SQL For Data Science

SQL For Data Science

A data scientist is responsible for cleaning, processing, and processing large data that has been collected by data engineers in a company. Data scientists also often have to conduct experiments to prove and provide the most appropriate advice for the development of an organization, company, and business entity. In daily work, data scientists will often deal with questions such as “how many types of users does the company have?” And “can it create models that can predict a product that will sell if sold to a specific target market?”

In essence, work as a data scientist is how you can produce a conclusion that can be digested and accepted by all, based on a large data set that already exists. Every day, data scientists deal with data processing programs such as SQL and Python. At the very least, you must master the fields of data programming, communication, mathematics, statistics, and economics like SQL.

SQL is a Structured Query Language that is a special language used to create and process databases. Structured Query Language or commonly called SQL, is a special language used to access data that is in a relational database. SQL is a computer language that uses the ANSI (American National Standard Institute) standard used in relational database management. With SQL or it can also be called a query we can manipulate or edit the database as we wish. such as running a query to retrieve data, add data, update data and delete data. Until now, almost all database servers and other database software know and are able to interpret SQL language. therefore learning SQL language is very important for those struggling in the IT field and who are always in contact with relational databases.

For Data Scientists, one of the validity of the data is also determined by how relevant the data source is, either as a complement or as a comparison. Meanwhile, data analysis activities are carried out in Python or R languages to manipulate data and use SQL to query (including creating relationships) on data sources. Coding is done when the data source has become a file extension that is ready to be processed. There are universally four common formats that can be accepted by almost all data analysis systems, namely Comma-separated Values (CSV), Scripts (* .py, * .ipynb, * .r etc.), application table files (* .xlsx, *. qgs etc.), and web programming files (* .html, * .svg etc.).


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