Over the last few decades, the strides we’ve seen in the tech landscape means that businesses are generating more data than ever. These brands are relying on data scientists to make sense of it all. On their part, the scientists rely on a few technologies and programming languages to deliver on the job, but is SQL part of these languages?
Data scientists use SQL (Structured Query Language) to process and analyze data stored in a database. Although SQL is not used exclusively in data science, data scientists with an understanding of SQL tables and queries can more efficiently store, manipulate, data—especially in relational databases.
The rest of the article will take a look at data science, SQL, and why aspiring data scientists should embrace the language. Watch out for my top recommendations on beginner-friendly SQL books later in the article, if you are interested in learning SQL as a beginner.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is Data Science? Why Is It Popular?
Data science is a broad field where one is required to have knowledge in all or some of the following fields: machine learning, mathematics, statistical research, computer science, and data processing. All of these fields feature deep handling of data from the collection to the processing and conclusion stage.
The popularity of data science today can be attributed to the growth of the digital sphere and the increasing demand for better business strategies. Data now plays an important role in the relationship between businesses and their customers. Brands are, therefore, always looking for ways to use the data to guide their decisions.
While the growth of online businesses popularized data science, more traditional brands in manufacturing, finance, transport, and healthcare rely on it a lot more today. Therefore, it is not a surprise that data science as a field and data scientists are in hot demand.
What Is SQL?
SQL (Structured Query Language) is a database language designed to create, maintain, and retrieve relational databases. It is one of the oldest languages as it’s been around since the 1970s.
Since then, it has grown to become one of the most important tools used by the modern-day data scientist. It is useful in every stage of handling data, from creating new datasets to manipulating or modifying existing sets.
Which Database Is Best for Data Science?
The best database for data science will come down to the nature of the data. For data that comes with clear and logical connections, a Relational database(collection of data sets organized in rows and columns from which data can be accessed in multiple ways) is the best. Some examples of relational databases include PostgreSQL, BigQuery, Amazon Redshift, and MySQL.
For data with limited logical connection, a Non-Relational database(Database that does not use rows and columns as its storage structure) is the best. Such a database is the best when you have millions of scattered data points to navigate and generate analytics. MongoDB and Apache Hadoop, Amazon DocumentDB, and Apache Cassandra are some of the popular non-relational databases.
So, as an upcoming data scientist, the best database to focus on will determine what type of data you’ll be analyzing in the sectors you are looking to focus on.
Top Reasons to Learn SQL as a Prospective Data Scientist
Here are some reasons why learning SQL should be high on your list as you prepare to take the plunge into a data science career.
It Is Easy to Learn and Work With
Many programming languages require a high-level understanding of the main concepts and memorization of the steps needed to carry out any task. SQL, on the other hand, is simpler because it uses declarative statements. The simple language structure featuring everyday words is easier to grasp than the strings of numbers and letters in other languages.
Starting your data science journey with SQL gives you the necessary foundation you need for effective data querying and manipulation. Once you are able to learn the basic keywords and know how to filter and join tables, you can begin to complete more complex SQL tasks.
It Is Widely Trusted
From Netflix to Airbnb, Twitter to Spotify, many of the world’s biggest tech brands today use SQL. Even in companies where there are in-house database systems, it is not uncommon to find database teams using SQL based databases for data queries and analysis. This is the case in companies like Google, Facebook, and Amazon.
Away from the big tech names, small companies typically choose SQL as their database technology. A quick look through job portals will highlight just how many small companies are looking for analysts with SQL skills compared to Python or R skills.
One of the reasons SQL is so widely used is that it can be applied in a wide range of contexts unlike task-specific databases available in the market such as Memcached, Redis. The technology can be used by sales teams to track sales and finance desks to analyze the company’s finances. It can also be used by marketing teams to analyze customer behavior and much more.
There’s a High Demand for SQL Skills
SQL is currently in the top 10 for the most-commonly searched programming technologies on the internet. In addition, jobs in the computer and information research niche—one that includes SQL positions—are expected to grow 16% in the next eight years, according to the U.S. Bureau of Labor Statistics.
According to the job site, Glassdoor, there are currently more than 20,000 job postings for SQL Developers. These facts show that SQL developers are in high demand. This spills over into the salary discussion, where the average base pay for an SQL developer is $81,622. More senior positions command higher salaries, showing that the skill is not just in demand but also lucrative.
The Future Is Secure for SQL
SQL has been around for five decades, and there is no sign of it going the way of some of the programming languages that have come and gone. The language is deeply woven into the fabric of some of the technologies we love today, and there are no indications that this will change anytime soon.
Around 64% of developers surveyed in the highly-rated Stack Overflow Developer Survey stated that SQL is the language they loved the most. With that many developers thinking highly about the technology, the chances of the technology becoming obsolete are very slim. Learning SQL, therefore, means learning a skill you can trust throughout your career.
The SQL Developer Ecosystem Is Robust
With so many developers professing love for the technology, it is not surprising that there is a massive repository of resources on the web today for learning SQL. Whatever the field you’d like to go into with your SQL knowledge, you’ll find lots of quality tutorials, videos, and courses that can help you. You also don’t have to look too long to find a coding Bootcamp that has a robust SQL curriculum.
What Are the Best Beginner Friendly SQL Books for Aspiring Data Scientists?
Listed below are my top recommendations on the books that you should read, as a beginner, for learning SQL in your journey to be a Data Scientist –
- Practical SQL: A Beginner’s Guide to Storytelling with Data: This book is a plain-English guide to SQL programming for anyone interested in the field of data analysis. The fast-paced approach employed makes it one of the most recommended books in the niche for beginners.
- Data Analysis Using SQL and Excel: It shows you how to use SQL and Excel to perform sophisticated business analysis. You’ll learn the fundamental techniques needed for data mining in relational databases.
- SQL for Data Analytics: The book excels at showing data analysts how to understand and find patterns in datasets. It is the perfect guide for going from beginner SQL knowledge to identifying and exploring data trends.
- Business Analytics: The Science of Data-Driven Decision Making: This is a 17-chapter book that covers prescriptive, predictive and descriptive analytics. Lots of examples are baked into each chapter to better convey principles.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
Data scientists today rely heavily on SQL because it is the foundation of data science. The spread of relational databases also means that the demand for the technology remains very high. Aspiring data scientists who begin their careers with SQL can understand how to manage huge data sets quickly and find it easier to move on to other languages in the future.
Suppose the technology’s simplicity isn’t enough reason to convince you to start with SQL as a prospective data scientist. In that case, you should consider the value of SQL in today’s labor market. SQL experts are always in demand.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
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