With massive data currently available, businesses and industries are collecting and churning out billions of data every day. The big data phenomenon requires a proper skill set to be able to make meaning out of It. From the medical field, education, business, sports, to logistics, relevant data skill is critical.
These enterprises must be able to not only collect and store data but also analyze it to make strategic decisions. These data-driven decisions eventually can increase their profitability and solve real-life problems. Imagine being able to use big data to develop a model that will ease traffic and make transport in major cities easy and convenient. This and many more can be done and one of the skills needed by a data scientist is SQL. So what is SQL?
What is SQL?
SQL (Structured Query Language) is a standard database language that is used to create, maintain and retrieve relational databases. Started in the 1970s, SQL has become a very important tool in a data scientist’s toolbox since it is critical in accessing, updating, inserting, manipulating and modifying data. It helps in communicating with relational databases to be able to understand the dataset and use it appropriately.
Here are five reasons why an aspiring data scientist needs to learn SQL for them to succeed in their data science career.
1. Easy to Learn and Use
Unlike other programming languages that require high-level conceptual understanding and memorization of the steps needed to perform a task, SQL is applauded for its simplicity by the use of declarative statements. It uses a simple language structure with English words that are easy to understand compared to memorizing strings of numbers and letters in other languages.
If you are new to programming and data science, SQL is the best language to start with. A short syntax allows you to query data and get insights from it. As an aspiring data scientist, you need to learn SQL since it is easy to master. SQL is at the very foundation of data science.
For you to progress steadily and with good mastery of the field, you need to start your data science career journey with a simple yet powerful language like SQL. It is very easy to learn the basics of SQL and use them to query and manipulate your data.
In addition to that, there are SQL-based Business Intelligence (BI) tools that are very handy and can easily be used by a data scientist. SQL will also provide you with the basic knowledge that can help you delve into other programming languages. In addition to that, it will also prepare you to understand NoSQL databases.
2. Understanding your Dataset
As a data scientist, the first thing you want to know is an in-depth understanding of the dataset. Learning SQL will give you a solid understanding of relational databases and hence enable you to master the foundations of data science.
SQL will help you to sufficiently investigate your dataset, visualize it, identify the structure and get to know how your dataset actually looks like. It will enable you to find out if there are any missing values, identify outliers, NULLS, and the format of your dataset.
Through slicing, filtering, aggregations, and sorting, SQL will allow you to play around with your dataset. You will also be thoroughly familiar with the data, understand its distribution and how the dataset is organized. As a scalpel is on the hand of a surgeon, so is SQL on the hand of a data scientist. It is irrefutably useful in ‘incising’ through the dataset for detailed understanding.
3. Integrates with Scripting Languages
In as much as SQL is powerful in data access, querying and manipulation, it is limited in some aspects like visualization. As a data scientist, you will need to meticulously present your data in a way that is easily understood by your team or organization. SQL integrates well with other scripting languages like R and Python. You can easily integrate SQL and Python to be able to do your work comfortably by incorporating your code package as a stored procedure.
Also, specialized connection libraries for SQL like SQLite and MySQLdb can be very useful in connecting a client app to your database engine thereby allowing you to work with your dataset.
4. Manage huge volumes of data
Data science in most cases involves dealing with huge volumes of data stored in relational databases. Working with such volumes of data needs high-level solutions to manage it other than the usual spreadsheets. As the volumes of datasets increase, it becomes untenable to use spreadsheets. The best solution for dealing with huge datasets is SQL. SQL has the capacity to manage such datasets.
With SQL, you do not have to worry when dealing with pools of data in relational databases. It can communicate, query and provide useful insights from the data.
5. A Gateway to Data Science Jobs
For most data science jobs, proficiency in SQL ranks higher than the other programming languages. Data science involves dealing with large datasets in databases and it will require expertise in SQL to be able to solve the problems in your project. Programming in SQL is highly marketable as far as data science is concerned.
The ability to store, update, access control and manipulate datasets is a great skill for every data scientist. SQL will, therefore, provide you with this ability that will make you sought-after data scientists.
Furthermore, SQL integrates with many database management systems DBMS) like MySQL, Microsoft SQL Server, Oracle Database, dBase among others. These DBMS allow one to dynamically build SQL statements for projects. This integration also makes it possible to switch between the systems.
SQL is used in most industries such as computer software, health, manufacturing, transport, banking, etc. In short, SQL is there to stay and mastering it will be an advantage for an aspiring data scientist.
In summary, as a free open-source programming language, SQL is at the very foundation of data science. Communication with relational databases will be easier when you learn SQL. I would recommend that any aspiring data scientist should learn SQL. This is because it is easy to learn, helps in a deep understanding of datasets, integrates easily with scripting languages, manages huge datasets, and is indeed a gateway to lucrative data science jobs.
So, before you begin learning other programming languages for data science, why don’t you begin with SQL and have a cool entry into data science.
This article was originally published on my Medium page in June 2019.