If you want to be a data analyst, data engineer or data architect, you will need to learn SQL along with programming languages like C, R and Python.
Here is a simple diagram that shows the stages where SQL is used: The highlighted portion is where we need SQL knowledge: big data, big data analytics and data analysis.
Why SQL?Though there are NoSQL databases that offer high performance and speed, SQL databases are still most widely used for all practical purposes.
There are more developers who understand SQL technology and hence the support and documentation are more plentiful.
Further, data integrity is one key factor that makes SQL stand apart from any NoSQL database, by way of the assurance that no duplicates or unauthorized data can enter the system.
Also, for complex queries and joins, a well-structured relational database works better.
What is SQL?SQL is a relational database management system used for storing, retrieving, updating and reading data from the database.
If you get introduced to SQL from the basics through this beautifully designed course, you will love SQL for life.
For this blog, we will concentrate on how SQL matters to data science.
Let us take a simple example of how you as a data scientist could possibly use SQL to collect and analyze data.
Suppose you want to know the popularity of a book named ‘The Data Science Handbook’ by the author ‘Carl Shan’ by checking how many users ordered a copy of it.
Because SQL is a well-structured language with a proper schema, you could have a structure like this:To get this data, we need to join the three tables, using some common columns or keys.
In this case, order_id is common to all the three tables and using this data, we can write a query to fetch the necessary details.
In real-life scenarios, this kind of system can be at multiple levels, where huge data needs to be analyzed and worked upon.
Everyday data from millions of users is stored and analyzed for various purposes.
Imagine doing all this without the use of SQL; is it even thinkable?While some people want to believe that SQL’s role in a data scientist’s job is reducing, that is not the case.
SQL is here to stay.
Here are some key SQL concepts that a data scientist should know:Relational database modelIn a relational database model, all the data points are related or connected to each other.
While creating this kind of database, the relationships between various tables and columns has to be defined in the design stage itself.
In our above example, the three tables are related.
The customer table’s primary key (“a specific choice of a minimal set of attributes (columns) that uniquely specify a tuple (row) in a relation (table)”) will be customer_id, whereas order_id will be a foreign key (“set of attributes subject to a certain kind of inclusion dependency constraint, specifically a constraint that the tuples consisting of the foreign key attributes in one relation, R, must also exist in some other (not necessarily distinct) relation, S”).
In the same way, the book_id and order_id combined can be the composite key for book table.
These relations have to be defined during the creation stage itself.
DBMS NormalizationNormalization is the design process where tables in the database are organized in such a way to avoid redundancy and dependency of the data.
Using normalization of different forms, we can divide data into smaller structures and establish links between them so that the data is optimally stored.
This nice article presents information about normalization in a very simple and understandable manner.
Database schemaDatabase schema is the logical view of a database.
All the relations like constraints, tables, views, triggers etc.
that are applied on the data form the schema.
Basic SQL commandsSQL can execute the following types of statements:Check out this distilled list of SQL interview questions that can help you brush through the concepts quickly.
Who should learn SQL?By now, you should understand that if you are crazy about data and playing with it, and want Data Science as your career choice, you should definitely learn SQL.
Data scientist as a career choiceLoads of data is generated every day and needs to be converted into new business solutions, designs and products which can only come from the creative mind of a data scientist.
This need will only increase by the day at least for a few decades.
In addition to the fat package that the industry offers to a data scientist, it is the challenge and ever-growing roles that attract professionals towards this job.
From data administrator, data architect, data analyst, business analyst to a data manager or business intelligence manager, there are plenty of opportunities to choose within the data science circle.
Knowledge of SQL, programming languages like R and Python, statistics and applied math, paired with critical thinking and industry knowledge will get you there sooner than you would think.
Bio: Saurabh Hooda has worked globally for telecom and finance giants in various capacities.
After working for a decade in Infosys and Sapient, he started his first startup, Leno, to solve a hyperlocal book-sharing problem.
He is interested in product marketing, and analytics.
His latest venture Hackr.
io recommends the best Data Science tutorial and online programming courses for every programming language.
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