Data Science is the most rapidly growing field, with tons of job openings. One must be familiar with SQL in order to work as a Data Scientist. While Machine Learning and AI now dominate the fields of Data Science, SQL, which is nearly 50 years old, remains one of the most important skills.

SQL is easily the most widely used language in the world, and as long as there is ‘data’ in data science, Structured Query Language (or see-quel as we call it) will continue to play a significant role. To analyze the data, we must first extract it from the database. This is where SQL comes into play. To train your model, you must have clean data, therefore SQL is not only a tool for machine learning.

This blog post on SQL for Data Science will teach you how to use SQL to store, access, and retrieve data for data analysis. SQL is a language for querying or retrieving data from relational databases.

Where does SQL fit in when it comes to Data Science?

Data Science involves data gathering, analysis, retrieval, and storage. Dealing with massive volumes of data is one of the most pressing challenges for data scientists. This is especially true in today’s environment, when huge volumes of data are available in almost every subject. Netflix, for example, recommends what you should watch next based on your recent viewing habits. Similarly, when you search for anything on Google, it gives results with goods or products that are comparable. Data storage and intelligent application make this feasible.

Why is SQL crucial for Data Science?

Easy to Learn and Use

Since it uses words from the English language in its simple syntax, SQL is always praised for its simplicity. Unlike some other sophisticated programming languages that demand a lot more effort and mental understanding, it makes it easier for you to understand the principles.

SQL is the ideal place for you to start if you are new to the discipline of data science. With just a few lines of code, you can quickly query and change your data to draw insights from it.

Understanding your Data

The core component of data science is data. You need to be able to draw out the true meaning from your data in order to perform data science, and SQL can help you with that.

You may efficiently examine and display your dataset with SQL for Data Science to generate reliable results. It will assist you in dealing with outliers, missing and null values, and other data abnormalities.

SQL Integrates with Scripting Languages

As a Data Scientist, you will occasionally have to communicate your findings to the other team members of the business when working on a project. It should be possible to understand the explanation with ease.

In these circumstances, SQL for Data Science can be of assistance because it interfaces well with the most popular scripting languages, like Python and R programming. You can link the client application with your database using some SQL libraries, such as SQLite, MySQLdb, etc. It eases the process of development a little.

Manage Large Volumes of Data

Massive amounts of data must be gathered and managed in databases in order to conduct data science. However, handling such massive volumes of data with spreadsheets gets tiresome. As a result, SQL gives you the right tools for managing such massive amounts of data and drawing conclusions from it.

SQL’s Processing Capabilities

SQL has the following processing capabilities:

1. Data Definition Language (DDL): The SQL DDL includes instructions for building indexes, defining relation schemas, and changing relation schemas.

2. Data Manipulation Language (DML): The SQL DML has a query language that is based on both relational algebra and tuple relational calculus. It also has instructions for inserting, deleting, and modifying tuples in the database.

3. View Definition: View definition commands are also included in the SQL DDL.

4. Authorization: SQL DDL instructions for defining access rights to relations and views are included.

5. Integrity: The SQL language supports (limited) kinds of integrity verification. SQL products and standards in the future are anticipated to have greater integrity checking capabilities.

Now we can go through some of the SQL commands to know it better.

1. CREATE TABLE command: The CREATE TABLE command is used to create tables. Columns are named and data types and sizes are specified for each column when a table is constructed. There is at least one column in each table. The syntax of the CREATE TABLE command is as follows:

You can also create a table using another table. Refer the below syntax and example:

The ‘CREATE TABLE AS’ Statement

2. SELECT FROM: The most basic example of retrieval is getting all of the material from a specified database table. If we wanted to know all of the film categories in the ‘Category’ table, we might use the following command:

Output:

3. DISTINCT: In some cases, we are only interested in knowing unique values. What if we want to know all of the MPAA ratings from the film table? Using DISTINCT, we can select only unique values:

Result:

Data Retrieval

1. WHERE: There are times when we only want to retrieve specific entries that meet certain criteria. Using the WHERE clause, we can specify conditions that must be met before returning the data. Essentially, we are filtering entries based on the condition. What if we only want to know about movies that are rated PG-13?

Result:

Furthermore, we can compile a list of movies with rental ratings of 4.5 or higher:

2. ORDER BY: We may sometimes better understand the data by sorting the returned result. What if we wanted the same list as before, but with PG-13 films arranged in length order from longest to shortest, and vice versa? With ORDER BY, this is simple to do:

Result:

3. LIMIT: Furthermore, there are instances when we are only interested in a small number of submissions. Here, we actually only care about the ten PG-13 movies with the longest runtimes, so we can utilize the LIMIT clause.

Return:

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Aggregations:

• GROUP BY & COUNT( ): In order to get insights, aggregates are frequently utilized to obtain a summary of the dataset. The GROUP BY clause is frequently used with aggregates. For instance, if we want to know how many rentals each client has already leased, we may count the rentals and sort the results by how many rentals each customer has thus far.

Results:

• SUM( ): Consider the amount of money each consumer has spent. This may be done by using the SUM aggregator function. In this case, we want the biggest spender to be at the top.

Result:

• AVG( ) & HAVING: After you have produced a grouped aggregate, we may add further conditions by using the HAVING operator. For instance, what MAAP rating categories, on average, contain films that are longer than 115 minutes?

Result:

Joins

• INNER JOIN (JOIN): You’ve undoubtedly observed that we can only accomplish so much with a single table, given the limitations. We frequently wish to examine the data from several tables. JOINs are used in this situation. On the basis of a shared key, we may link tables. For instance, suppose we wanted to find movies that weren’t in English:

(Please take note that this database only contains English-language films. With the query mentioned above, you will receive an empty table.)

Additionally, you may join several tables. For instance, the categories and accompanying average rating are listed below. In addition, we have returned numbers in ascending order of average rating.

Returns:

There are many types of joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, OUTER JOIN, CROSS JOIN. Here, we used JOIN, which by default is an INNER JOIN in which you get back entries only if they are present in both tables.

You may look at the Joining Data in SQL course if you want to understand more about the variations between these joins.

Conclusion

We looked through all of the instructions that will let you plan your own SQL track in this tutorial. This article addressed all commands, from the fundamentals to the advanced. If you enjoy my writing style, please follow me on Medium.