11 Ways Data Scientists Use SQL To Achieve Results

With having a massive quantity of data in every sector nowadays, the need for data scientists is increasing day by day to structure that immense data. A proper skill is required that can store that data properly and bring meaning out of it. 

Andrew McAfee says, 

“The world is a big data problem.” 

You can see the list of jobs for data scientists who have experience using SQL. It is a common skill that is enlisted in most of the job post requirements. SQL will remain an important part of data science for as long as there is “data” in data science. SQL is the first thing that comes to mind when talking about data science. 

All About Mining Metaverse x
All About Mining Metaverse

If you still want to be clear with the definition of SQL and how data scientists use it, here’s a quick sneak peek. 

SQL is a basic programming language for storing, employing, and securing data in databases. The tool has become a very important one in the data scientist’s toolbox. It has very easy and quick-to-learn features to organize the data and to gain essential insights. Data scientists utilize this programmer to communicate with databases and use the dataset efficiently. 

Now that you know the clear definition of SQL, let’s tell you a quick fun fact before we dive into the ways of using it. 

Consider it a quick tip! 

To not get yourself laughed at when you enter to give an interview for your first data scientist job, do not start saying S-Q-L when talking about it. For the reason that Structured Query Language is pronounced as a sequel and not S-Q-L. 

That is because the initial version was named SEQUEL, for Structured English QUEry Language. You can apply this tip to save yourself from sounding like a dumb person. 

Now, talking about the ways to use SQL, there are a number of them present out there. Without further ado, let’s get straight into them. 

11 Ways to Use SQL 

Following are the top easy and effortless ways to use SQL that a data scientist must know. 

Select Statement 

Being a data scientist, you need to select or read a large amount of data from various tables to get structures, statistics, and other significant data. 

The fundamental select query is: 

select the “column, table, or expression” name

In the actual world, databases have millions of indexes and records, and if there are numerous sections or columns in a table you will be devastated by the size of results. To solve that problem, you can only choose the columns that you want to read and it will appear and remove the fuss. 

For instance, if you need to see the name and age of all enrolled students, you can choose the database by selecting the name and age of the student. 

String functions (text mining)

Many valuable string procedures are present in SQL just like we possess in programming languages. You can forgo jotting down lines of code when features like these are present in the DBMS(Database management systems) itself because it is quicker. Some traditional string functions are as following: 

  • Upper and Lower

You can use this feature to modify the whole string to upper or lower case.

  • Replace

If you want to replace one or more characters with another, you can employ this function. 

  • Concat

This function joins two or more various columns or strings to give an outcome as one. 

  • Substring 

Similar to the function of programming language, SQL substring brings specific aspects from a string. 

  • Len

By this, you can get your preferred number of characters in a string. For instance, you can select a student profile under a certain number of characteristics that you want. 

This will show only those students’ profiles that have briefer descriptions.

  • ltrim, rtrim

ltrim and rtrim eliminate the leading and trailing spaces from a string. 

Date functions

The handling of dates is a slightly complicated process but SQL does this easily without any hassle.

There are many data functions present in SQL: 

  • DATEADD – you can put in one year to the existing date. 
  • TO_DATE – using this, you can convert a string into a date.
  • DATEDIFF – you can reveal the variation between the 2 provided dates.
  • DATEPART – you can learn a specific portion of the date; for instance, year, month, or date. 
  • DAY – you can have the day of the month for the provided date which is a super exciting feature. 
  • CURRENT_TIMESTAMP – you can get the date and time in the timestamp. 

All the above-mentioned functions are relatively valuable to evaluate several categories of data. For instance, you can employ DATEDIFF to obtain data from a specific range of periods or employ the DAY feature to discover what days the majority of the students get absent or else. 

Aggregations (Statistical functions)

The aggregate processes enable us to discover the sum (SUM), average(AVG), minimum(MIN), maximum(MAX), and count(COUNT) values from the bundle of data. We use these processes or features with the group by and having clauses. 

For instance, if you wish to know what is the normal percentage of marks that the students of each department have obtained collectively, you can employ the AVG function.

In the same manner, if you want to find out the number of students in a specific department, you can employ the count feature. 

