Data Science

Perhaps one of the most popular topics of recent years is Data Science. In fact, according to the article I shared at the end of the article, it is the sexiest profession of this century. By definition, data science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract information and insights from structured and unstructured data. Data science is associated with data mining and big data. The structured data mentioned in the definition can be thought of as data in a table structure. Unstructured means keeping data in different formats and content such as pdf, text, audio, image files. For example, sentiment analysis from customer comments on e-commerce sites or analyzing the pictures of the products and analyzing which color/content products get more likes/demands.

People who deal with this science are also called data scientists. To give details, data scientists make hypotheses for big data management fed from different data sources and conduct research to test the accuracy or falsehood of these hypotheses. In other words, it takes the structured or unstructured data mentioned in data science, establishes relationships between them and makes sense of this data, and ensures that these data are used in a way that will benefit the organization.

If you say who can deal with data science, we see professionals and academics who have a master’s degree in statistics and mathematics in USA, where I think this work is done at the highest level. Of course, there are also a substantial number of professionals from the software industry in this cluster. I think anyone who is curious about this subject and is open to self-development can make a career in data science.

To list the features that data scientists should have;

• Curiosity: Everything starts with being curious about the data you have and the relationship between the data.

• Mathematics/Statistics Knowledge: Academic knowledge is important for the correct analysis of the data you have.

• Software Knowledge: Software knowledge is an important competence for you to make this data meaningful by using the infrastructures provided by technology.

• Presentation Skills: Although it may not seem very relevant, you should produce a report at the end of your data science project and you should be able to present this report to relevant stakeholders. Although you have a very good data analysis and report, if you cannot convey it well, you will obviously not get much results.

Although it is almost impossible to have these four features ready in a person at the same time, all of them can be developed except curiosity.

Further Reading

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