10 Essential SQL Queries for Data Analysis
A beginners guide
The acronym, Structured Query Language (SQL) can be defined as an effective tool necessary for any data analyst or data scientist. With SQLs, you are able to extract needed insights from large amounts of datasets. This is mostly achieved by efficient data query and data manipulation. The goal of this article is to explore the top 10 SQL entries needed for every data scientist to efficiently analyze data.
“You can be a beginner or a veteran in the world of data science, it is necessary for you to have a strong foundation in SQL”
Let’s Explore the Commands
SELECT Statement
This entry is the strongest of the SQL series. With the select statement, you can retrieve data from a database table. You can make specifications on which columns you want to takeout, rename certain columns, and apply basic calculations in the SELECT statement.
WHERE Clause
This one filters rows where there is a specific condition attached so you can extract only the data that fulfils certain conditions. It is safe to say this query is a vital tool to filter and segment data
GROUP BY Clause
In situations where you are required to carry out certain functions for example; SUM, AVG, COUNT with data that is grouped in specific columns, you use the GROUP BY clause as it allows you to classify rows with the same values in specific columns.
ORDER BY Clause
This clause lets you arrange the set of results in an order either ascending or descending based on one or more columns. You can always apply this to arrange data for a better understanding of patterns.
JOIN Operations
These are applied in the combination of data from multiple tables in a column that is related. There are four primary JOINs, they include; INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN. With these, you can easily solidify data obtained from different sources for analysis.
Subqueries
A word phrase that could be used to describe Subqueries are nested queries. They are known as queries that exist within other queries. You would find nested queries useful when you need to sort data based on the result of another query.
COUNT() Function
This function counts the number of rows that meet a required condition in a table. It is used to generate statistics and understand the data size.
SUM() and AVG() Functions
These functions can be applied in the calculation of the total and average estimates contained in a numeric column. When performing calculations on your data, the SUM() and AVG() are essential.
DISTINCT Keyword
With the DISTINCT keyword, you can retrieve unique values from a column. It can help you identify categories or elements that are distinct in a dataset.
LIMIT Clause
When you are dealing with large datasets or you need a data sample from a large set, the function of the LIMIT clause is to restrict the number of rows returned by a query.
Conclusion
It is essential to master these ten SQL queries if you are a data analyst passionate about reaching your full potential in the world of data science. If you are looking to start up a career in data science, our SQL Bootcamp at Reispar Analytics Academy is for you. Also, if you are seeking database analytics consulting for companies, book an appointment with a consultant. The mastery of these tools makes it easy to aggregate, analyze, filter, and retrieve data in a bid to extract insights that are valuable and drive data decisions.
Written by:
Adeoye Esther Ifeoluwa
