The evolving technology that is constantly in our language is on the agenda these days with the convenience it provides when it comes to collecting data and storing it in stacks. So why are we trying to store data?
succinctly “Big Data” I'm sure it will be familiar if we say it. Big data is called data that is of a size that cannot be managed with traditional database tools. A new field called “data science” was born, with the aim of processing all this information that cannot be analyzed by human hands, from billions of sources.
Nowadays, when the Internet is starting to be used even on the smallest devices, we actually owe the rapid and error-free progress of things to big data and data science. Data science serves to transform both structured and unstructured data into useful/valuable information to solve complex problems. In this process, he uses scientific problem-solving techniques, mathematics, statistics, and the disciplines of software development together.
There are many definitions of data science, according to these definitions the three main objectives of data science are;
Sami aydoğan from the efilli team briefly explains the data and its use as follows:
“Data science, in my opinion, helps us to get the output we want by playing with the data. Data science is important for every company, big or small. Because it is inevitable to use data science to evaluate work done, performance, and future goals. As a data engineer, I liken the data to the dough needed for the baker to make bread. Changing the content of the dough and giving shape to the dough allows you to change the bread that will turn out. Such is the case with the data, if you properly evaluate the data, you will get useful outputs for the organization you work for. “
Data science does not stop counting the uses... Data science not only in e-commerce, but also in the marketing departments of companies, banks and factories makes things easier for us. For example;
and many more things are made possible thanks to data science.
Doing data science is not the same as developing software.
Efilli board member professor Dr. Deniz kılınç explains the differences between data science and software development as follows:
“Although they have a lot in common, they both have different workflows and specializations of their own. I can safely say that the most basic point of their intersection is “writing programs”. When doing a job in classic software development processes, you can “unfortunately” do business on the market without knowing much about the infrastructure of the work on a ready-made roof. But there is no escape in data science and artificial intelligence projects, if you work as a “data scientist” in the industry, you definitely need to know the theoretical detail of the infrastructure/algorithm you are using. “>
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