Learn how to – How Important are SQL & Databases in Data Science

Learn how to – How Important are SQL & Databases in Data Science.

Nowadays the technology world is in the data-driven direction. With the development of Artificial intelligence and Machine Learning that hugely depend on data gathering and processing, data science is getting more popular. So does the demand for data specialists.

The majority of vacancies available on the market contain the similar skill set criteria to the potential applicants. Despite the fact that there are different jobs related to data analysis like data engineers, data scientists, business intelligence specialists, DBA, etc and their primary responsibilities may vary, all of them have one demand in common. And this common must-have is proficiency in SQL, or Structured Query Language. 

Among the other frequently met requirements there are Database server structure understanding, and comprehension of its architecture, as well as possessing some critical soft skills like problem-solving and critical thinking. 

However, if some of these skills can be developed through experience, SQL comes first on the list when you search for your first internship in data science. There’s a pretty straightforward reason behind it: in order to perform your job duties you will need to access, manipulate, search and control datasets, and knowing SQL makes this work much easier.

Let’s have a closer look on why knowing SQL and working with databases are so important for a data scientist:

It’s everywhere 

SQL is an open-sourced language thus it’s used for querying data in the majority of relational database systems like MySQL, Microsoft SQL, MariaDB, PostgreSQL, and others. So if you are working with databases, you will definitely use SQL in your day-to-day work. Taking into account that SQL was created 40 years ago, there might be a misleading impression that it’s becoming obsolete. It’s quite the contrary, as apart from supporting nowadays systems, with the trend of moving to the cloud, new database solutions like Google Cloud SQL and Microsoft Azure are also using SQL as their basis. The great thing about this wide usage is that it’s relatively easy to start training in data science with a variety of courses,bootcamps, tutorials and books for beginners like “SQL for Dummies” and more advanced ones available online.

It’s compatible with other programming languages and applications 

Sometimes, using SQL alone may not be efficient to cover the basic business needs like data visualization and if your work is related to data interpretation, you will need additional tools. The good news is that SQL is compatible with data analysis and business intelligence tools like Tableau, or Power BI to visualize data, create custom reports and dashboards to track business metrics. When it comes to application development, all of them require a database to function properly. Here, the integration of SQL with other programming languages like Python, C++, or R comes in handy to do database performance analysis, develop effective cross-platform software, support data storage, and retrieve data to serve clients’ requests to the application. 

It simplifies understanding of the data you are working with 

When you work with relational databases, one of the main skills to learn is understanding how data is organized. Here, the knowledge of SQL comes in handy. With its help, you will be able to navigate through the dataset content, perform modifications, and understand the peculiarities of the database you are working with. It’s also helpful in troubleshooting issues that may occur from time to time because you will be able to identify empty cells, or corrupted content, as well as spotting the patterns of the structure. This will result in the facilitation of database management and deeper knowledge of your datasets increasing the effectiveness of your work. In addition, the error investigation is simplified by clear error codes, so if during the troubleshooting you misuse operator, or there’s a syntax error, the codes are pretty self-explanatory and helpful to highlight where the root cause of the issue lies.

It enables large data massives processing

Taking into account that data science is relying on operating large amounts of data, it may not always be possible to reflect in visually convenient formats like spreadsheets. Sometimes, datasets may have a complex structure creating additional difficulties when it comes to data interpretation. And with the dataset volumes increasing, SQL becomes the only way to maintain and work with such big data massives. By setting up the parameters of a query, it’s possible to process, filter, and select the required information to later act on it. This way, no matter how huge the database is, you will be able to get powerful insights from it. 

These are the most common reasons for using SQL when working with ordinary tasks related to data management. With that being said, we can see that SQL that was created in 1970 is still in use, and moreover, it remains one of the most in-demand skills for everyone planning a career in data science and it’s not going away anytime soon.