5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists
For budding data scientists and data analysts, there are mountains of information about why you should learn R over Python and the other way around. Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL. But why is SQL, or Structured Query Language, so important to learn? What can it do, that sets it apart from other programming languages? Well, in this piece we will break down five solid reasons why you should not only learn SQL but become a SQL Wizard.
It’s a foundational skill for working with relational databases
Just about every data scientist or analyst will have to work with relational databases in their careers. But it’s not just that, as you move forward in your data journey, your understanding of these databases will only become more important, and the language spoken by these databases is SQL. That’s because it’s a simple-to-understand and easy-to-use programing language that allows for the querying, analyzing, and manipulating of data stored in relational databases — in short, it’s a one-stop shop for your data where it tends to live when stored. So by learning to use SQL, you’ll write efficient and effective queries, as well as understand how the data is structured and stored.
But you’re asking yourself, what about day-to-day operations? Well, believe it or not, SQL is used very often because it’s the direct way of retrieving your data. Without it, whatever you put in Excel, Python, or R, wouldn’t exist because there would be a way to manage the data. But its status as the go-between for programming and data professionals isn’t its only power. Within SQL you can also filter data, aggregate it and create valuations, manipulate data, update it, and even do data modeling. This means that as a foundational skill, SQL provides many transferable skills that will make you a better Python or R programmer.
You can perform data analysis within SQL
Though mentioned in the first example, let’s expand on this a bit more. SQL allows for some pretty hefty and easy ad-hoc data analysis for the data professional on the go. It provides a wide range of functions and operators for performing data analysis tasks, such as filtering, grouping, and aggregating data. Let’s briefly dive into each bit.
Let’s start with the first clause often learned by new SQL users, the WHERE clause. It allows for the setting of conditions so you see the data you care about for a particular project. So, for example, if you’re interested in learning more about customers based on their income levels, you can filter based on that condition. Next, you have the GROUP BY clause. This allows data scientists to group data by one or more columns, and then perform calculations on each group.
Then you have the JOIN clause. This one is tricky for beginners, but it’s a critical skill to learn as a budding data professional. That’s because it allows data scientists to combine data from multiple tables into a single result set. JOIN clauses can really make sense of the data when you’re making comparisons, painting a clear picture of what you see and potentially answering your question with greater depth. Finally, SQL’s window function. Though a bit more advanced, it’s another important tool in SQL that provides a way to perform calculations on a subset of data within a larger result set. By learning SQL, data scientists can write complex queries that help them to gain insights into their data.
As you can see, within SQL you can do quite a bit of data analysis before even extracting data to your preferred language.
It allows you to work efficiently
SQL can help data scientists and teams to work more efficiently because it allows for the retrieval and manipulation of data in a quick manner that allows for reusable queries. There are several commands that users use to manipulate data fast, INSERT, DELETE, & UPDATE all can save data professionals a considerable amount of time and effort when using large data sets.
Another boon for efficient work that SQL provides is its simple and consistent syntax that allows for collaboration across multiple databases. This provides an inviting setting for members of a team to work together, bridging the information gap between software developers, administrators, and other team members. This allows for more effective communications with less chance of misunderstanding or miscommunication that could stall or slow down a project.
Integration with Other Tools
Not only is SQL great at speaking with its users, but it also can speak with other programs as well. This allows for it to be integrated with many different tools and technologies to improve data management and analysis workflows. One set of tools that are becoming more important in our data-driven world is BI tools. Think of Tableau, Power BI, and QlikView. Each of these creates visualizations and reports based on data stored in a database. They often provide drag-and-drop interfaces that allow non-technical users to create reports and dashboards using SQL queries as the underlying data source.
Data integration tools allow for the combining of data from multiple sources. The most popular of these tools are Talend, Informatica, and Apache NiFi. These are used to extract, transform, and load (ETL) data between different systems. These tools often provide pre-built connectors for many different databases and data sources and allow data scientists to write SQL queries to transform data as it moves between systems.
Thanks to libraries such as SQLAlchemy and RMySQL, SQL can be integrated with programming languages such as R and Python. This is important because it allows teams to use SQL queries as part of their data analysis pipelines and to write scripts that connect to a database and perform complex data analysis tasks. Imagine combining the data power of SQL with your preferred scripting program. It opens a lot of possibilities when it comes to statistical modeling, visualization, and overall analysis.
Finally, cloud services. Many cloud providers, such as Amazon Web Services and Microsoft Azure, offer SQL-based database services that can be used to store and analyze data in the cloud. These services often provide integration with other cloud services, such as data storage and processing tools, to create end-to-end data workflows.
SQL is the standard language when it comes to relational database systems.
Due to the reasons above and more, this has positioned SQL as the standard when it comes to relational databases. Though the language is going on fifty years old, its direct and simple syntax is still quite powerful and this is seen with how so many new and established tools and programming languages have created libraries specifically to bridge the gap between them and SQL. It’s also a flexible language that can be used for a variety of tasks, especially for ad hoc work. Then there is standardization, SQL is used by many systems, vendors, and developers and its standardization allows for queries that can work across multiple platforms. Finally, it’s open source.
Conclusion
Because of all of this, SQL has a large and active open-source community that contributes to the development of SAL-based tools and technologies. This is keeping the programming language current as new tools and methods in data science are developed. With all of that said, mastering the fundamentals of SQL is still a critical part of the data science journey. Though R, Python, and other tools tend to get the most attention in conversations, SQL has proven itself to still be the backbone of data science.
So what do you think? Are you ready to learn SQL, or upgrade those existing skills? Then you’ll want to sign up for the ODSC East Bootcamp. There you’ll have the opportunity to get hands-on training with SQL and learn how you can truly get the most out of one of data science’s most reliable tools. Join us at East, and earn that SQL wizard hat.
Between now and May 9th-11th, getting an ODSC East 2023 bootcamp ticket will grant you access to a number of live training sessions over the next few months that will cover the above five pillars of data science, all leading up to a week of training in May that will let you use your newfound skills for more advanced topics in machine learning, deep learning, NLP, and more. Here are a few sessions that you can check out soon:
- March 2, 2023: ODSC East Bootcamp Warmup: Data Primer Course — now available on-demand!
- March 14, 2023: ODSC East Bootcamp Warmup: SQL Primer Course
- April 6, 2023: ODSC East Bootcamp Warmup: Programming Primer Course with Python
- April 26, 2023: ODSC East Bootcamp Warmup: AI Primer Course
And during ODSC East this May 9th-11th, you can check out these bootcamp-exclusive sessions:
- An Introduction to Data Wrangling with SQL
- Programming with Data: Python and Pandas
- Introduction to Machine Learning
- Introduction to Math for Data Science
- Introduction to Data Visualization
- Introduction to Deep Learning with TensorFlow & Keras
- Introduction to Deep Learning with PyTorch
- Introduction to ML using scikit-learn
- Introduction to NLP
- Hugging Face Transformers
- Idiomatic Pandas
- Introduction to Large-scale Analytics with PySpark
- A Practical Tutorial on Building Machine Learning Demos with Gradio
