Installation of SQL for Data Science

Data Science has a number one for being the most promising job of the era since the time. You are all trying to join the Data Science learning race. This SQL for Data Science will help you understand how to use SQL to store, access, and retrieve data for data analysis.

By the end of this, you will be able to understand the Installation of SQL for Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us. To get in-depth knowledge of Data Science, you can enroll for a live Data Science online course

SQL For Data Science

This data generation is the reason why high-end technologies like data science, artificial intelligence, machine learning, and so on are popular. Useful insights are obtained from data that is called Data Science. Data Science involves tons of data being extracted, processed, and analyzed. What we need at the moment are resources that can be used to store and handle this massive amount of data. Here's where SQL comes in. You can use SQL to store, view, and extract vast quantities of data to more easily execute the entire Data Science process. SQL stands for Structured Query Language is a language of inquiry aimed at managing Relational Databases.

Relational Databases for Data science

A relational database is a collection of well-defined tables from which data can be accessed, modified, updated, etc. without altering the tables of the database. SQL is the relational database standard (API).

Returning to SQL, you can use SQL programming to perform multiple data actions such as querying, inserting, updating, deleting database records. Examples of relational databases using SQL include My SQL, Oracle, etc.

Let's get acquainted with the basic SQL commands before we continue with SQL.

Basics of SQL for Data Science

SQL gives a set of simple commands for modifying data tables, let's go through some of the basic SQL commands.

CREATE DATABASE-creates a new database
TABLE CREATE-creates a new table
INSERT INTO – fills new data in a database
SELECT-extracts database data
UPDATE-Database maintenance
DELETE-deletes client data
ALTER Code-Changes the database
ALTER TABLE-Changes a list
List DROP-deletes a table
CREATE INDEX-creates an index to search for an object
Down INDEX – lifts index

Let's install My SQL to better understand SQL, and see how you can play with data.

Installing My SQL for Data Science

It's a simple task to install My SQL. Here's a step-by-step guide for installing MySQL on your system.

Upload, edit, and modify data once you're finished installing My SQL.

Step 1: Build a SQL Database

A SQL database is a warehouse for storing data in a structured format. Now let's use My SQL to create a database:

Build DATABASE;

USE;

Two SQL commands appear in the code above.

In capital letters, SQL commands are defined and a semi-colon is used to terminate a SQL command.

Build DATABASE: This command generates database

USE: The Database Activation Command is used. You trigger the database here.

Step 2: Create a table with the functions required for the data

The creation of a table is as easy as creating a database. What you have to do is describe the table variables or functions with their respective data types. Let's see how one can do this.

CREATE TABLE toys (TID INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT, TEXT Item name, INTEGER price, INTEGER amount);

The following things occur in the code snippet above.

Use the command 'CREATE TABLE' to create a table named toys.

The table of toys contains attributes, namely TID (Transaction ID), Item name, Price and Quantity.

Each variable has its respective data types defined.

The primary key is declared to be the variable TID. A primary key basically denotes a variable capable of holding a unique value.

The details of the specified table can be further reviewed with the following command.

JOYS DESCRIBE;

Step 3: Insertion in table of data

Now that you have put together a table, let's fill it with some values. I mentioned earlier in this blog how you can add data to a table by simply using a single command, i.e. INSERT INTO.

Let's see how they do this:

Embed VALUES INTO toys (NULL, "Train," 550, 88);
INSERT VALUES INTO toys (NULL, "Hotwheels car," 350, 80);
INSERT VALUES TOYES (NULL, "Magic Pencil" 70, 100);
INSERT VALUES TOYES (NULL, "Dog house," 120, 54);
INSERT VALUES TOYES (NULL, "Skateboard," 700, 42);
INSERT VALUES TOYES (NULL, "G.I. Joe" 300, 120);

You simply insert observations into our 'toys' table in the code snippet above, using the INSERT INTO command. For each observation, I have specified within the brackets the value of each variable or feature that was defined during the table creation.

The variable TID is set to NULL as it auto-increases from one.

Let's now display all of the data present in our table. This can be done via the command below:

Pick * BY TOYES;

Step 4: Switch the entries to data

Let's say you have agreed to lift the price as it attracts a lot of customers to you. You change the variable price within a database.

It is easy, only use the command below.

Toys UPDATE SET Price=350 WHERE TID=6;

The UPDATE command lets you change any values / variables that are stored in the table. The SET parameter lets you select a particular feature, and the parameter WHERE is used to identify the variable or value you want to change. Let us update the data entry price in the above order, whose TID is 6. Now let us take a look at the table updated.

Pick * BY TOYES;

You can also change what you want to display by simply referring to the columns that you wish to view. For instance, the command below only displays the toy name and its respective price.

SELECT Item name, Toys FROM Price;

Step 5: Data Recovery

So it is finally time, after inserting the data and modifying it, to extract and retrieve the data according to the business requirements. For further data analysis and data modeling, data can be retrieved here.

Note that this is a simple example to get you started with SQL but the data is much more complex and large in size in real-world scenarios. Nonetheless, the SQL commands are still the same and that is what makes SQL so simple and understandable. With a set of simple SQL commands, it can process complex data sets.

Now let's get back data with a few modifications. Refer to the code below and try to see what it is doing without looking at the output.

SELECT * LIMIT 2 FROM Toys;

You know this! In my table, it shows the first two observations.

Let's try one more interesting thing.

SELECT * ORDER BY Cost ASC FROM toys;

The values are grouped in relation to the ascending order of the price variable, as shown in the figure. If you want to look for the three items that are most frequently purchased, what would you do?

It really is quite easy.

SELECT * BY Quantity DESC LIMIT 3 ORDER TOYES;

Let's give it another try.

SELECT * FROM TOYES WHERE > 400 ORDER BY ASC price;

This question extracts the toy's information that costs more than 400 and arranges the production in ascending order of prices.

So that's how you use SQL to process the data.

Conclusion:

I hope you reach a conclusion about using a SQL database for Data science. You can learn more through Data science certification.