SQL for Data science

Data Science, AI and ML
#1

To become a good Data Scientist, you must be familiar with SQL. Many beginners who want to be in Data Science but are afraid of coding proceed with SQL queries.

  1. SQL is required for a Data Scientist to work with massive datasets. As a result, a data scientist needs to be fluent in SQL commands to query these databases.

  2. Big Data Platforms like Hadoop and Spark have an extension for querying and manipulating data with SQL commands.

  3. SQL is the industry standard for experimenting with data by establishing a testing environment.

  4. SQL is necessary to run analytics on data stored in database systems like Oracle, Microsoft SQL, and MySQL.

  5. SQL is also useful for data management and planning. As a result, while working with various Big Data technologies, we employ SQL.

#2

SQL is not a language you could or would want to use for data science unless your “science” is limited to filtering based on some columns, grouping by some category etc. SQL is good for writing moderately complicated queries or joins but it can’t handle any serious data wrangling/cleaning/modeling tasks which frequently comprise any data science project. You should look into using Python or R for doing any sophisticated data science.

As far as learning basic SQL is concerned I would recommend Board Infinity. They have a pretty good section on SQL with some sample databases and a playground to try your SQL code in.