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Why SQL is still important for data analysis
Some thoughts about how useful SQL is in data analysis
Data science is a wonderful job because it mixes several different skills. However, while data scientists usually follow (and, possibly, create) innovation, there are some “good old” skills that are still useful for this kind of job. One of such skills is SQL.
Are you really working with Big Data?
The buzzword “Big Data” has been around for years but let’s face it: data scientists rarely work with Big Data. They usually work with tabular data taken from relational databases like MySQL, PostgreSQL, Teradata, Oracle or MS Access (yes, it happens). All these technologies share the same common language, which is SQL. So, if you want to explore such databases and create your training dataset, you must know SQL very well.
Relational databases are the most common type of corporate database and dozens of legacy systems rely on such technologies. No company will ever switch to Big Data in one day, so SQL is still a very important tool for a data scientist who wants to work hard where data really is.
Yes? Ok, no problem
If you work with non-relational databases like MongoDB or CosmosDB, you can still analyze the result of…