'Data Science' has been one of the most heard and most common concepts with the developing technology. The increasingly digitized world has created many areas of research and practice for people. Along with these, many new professional groups have started to appear. The product produced in the new digital world reaches consumers with brand new technology. During this process, the data is kept in storage. It is extremely important for every sector that this data kept in storage is connected to a meaning. Companies make decisions about their next steps by processing and researching this data.

1.  Data Science and Education

Data Science professionals are generally postgraduate students or postgraduate graduates. It is very important to get an education in this field of science. Particularly, studying statistics, social sciences and computer science are extremely important for the expertise of this science. Also, as the theoretical education in the field of large-scale data analysis such as Big Data, which is becoming increasingly popular and advancing, begins to increase, the use of data science will become easier.

1. Data Science: R Programming Language

In order to be successful in data analysis, it is necessary to know and use the analysis programs in these areas at a good level. R programming language, which emerged in the 1990s and has millions of users today, is one of these programs. The R programming language, which was especially supported by giant companies such as Microsoft and Oracle at the time, has been the best-performing program of data analytics today. The R programming language with its analysis capacity and easy use has enabled 43 percent of scientists to use this program.

1. Python Coding

Another coding language among the data science process components is the Python language. Although Python has just started to be used in our country, this language first appeared in the 1990s and has reached an incredible number of users today. Python, a language used by many data scientists, is highly appreciated for its easy use and advantages. Operations that can be done in 10 lines with other coding languages ​​can be done in only 1-2 lines with Python.

1. Data Science: Hadoop Platform

Hadoop has become an important part of data science with the opportunities it provides. The ability to use Hadoop is now a feature that people who want to work in this field should improve themselves. In fact, research done on Linkedin shows that data science-related employers pay attention to the competence of Hadoop usage from candidates. This rate is 49 percent. In other words, half of the employers want candidates to know the Hadoop platform well.

1. SQL Database

With the developing technology, new technologies and usage tools related to data science are increasing day by day. But old tools like SQL still maintain their importance. SQL stands out as the most basic need in the data field and the program to be used. SQL has managed to maintain its place in this field with some advantages it has offered over the years. Especially because it’s been used and developed by users for many years, the SQL database remained up to date. In addition, the explicit commands provide great convenience to the user.

1. Apache Spark

The Data Science Department is closely associated with many programs. Although Apache Spart is very similar to Hadoop Program, there are important points that separate them from each other. The processing and analysis of data are very time-demanding issues. In addition, fast analysis and rapid conclusions are very important not only in the data field but also in all areas of technology. Apache Spark is highly preferred with this speed advantage. Moreover, Big Data can be both examined and analyzed with Apache Spark.

1. Intellectual Curiosity

It is very important to ask the right question for good and successful analysis. The right question is the first step of the analysis. Persons doing data analysis work follow a path while trying to find the answer to every question they ask. During this path to the end of the analysis, discoveries are added to the field of Data Science and these discoveries are gradually developed. Intellectual curiosity is therefore very important for the development of data and to find alternative answers.