Top 10 Advanced SQL Functions For Data Analysis - Issue 151
A roundup of helpful SQL functions to leverage for statistics, data generation, processing, and analytics.
Welcome to the Data Analysis Journal, a weekly newsletter about data science and analytics.
It’s easy to get started with SQL and grasp its basics, but it takes a lot of time and practice to advance to a proficient level.
Naturally, many analysts leverage Python or R and supplement with Excel and different BI tools to save time and move faster instead of spending hours researching the right function, figuring out its applied use case, and polishing syntax.
Today I’ll cover my 10 concepts and complex functions of advanced querying with a focus on statistics and analysis, including some use cases and examples.
Between us, I believe, in a few years Python may become another Cobol (like R already did, in a way), and most statistics, ML, and analysis will be supported by SQL (like it already is!).
Regardless of what MongoDB (NoSQL databases offer many benefits over relational databases) or ThoughtSpot (The Secret to Self-Service Success) tells you, SQL is a must-have skill for everyone who has “data” or “analysis” in their title. It never hurts to invest in learning it, and it pays off regardless of the company, your career goals, or projects.
Below are some interesting advanced or underrated SQL functions that aim to speed up data manipulation, data generation, and analysis.
Run your statistics in SQL
You’re probably already aware of basic aggregations and math like AVG, MEDIAN, and MIN/MAX. You might not know that you also can use SQL to run most of the descriptive statistics and analytical operations on your dataset. Almost every SQL variation now supports these as a built-in (super helpful if you use SQL to support experimentation).