn't provide. It would be the equivalent of natural gas extraction separating it from crude oil. The data allow voters to better understand and, from that, make a micro-segmentation of citizenship. This is basically determining the behavior of those voters based on available data and making personalized communication by rethinking previous strategies. Will not elaborate on this part as there are many websites that deal with this issue and it is not the objective of this post. We can define data scientists as professionals who, using large volumes of information and of different types, solve business problems and get answers from data. 

The data scientist should obtain data from the source that was intended to solve that question. Structured data (e.g., databases such as SQL), unstructured (e.g., images, audios) and semi-structured (e.g., texts with a certain structure) come into play. In companies that are starting usually only structured data is used and accessed by a typical query language such as SQL. The final result obtained can be an analysis, productivity of some statistical model results for the business, is the knowledge of mathematics and statistics.

It is not necessary to be a doctorate in mathematics or statistics to be a good data scientist, however, it is to have a certain command of basic statistical concepts and know how to interpret the algorithms that one uses. 
Finally, and something that is not usually given the importance it has is business knowledge. As important as knowing what algorithm to use or how to program it knows what business questions you want to solve, what brings value and what doesn't, or to what extent the problem we want to address can be viable.

different types of problems that can be solved with machine learning techniques these three types of skills are what a good data scientist should have. The first thing a data scientist should know is how to program, as well as having some SQL handling. 



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