DATA SCIENCE Career

 Data science is unique in the technology space in two ways. First, compared to software development, for example, data science is intangible. You can't see a flashy front end, just the results of a model. Second, it is science, which means that, unlike engineering, it is difficult to define a perfectly defined plan in advance. Uncertainty is an integral part of data science, and this can make estimates and decisions difficult. These two factors make understanding data science more challenging (KAMPAKIS, 2020).


According to Godsey (2017), the origins of data science as a field of study lie somewhere between statistics and software development.


Data science has three main fields that include, artificial intelligence, machine learning and statistics. In this way, statistics is an essential tool in the arsenal of any data scientist, because it helps to develop and study methods to collect, analyze, interpret and present data. The countless methodologies used allow data scientists to perform such things as: designing experiments and interpreting results to improve decision making; build forecasting models; transform data into insights; make smart estimates, etc. (KAMPAKIS, 2020).


According to Skiena (2017), data science is at the intersection of computer science, statistics and underlying application domains. Computer science comes from machine learning and high-performance computing technologies to deal with scale. From the statistics comes a long tradition of exploratory data analysis, test of significance and visualization. From the domains of application in business and science, battle-worthy challenges and standards of assessment emerge to assess when they have been successfully achieved.

The Information security engineer should work in collaboration with the information security team to offer support to security tools and technologies such as firewall, proxy server, remote access, and others.

In addition to statistics and software, many people say that data science has a third major component, which is something like experience in the subject or domain. Although it is important to understand a problem before trying to solve it, a good data scientist can switch domains and start contributing a little earlier (GODSEY, 2017).

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