A Guide to SQL Data Analysis for Beginners

For most people, learning something new tends to be a bit intimidating. After all, nothing new can be acquired without any prior knowledge or experience, and the same goes for SQL.

However, mastering the SQL fundamentals may not be as difficult as you would think. As a matter of fact, once you get familiar with the basics, you’ll see that SQL data analysis is not that difficult at all.

If you’re a beginner in this field, this guide is one of the must-read resources. It will tell you all you need to know about SQL and data analysis.

What is SQL exactly?

SQL refers to Structured Query Language, which is a type of programming language that's designed to facilitate retrieving specific information from databases. In simpler terms, SQL is the language used by databases.

This is important because most businesses store their data in databases. Of course, while there are numerous kinds of databases (MySQL, PostgreSQL, Microsoft SQL Server), the majority of them ‘speak’ SQL.

Therefore, mastering SQL fundamentals translates to being able to work with any of them. After all, at most companies, one has to use SQL to retrieve the data from the company’s database. It is definitely the most common piece of technology when it comes to data and databases.

What is SQL in terms of data analysis?

Since SQL is one of the most commonly used languages (with a high degree of flexibility), users are always eager to create advanced tools and dashboards for data analytics. This technology was adapted into a wide range of proprietary tools, including MySQL, Microsoft Access, and PostgreSQL, where each tool has its own focus and niche.

Another great benefit that this technology brings is the ability to create and interact with databases quickly. Its simple language makes it easy to perform well when dealing with complex data analysis.

The internal logic of the language and the way it interacts with data is similar to tools like Excel and even some python library Pandas.

SQL is accessible, users can build complex models and analyses quickly with it, and it also offers an in-depth ability for data maneuvering. Moreover, simply having an SQL cheat sheet should be enough in most cases to get by and perform well when using the language for SQL data analysis.

The capability to give SQL simple commands in English for complex processes implies that it is highly popular for users who require complex analytics but don’t know more sophisticated computer languages.

Combine SQL with pandas

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More experienced IT professionals know the differences and advantages of using both SQL and pandas for data analysis. Yet, some of them think that they should use one or the other.

The most important thing is that you don't have to choose between the two. You should know that SQL and pandas both have a spot in a functional data analysis tech stack.

You can go for one of the two methods mentioned, which are read_sql and pandas read_sql. Of course, you should choose according to your knowledge and capability, and if you’re looking for an easier way out, then read_sql is the right choice.

However, if you want to do the real heavy lifting (which brings better results) directly in the database instance through SQL and then do the fine-tuned analysis on the local machine using pandas, go for option number two.

How should you use SQL for data analytics?

The most popular use for SQL nowadays is definitely the use as a base infrastructure to build its convenient dashboards along with reporting tools (SQL for data analytics).

Since a user will have no problems communicating complex instructions to databases and manipulating data in a matter of a few seconds, SQL makes intuitive dashboards that can display data in a wide range of ways.

On top of that, SQL is great if you want to build data warehouses since it is easily accessed, has clear organization, and it can interact effectively.

On the other hand, many individuals use SQL data analytics by integrating them directly into other frameworks. This offers additional functionalities and communication capabilities without having to build completely new structures from the ground up.

Finally, SQL analytics can be used with other languages such as Scala, Python, and Hadoop. These are, after all, three of the most popular languages that are currently used for data science along with big data management.

What is a database?

To describe it with simple words, a database is actually an organized collection of data. There is a wide array of methods used to organize a database and there are many different types of databases designed for different purposes.

For instance, if you have worked in Excel, you are most likely familiar with tables. Tables are similar to spreadsheets and they have rows and columns exactly like Excel, but they are a bit more rigid.

For example, database tables are always organized by column, and each column must have its own name. So, generally, within databases, tables are organized in schemas.

A database schema refers to the logical configuration of all or a part of a relational database. It can exist as a visual representation but it can occur as a set of formulas also known as integrity constraints that govern a database.

These formulas are presented in a data definition language and one language of that kind is SQL.

As part of a data dictionary, a database schema stipulates how the entities that make up the database relate to one another, including tables, views, stored procedures, and so on.

What is a relational database?

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A relational database is a database that stores related information across numerous tables and allows users to query information in more than one table simultaneously.

Imagine you have a business and you want to keep track of your sales information. You can set up a spreadsheet in Excel with all of the data you want to monitor (as separate columns).

These include:

● Order number

● Amount due

● Shipment tracking number

● Customer name

This would be okay if you wanted to track the information needed for the very start of your business days. But as you start getting more and more orders (and repeat orders from the same customer), you will find that their name, address, and phone number gets stored in multiple rows of the spreadsheet.

With growth, the redundant data will take up a lot of space and that can slow down your sales tracking system. However, with a relational database, you can avoid this and similar problems.

Final words

Hopefully, we’ve covered the fundamentals of SQL. Now would be a good time to choose a database you find interesting and start writing queries to pull out information.

Make sure to equip yourself with enough knowledge regarding this topic, as well as find the right tools to get the most out of SQL data analysis.