Companies are currently collecting and working on large amounts of data each day. The handling of this big data requires structured algorithms and precise calculations and this is where Machine Learning comes into the picture. However, Machine Learning has always been associated with learning Python, R, or some other programming language. Data scientists work on these large amounts of data, and what is data without a database?

So the question is- where do we start? The answer is pretty simple-SQL. The structured query language is the easiest and most important skill required to understand and maintain the database.

Before starting with how machine learning is associated with SQL let us understand what SQL is.

STRUCTURED QUERY LANGUAGE

SQL is a database language to manage data in RDBMS (Relational Database Management system). it helps in the creation, deletion, and manipulation of tables that store the data. The data stored is in a very organized manner and makes it very easy to access anytime.

Here, SQL queries are created to request access to the database. These queries are created with logic to access a particular type of information. This makes it very easy to maintain and store data efficiently.

Top reasons why SQL is required by a data scientist

1. Easy to learn and implement

Programming languages like R, Python, JavaScript, etc. require a vast amount of conceptual understanding, logical approach, and syntaxes to memorize. SQL is quite easy to learn and memorize in comparison. Queries are created with very common English words.

1. Understand the data

With so much data to work with, it is important to know the dataset first. SQL helps in understanding it. It gives an understanding of RDBMS and data manipulation. With so much data available you must know how to visualize it in numbers. You must know how to sort, organize and play around with it to make it easy to work on the projects. SQL helps with all of that.

1. Manipulate and handle the data

Once understood what data is and how it can be visualized, comes the important part- how to manage and manipulate it. Creation and deletion, updating, and sorting will help you manage large amounts of data. The whole point of machine learning is data wrangling and SQL helps with that.

Big data is managed and churned in companies and this requires careful analysis of the data.

1. Integrates with the scripting language

The data created and assembled needs a visualization too. This is done through programming languages like python and R. SQL gets integrated with these languages easily. A data scientist needs to work and show how the data works and visual implementation becomes necessary. To work on your dataset libraries like SQLite and SQLdb can be used to connect to your database engine.

1. Association with big platforms

Big platforms like Hadoop have extensions HiveQL to manipulate data. This makes it easy to work on big data with the same platform providing both database engine and data computation. Even for performing data analytics knowledge of large platforms like Oracle, Microsoft SQL, etc. SQL is required.

1. Pathway to become a data scientist

After discussing the usage of SQL at large it is very obvious that learning SQL will open more pathways to becoming a data scientist. In fact, in many places proficiency in SQL is ranked higher than programming knowledge in Python and others. It will make you use the organization's sought-after data scientists. All the major industries like healthcare, IT, manufacturing, banking, etc. have turned to big data and require people that can both, maintain and analyze the data. So it is important to understand that SQL is the foundation of data science.

SKILLS REQUIRED

1. Knowledge of DBMS and RDBMS is a must. To understand how the data is stored and manipulated in-depth knowledge of these two is a must.

2. SQL terms and commands: knowing commands of the SQL and a basic understanding of-

• Data Manipulative Language

• Data Definition Language

• Data Control Language is important. Along with this, basic terms like Query, Clauses, Sub-query, Null Value, Joins, etc. are a must too.

1. Learn to structure the database. Know the ins and outs of the data and tables

2. Knowledge and hands-on experience with an SQL-based DBMS. This can be any platform like MySQL, Oracle, Microsft SQL, etc. which will give you an extra edge in terms of getting that sought-after data scientist job.

3. Learn PHP. This open-source programming language is used to interact with MySQL. This will prepare you to handle varied projects in the organization.

In conclusion, I would like to say that SQL will always remain the foundation of data science. I would recommend getting a deep understanding of datasets, the organization of data, and ultimately how to visualize them. This will make you proficient in handling data and ultimately will ease the path for you to understand new technologies like Machine learning.