Modern industries and businesses collect and churn out massive amounts of data each and every day. No matter the sector, it is vital for companies to have proper systems and skills in place to make sense of this data and leverage it to improve their operations. Right from the field of education and medicine to the domains of business and sports, the use of big data can be found in almost every domain. Organizations should have the capability to not just collect and store data effectively, but they must also know the ways to analyze it properly, and subsequently take informed and strategic decisions that can augment their profitability, as well as resolve real life issues.  SQL is among the key skills needed by a data scientist to become well-versed in understanding data insights and make adequate use of it. 

What is meant by SQL?

SQL or Structured Query Language essentially is a programming language that is used for the purpose of creating, maintaining and retrieving relational databases. Having roots in the 1970s, this database language became increasingly popular over the decades, with the growing use of data. It is considered to be a vital skill for any data scientist as it aids to access, update, insert, manipulate and modify data. 

Relational data is known to be formed by the collection of two-dimensional tables like excel spreadsheets and data sets. Such tables are subsequently created featuring any possible number of rows, as well as fixed number of columns.  To further explain this concept, one can take an example of car manufacturing companies. Suppose each of such companies may have a database featuring multiple tables, one for every car model fabricated.  Diverse metrics about each of the car model sales in varying nations shall be stored in each of the tables. 

Along with R and Python, SQL is known to be among the most requested skills when it comes to the field of data science. Modern day organizations are not giving an increasing importance to data, so as to solve business problems, understand the needs of the customers, make predictions as per the market trends, and so on. SQL additionally can speed up workflow executions to a great extent when performing operations with data. 

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Reasons why aspiring data scientists should master SQL

1. Simple to use and learn:  There are several programming languages that involve a high level of memorization of steps required to perform a task, as well as superior conceptual understanding. But SQL is not one of them. This database language is in fact characterized by its simplicity and the usage of declarative statements.  SQL is known to make use of easy language structure having English words that can be understood without much of a problem, in comparison to other languages where one has to memorize a string of letters and numbers.  SQL would be the ideal database language to get started with, for anyone new to data science and programming.  One would just need a short syntax to query data, and subsequently acquire valuable insights through it. 

In many ways, SQL can be considered to be the very foundation of data science, and hence is vital for any newbie data scientist to learn. If they desire to progress in this field, having a good understanding of a simple, yet powerful language like SQL is vital.  It won’t be an issue for anyone having limited data science knowledge to learn the SQL basis, and subsequently utilize them for querying and manipulating data. Knowing SQL would also provide people with the basic knowledge needed to venture into other, more complex programming languages, as well as allow them to comprehend NoSQL databases as well. Moreover, the various Business Intelligence (BI) tools available today that are based on SQL can come in handy for diverse purposes.

1. Understanding the datasets:  For any data scientist, acquiring an in-depth understanding of the datasets they are working with is extremely important.  Learning SQL shall provide them with a solid foundational knowledge about relational databases, which can act as a stepping stone towards mastering diverse data science languages and techniques. 

With SQL people shall be able to effectively investigate their data sets, as well as visualize and identify the structure to gain a proper knowledge of how exactly their dataset looks like. It would aid people to identify outliers and NULLS, and find out whether there are any missing values or not. Through aggregations, sorting, filtering and slicing, SQL can help people to get thoroughly familiar with their data set, understand the manner values are distributed and organized, and carry out diverse tasks through it.  SQL to a data scientist is much like what scalpel is to a surgeon. It allows them to incise through a data set to acquire a better and more accurate understanding.

2. Integrates with discerning scripting languages:  While SQL is an extremely robust tool for manipulating, querying and accessing data, it also has limitations in certain aspects, such as visualization. Modern data scientists, however, are required to systematically present their data in a manner that can be easily understood by the company leadership and other relevant team members and stakeholders.  SQL essentially can be integrated with discerning other scripting languages like Python and R, enabling the data scientists to conduct their tasks easily by incorporating their code package as a stored process.  Moreover, various exclusive connection libraries meant for SQL like MySQLdb and SQLite can prove to be immensely helpful in connecting a client application to the database engine, thereby enabling people to work with their data set with extreme ease. 

3. Managing expansive data volume: In the majority of the cases, data science involves dealing with a large volume of data that are stored in relational databases. To properly work with such expansive data volume requires powerful solutions for its management, besides the typical spreadsheets. With the increase in the dataset volume, it eventually becomes unfeasible to use spreadsheets. SQL comes as the ideal solution for managing large data sets.  Through this tool, people can easily deal with pools of data in relational databases, and query, communicate and deliver valuable insights through it. 

