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Why aspiring data scientists should learn SQL

why any aspiring data scientist should learn sql

A question that comes to the mind is – isn’t data science way too advanced for simple SQL? The answer is a clear NO! SQL helps one build a foundation for one’s data science career. Let’s have a look  as to how. The field of Data science is hot and happening right now. Just imagine that you could predict the next market crash or contain the spread of Ebola! Or that you accurately predict a health crisis months, or even years before it occurred! Data scientists the world over, are working hard on projects of this kind, and they are earning hefty salaries in the process. It’s no wonder then, that ‘data scientist’ has been crowned the Sexiest Job of the 21st Century by the Harvard Business Review. Now let us take our mind  back to the thought of predicting problems and finding solutions with the aid of data science. For this to occur, mountain-like voluminous data is needed. Many countries have adopted open data initiatives, because of which public data repositories are becoming more complex and more common. In order to tap into all this information, it is essential to be able to communicate with the databases that store it. It at this juncture that  SQL comes in. It Starts with the Database If your eyes become  glazed at the notion of databases, then stay with me. Databases have been around for quite a while, they aren’t new. It’s just that the Big Data epoch has injected a sense of newness and urgency into the world of databases. Fundamentally, there exist three common types of database: hierarchical, network, and relational. A relational database is not dependent on its applications – the database structure can be suitably modified without impacting any of the connected applications. In a relational database, one can define complex relationships between tables, as also access the relations directly. In contrast, a hierarchical or network databases are often designed for specific applications. These two database types are considered to be legacy solutions. In short, SQL is the most common way to communicate with relational databases; which have become the most common data storage mechanism. What Is SQL? The abbreviation SQL, stands for  Structured Query Language, which is a powerful programming language that can add, delete, extract, or operate on information within a relational database. SQL can also be used to perform complicated analytical functions and change the structure of the database itself – adding or deleting tables, for example. SQL became an ANSI standard in 1986 and an ISO standard in 1987. Different “flavors” of SQL  work with different database engines. For instance, PostgreSQL complies as closely as possible with the SQL standard, however, other engines use their own variant, for example, Microsoft SQL Server uses Transact-SQL, or T-SQL. Akin to dialects in a spoken language, these SQL variants often use different words or structures. These different flavors of SQL can also have additional functionalities that are unique to that particular variant. However, all of them are still firmly recognizable as SQL. Four Reasons Why SQL Is Awesome Armed with this background knowledge of what SQL is and its relevance to data science, let’s dwell upon four reasons why any aspiring data scientist needs this skill in their toolbox: SQL Mastery is a Must for Most Data Science Jobs. Proficiency in SQL is a basic requirement for almost any data science job, be it that of a data analyst, programmer analyst, database administrator, business intelligence developer or that of a database developer. SQL will be needed to communicate with the database and work with the data. Most of the technical interviews for the jobs just mentioned test SQL skills in some way, quite often in the form of a whiteboard test, where you are asked to solve a problem by writing code on a whiteboard. SQL Integrates with Scripting Languages Querying a database with SQL will often give you all the insights that you need. But you may want to take it to an advanced level. Maybe you would like to summarize the data in a certain way and then create an appealing data visualization for your web application. Else, you might like to use the query result as one of the inputs for the next step of some code that you are writing. Or maybe you have a working script package and you would like to integrate it into the SQL environment. Fortunately, you can convert the result set into an XML or JSON format and it can be used  for subsequent data consumption. Specialized connection libraries; such as SQLite and MySQLdb, allow you to connect a client app to your database, depending upon the version of SQL used. Your code package can also be integrated as a stored procedure. This makes algorithm building and tuning, exploratory data analysis and model evaluation and deployment, a lot easier. SQL Is Declarative Machine learning involves self-learning algorithms – i.e., algorithms that can adjust their performance without having the process hard-coded in a set of logical rules. In other words, machine learning lets one specify one’s objective without specifying how it is done. SQL works in a similar way. SQL is non-procedural and has been designed specifically for accessing data. The fundamental difference between SQL and conventional programming languages (R, Python, Java, etc.) is that SQL statements specify WHAT data operations should be performed rather than HOW the operations are to be performed.  When one writes Python script, the Python interpreter reads the program line by line and carries out the instructions in each line. If you’ve ever had any experience of writing any code, you know how long that takes!              Whereas, SQL’s concise set of commands save time and reduce the amount of programming required to perform complex queries. Instead of directing a compiler at every step, you simply tell it what you want it to do. SQL Prepares You for NoSQL NoSQL databases have become more popular due to the velocity and volume of Big Data. NoSQL is prized for its scalability and flexibility, but because it has evolved so quickly there is currently no standard engine or interface for NoSQL, which is prized for its scalability and flexibility. If SQL is tackled first, then learning NoSQL will be a lot easier. Once a solid SQL foundation is achieved, you’ll appreciate the limitations as well as the advantages of NoSQL (i.e. NoSQL uses flexible document objects rather than SQL’s predetermined, fixed tabular schema). SQL Opens the Door to Data Science A large number of people are rushing headlong into machine learning, data science and artificial intelligence. It is imperative that you set yourself apart by mastering both the foundations of this field, as well as the jazzier concepts. Learning SQL will give you an in-depth understanding of relational databases, which are the bread and butter of data science. This will also elevate  your professional profile, as compared to those with limited database experience.

Manu Jeevan

Manu Jeevan is a self-taught data scientist and loves to explain data science concepts in simple terms. You can connect with him on LinkedIn, or email him at manu@bigdataexaminer.com.
Manu Jeevan
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