SQL: The Basics for Data Science Newbies | Learnbay

 

SQL- is this a Database Query?

SQL is the abbreviation of ‘Structured Query Language’. This is such a computerized language that bridges the communication gap between a database and all of its users. So in case you need to carry out any task with the help of this database, or better to say if we have any query with this database, then we communicate with the same through this language, SQL. That’s why it’s called ‘database query language.’

What SQL does?

It manages all the relational databases and is the unit of the same.

Now, what relational database exactly is?

Suppose I gave you a set of data but in an unstructured manner. So, you can evaluate the relationships between all the data provided to you.

Here, what will be your first step to identify the internal relationships between provided data?

Arranging all the data you have in a tabular form will be your first step? Right?

Such kinds of multiple tables, in which each table is well-defined, form databases. If all the data from these can base offers flexibility of their alternation, accessing, and updation but without making a single change to the main database, then the considered database is termed as ‘relational database.’

‘Sequel’- What’s that?’

Well, it’s not a newer type of programming language or quarry language. Some people pronounce SQL as ‘sequel.’ But you are free to choose any of these two pronunciations.

In what way does SQL Work? And How it’s Empowering Data Science?

With the help of SQL programming, one can carry a range of activities with the data in a given data set.

Are you getting confused about what type of activities? Well, in the case of excel, what type of operation you can do. Inserting, deleting, sorting, query generation, etc., right? The same can be done with SQL but in a more advanced way, yes, far more intelligent than advanced excel.

‘Database’ is the foundation of data science and such databases are not like a small cup, rather it resembles a vast and endless ocean. So do you think applications like excel, SPSS alone can handle endless queries, automated process, algorithms managements?

Certainly not. Hence SQL became the foundation of data science. Although data analysts can manage solving problems without core knowledge of this quarry language (using tools and applications) but in case your target is ML or AI, then you will be taking the biggest risk of your career if you ignore learning SQL.

Step-by-step Process of Query using SQL

Creation of Query: Suppose you want to know the name of a cute girl. What will you do is ask a question, ‘what is your name?’, which is actually a query. Similarly, to avail our desired output from the database, what we need to do first is to generate a query, which is termed as ‘SQL query.’

Passing of query through translator and parser: Being a high-level language, to make the SQL query understandable by machine, we need the help of a translator that breaks the query into low-level language. Next comes the responsibility parser that checked the converted low-level language for any kind of syntax error.

Optimization of query: If you want to know the name of a cute girl mentioned above, you can ask her the name in different ways. Please follow a few of the variable questions but with the same results.

1. What is your name?
2. What is your good name?
3. Please tell me your good name?
4. Please tell me your name?
5. By which name should I call you?

Similarly, in SQL, we can input the same query in variable ways. ‘Query Optimizer’ search for the best-fit query related to the input that has the highest degree of execution efficacy.

Evaluation of query: This is the final stage, in which once the query gets executed, the end-user can visualize the final output, better to say, answer to the query.

How SQL won the race in the data science field over other database management systems? (Benefits of SQL)

Firstly, you have to offer minimal effort to learn SQL. On the very second day of your training, you can do more with SQL on your own.

Secondly, no one can question its efficacy. Yes, SQL is a universally recognized quarry language used across the majority of relational database management systems (RDMS). So, once you become well-versed in SQL, then it’s predictable that you are able to work with any RDMS.

Thirdly, and the most important feature that SQL offers is ample scopes of data integrity. You can carry out endless data integrity tasks in collaboration with SQL.

Let’s Know the Basic Commands of SQL

SQL commands are based on natural language processing. Below are the lists of few SQL commands for ease of understanding.

How many types of SQL commands are available?

Mainly five types of commands are available.

“DDL (Data Definition Language)”

These commands are used for defining structures. E.g. ALTER, CREATE, etc.

DML (Data Manipulation Language).

“DCL (Data Control Language)”

These commands are used for making changes in the access permission of users. E.g. GRANT.

“DML (DATA Manipulation Language)”

These commands are used for managing the existing dataset. E.g. INSERT, MERGE, etc.

“TCL (Transaction Control Language)”

These commands are used for making further changes to the data already manipulated by DML. E.g. ROLLBACK, SAVEPOINT, etc.

“Data Retrieval”

These commands are used for retrieving results or data from databases. E.g. SELECT.

Top 3 DBMS in the Data Science field of 2021

1.MySQL

At present, MySQL is the most popular RDBMS in the field of data science. This application is coded in C, C++ and powered by Oracle.

Why Choose MySQL?

• Additional security layer making the sensitive data projection stress free.
• Offer impressive scopes of vast amounts of data scalability.
• Associated with an In-built backup tool named ‘mysqldump.’ (both in the community and enterprise version)

2. MongoDB

Although MySQL won greater popularity. But, for most advanced data science problems, MongoDB becomes the first choice for data scientists/ engineers. This DBMS is a document-based and distributed database.

Why choose MongoDB?

• Associated with aggregation support.
• Offer ample amount of flexibility for altering data structures over time and as per further project requirement.
• This DBMS owns advantages of better geographical distribution, horizontal scaling, making the application easily available. Hence become a valuable choice for data scientists across the globe.

3. Microsoft SQL Server

Unlike MySQL, this is also an open-sourced RDBMS, coded in C and C ++.

Why choose Microsoft SQL Server?

• Extended opportunity for strong insight generation through structured, unstructured, relational and non-relational databases.
• It provides multi-language as well as multi-platform support.
• This application makes the Big Data working environment easier due to its multi-platform functionality with Hadoop Distributed File System (HDFS), Highly-integrated analytical tools with SQL Server, Apache Spark, etc.

What Next?

Now you are well aware of the foundational knowledge of SQL. It’s time to dive deeper. If you are interested in a data science career switch and want to learn more about SQL, you can join our Certification Courses in AI and ML. We provide live online classes. So zero waiting time for your quarry solution.

You can Check Sample Video on SQL here.