4. Gateway for data science jobs:  In the modern market, proficiency in SQL is demanded more than any of the other programming languages for the majority of the data science jobs. The sphere of data science involves dealing with expansive sets of data in databases, and doing so would require a good level of SQL knowledge and expertise. The knowledge of this programming language is among the most marketable skills in data science. Moreover, SQL can be integrated with a variety of other database management systems. This includes dBase, Microsoft SQL Server, Oracle Database and MySQL among others, which help in the dynamic creation of SQL statements for discerning projects. Such integrations also make it possible to switch between various systems. SQL is a programming language which is used in numerous industries, such as banking, transportation, manufacturing, software and so on.

The capacity to ensure the proper storage, updates, access control and manipulation of data sets itself is highly valuable for all data scientists.  On the whole, it can be understood that SQL is here to stay, and trying to master it would prove to be extremely beneficial for any aspiring data scientist.

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The steps to Master Sql For Data Science

1. Understanding the Relational Database basis:  An organized collection of data oriented in the form of a table is known as a relational database, and SQL is needed for accessing, retrieving and manipulating data in such a database.  Important terminologies used in regards to relational databases includes: 

• Relations refer to the tables in the database 
• Records or tuples imply to the number of rows present in the database
• Data categories featured in the columns are known as attributes
• A unique value identifying information in the database. Is called the primary key 
• Foreign Key essentially acts as a link for the primary table to another table.

In addition to gaining knowledge about relational databases, one must see to it that they have a proper understanding of relational algebra as well. 

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2. Know the SQL basics: As one gets a good understanding of Relational Databases, they must proceed forward to do the same for SQL. This programming language is largely based on relational algebra which defines a set of logical rules meant for modeling data.  There are many online tutorials that one can check out to know the basic SQL commands. They can even install user-friendly and easy tools like SQLite and MySQL to practice SQL. Consistent practice is crucial for ultimately mastering SQL. 

3. Data Manipulation Language: This language allows people to add, modify and even delete data in a database.  INSERT, UPDATE and DELETE are some of the most common DML commands. With the usage of the INSERT query, people can easily insert records into the datable. Subsequently, they may use the UPDATE query for modifying columns in the database, while the DELETE command can be used for deleting rows. 

4. Gain a good understanding of the Data Definition Language concepts: Data Definition Language or DDL is another crucial command in SQL. Through this command, people can describe database schemas and data structures, as well as manipulate them.  The structure of a table can be changed through the alteration of a table, its deletion or even the creation of a table, for example.  CREATE, ALTER and DROP are some of the common commands of Data Definition Language. CREATE is used for the creation of a new table in the database, while the ALTER command is used for altering a database structure. Through the DROP command people can additionally delete a whole record stored in the table.

5. Know the SQL Joins: In the SQL programming language, Joins are among the most vital concepts. In fact, in many of the job interviews for data scientists, questions in regards to Joins are commonly asked.  Overall, Join commands in SQL basically enable people to merge columns from a single or multiple tables into one. In case there is a join only from a single table, then the process is called self-join. There are four joins in SQL, which are : 

• INNER JOIN: When two tables feature column values that match then an INNER JOIN takes place. Explicit JOIN is among the most common and best practice for INNER JOINs.  

• LEFT JOIN: This join returns all values from Table A or the left table and its equivalent matching records from Table B or the tight table. The result is NULL in case there is not match between these two tables. 

• RIGHT JOIN : This join returns all the values of Table B or the right table, and matches its equivalent values in Table A or the left table. 

• FULL JOIN: In the scenario that there is a match among any of the records of either of the tables, then the FULL JOIN command is used in order to return all values of the two tables.

6. Know how to interface SQL with R and Python: The ultimate step to master SQL for Data Science would be to understand and learn its interfacing with other popularly used programming languages like Python and R.  When it comes to the web development domain, one can frequently witness languages like Ruby and PHP using SQL for the purpose of retrieving data from the database. For data science, Rhythm and SQL are the two prime languages that leverage SQL to retrieve data. This is the very first step for obtaining data for further analysis and processing. 

The steps underlined above can go a long way in enabling people to master SQL for data science and explore advantageous employment prospects in this domain.  Being a free open-source programming language, for many, SQL lays down the foundation of data science and can make a lot of important tasks easier. Communicating with relational databases especially becomes simpler as people try to master SQL. Moreover, it will be a relatively easy programming language to learn for any budding data scientist. In the process of mastering SQL, people shall gain a deep knowledge of data sets, acquire the skills needed to manage huge data sets, as well as know the various scripting languages SQL can be integrated with.

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