SQL For Data Science – A Comprehensive Guide In 5 Points

Introduction

Anyone who is working n the Data Science field will know that the knowledge of sql for data science is fundamental to the field and everything else that comes after such as Python or R is built on top of that. 

1. What is SQL?
2. How does SQL work?
3. How do data scientists use SQL for data science?
4. 7 steps to master SQL
5. SQL features

1) What is SQL?

SQL (pronounced “Sequel”) is an abbreviation for Structured Query Language. It is basically a programming language that is designed to manipulate data stored in a Relational Database Management Systems or RDBMS. It is used to perform functions such as insert, delete, update, modify data, and more. It’s important to remember that SQL cannot write full applications and data scientists are only using sql for data analysis to fetch the data from databases. After that, the data is used for its respective purpose. SQL is a powerful language that is very simple to learn and forms the foundations for a career in the field.

2) How does SQL work?

Put in a simple way, SQL is a Query language that performs the task of retrieving information from databases. There are different types of methodologies for organizing and collecting data, but the fundamentals are exactly the same. Data comes to a server and a data storage system such as Apache or Nginx platform then processes that data into tables and stores it in a data warehouse server for SQL to use.

This is usually done by converting the data to a format that the database can use, because we don’t want to write directly to the database itself. The database warehouse is the core database engine that allows a SQL client to connect and communicate with it. In order to retrieve the data, the database warehouse forwards the necessary SQL request to an application server. The application server then processes it and sends that back to a web server which turns it into a presentable content to the particular user.

3) How do data scientists use SQL for data science?

For any Data Scientist, data is the fundamental thing they will be working with. There are numerous sources of data and it comes down to the data scientists sometimes to create their own database to manipulate it such as storing, moving or deleting it. SQL is what is used to retrieve data from databases followed by the rest of the procedures such as cleaning, etc. Beyond this data scientists apply all kinds of interpretation using machine learning models, testing, predicting, training, visualizing and more. Therefore database and sql for data science is at the core of the field and one cannot do without the other. 

4) 7 steps to master SQL

If you wish to master sql for data science you have to take a more methodical approach to learn from some fundamentals. Here are 7 steps on how you can start your learning into SQL:

Step 1: Start from the fundamentals of Relational Databases

SQL as you already know is the language for querying and managing data in relational database management systems (RDBMS). The two are so intertwined that they are often used interchangeably by the uninitiated. On the other hand the term SQL is also used to indicate non-relational databases under the umbrella term “NoSQL”. Any SQL skill you may have would still be useful in non-RDBMS systems as well. 

To get started, watch this course intro from the Carnegie Mellon University by Prof. Andy Pavlo. by clicking here.

Step 2: SQL Overview 

Once you are familiar with the relational databases, it’s time to learn about SQL. Watch this video below by Charles Germany to quickly learn the origins of SQL and a few examples by clicking here.

Next you will learn about how to write SQL basic queries and about the list of SQL commands.  Familiarize yourself with it as many of it covered in the next steps. You have set up an SQL environment and run it in the computer. For that you will need to install SQLite and command line shell for SQLite. 

Step 3: Selecting, Inserting, Updating

At this step it is important to learn the most widely used commands such as SELECT- for querying databases, INSERT- for inserting records and UPDATE- for updating existing records. You can find all the preliminary exercises on SQL Course: 

• Selecting Data
• Inserting into a Table
• Updating Records

Step 5: Views and Joins

Views and joins are slightly advanced topics. Here’s a quick look at views from Socratica

Joins is probably going to be one of the more complicated topics you will come across in SQL. Here is a tutorial on it by Socratica:

Step 6: Advanced SQL

Advanced SQL covers a range of things hence you can start with narrowing your study to this. Watch this second lecture by Andy Pavlo where he discusses the fundamental aspects you need to know by clicking here 

After you get a grasp of the aspects of SQL, move to the concept of stored procedures. Read from this MySQL Stored Procedure for complete details. 

Step 7: Query Optimization

Learning how to write a query is basic, then comes optimizing it for results and run time. It becomes more important as the databases grow larger. Watch the Query optimization lecture by Andy Pavlo of Carnegie Mellon to learn deeper, click here

Later, read this SQL Optimization tutorial by Beginner SQL Tutorial.  

5) SQL features

Here are some of the things you can do with SQL: 

• Generate queries from a query: You can use the series string to generate a mass query and use data fetch data from other systems. 
• Handle dates: Various date functions meets the user formatting and type conversion needs. 
• Text Mining: SQL’s built-in-string functions enable you to do a lot more before you consider a scripting language. 
• Load data to your database using the \COPY command. 
• Generate sequences: Time series and funnels can be handled with the generate_series function. 

Conclusion 

The need for sql for data science cannot be overemphasized for data scientists working in the field. SQL is traditionally used when querying data from relational databases and its success has made even the modern-day big data platforms to emulate it to process data that comes in parallel to the unstructured ones. The SQL mastering steps described above is enough to build all the background you will need for data analysis using sql. 

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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