You can do that by selecting the count option from the certain department. 

Joins

It happens most of the time that you want to collate data from numerous tables and have merely those columns that are conforming to some situation or structure. For obtaining data from two or more than two tables, data scientists use the SQL joins option.

Regular expressions

It is very valuable to learn about regex. For instance, if you wish to verify a phone number, credit card, or any other numeric value that matches a specific sequence, you can use regex.

For representation, if you prefer to have to get a phone number that begins with a digit and not any bracket or any other symbol you can employ, 

Choose from contact where 

select * from contact where phone like ‘[0-9]%’;

You can do the exact system employing any programming language such as JavaScript, but SQL is easier and very less time-consuming than implementing code. The regular expressions can also be employed to discover a particular pattern in any section of a table. 

For instance, if you wish to obtain the name of students that contain ‘ya’, you can have it in front of you by employing the LIKE clause.

select first_name, dept from employee where first_name like ‘%ya%’;

Loading and copying data into the database

If you have a large amount of data in Excel or .csv layout and you wish to duplicate all of it into a DBMS, SQL can execute that for you.

You can employ the feature of COPY FROM to copy data from a ledger to the database. If the case is the opposite, you can use the same way to duplicate data from the database into a file, employing the COPY INTO command.

Data bucketing

As we know that by using groups, we can get the sets of data so that we can discover trends and determine business chances by evaluating them. Bucketing is the term employed to reveal those groupings (majorly with timestamps and numbers) and to develop histograms. This lessens human observance negligences.

For instance, using the truncate feature, you can obtain the closest round-off value for a decimal.

select truncate (average, 0) from employees; — if the actual value was 78.333, the result will be 78.

In the same manner, date_trunc is employed to organize dates together. This could be beneficial in pursuing user activity over some time. For instance, the actions of the students in an online study portal over a particular date interval such as weekdays and weekends.

Sequencing

With SQL, you can have sequences by generating the sequence of numbers on-demand in ascending or descending decree. Sequences can be developed utilizing create sequence <sequence_name> by providing essential details. 

Sequences are not related to any tables, hence it is valuable to fetch table values employing a sequence if there is present any. Calling the‘ next value’ function recovers the next row rather than choosing from the table. 

Updating a Row 

By using SQL, you can also revamp or update the already existing rows in a table. You can specify the update after you have set the keyword. 

Delete 

With SQL features, you can also delete rows from a table. To do that, you can employ the “The DELETE FROM” statement on the basis of a condition. If the condition is absent, the whole table can be deleted. 

The Bottom Line 

Steve Job said, 

“Everyone should know how to program a computer because it teaches you how to think.” 

If you’re a beginner and you haven’t yet possessed hands-on familiarity with SQL, you must try these ways that we have quoted above. As a novice data scientist, you may not be employing all the crucial or significant aspects of SQL. 

Nonetheless, once you begin fiddling with the data, you will certainly rejoice to dig more. The above list should definitely assist you to get through the features you will be required to employ as a data scientist. 

If you want to become a pro at SQL, the best way to do so is to learn SQL is by doing practice. You have various choices to set up a domain to practice SQL. Opt for them and start learning through experiencing. 

What are the basic SQL skills? 

Following is the list of the basic skills: 

  • Know to structure a database. 
  • Edit SQL statements & clauses. 
  • Organize the SQL database. 
  • Labor with traditional database features like MySQL and PostgreSQL. 
  • Master PHP. 
  • Know technical SQL data analysis for dealing. 
  • Establish a database employing WAMP and SQL.

How can I experience SQL at home? 

There are four steps that you can practice at home: 

  • Download the SQL software. Your initial step is to download database software. 
  • Establish your primary database and data table. 
  • Get yourself some data. 
  • Get curious and explore. You’ll learn the processes. 

References 

https://towardsdatascience.com/4-steps-to-start-practicing-sql-at-home-cb771beeca20

https://mashable.com/2017/08/11/learn-essential-sql-skills/

https://www.pinterest.com/amp/pin/762797255606028866/

https://towardsdatascience.com/11-must-know-sql-statements-for-data-scientists-a098b14c470a

https://www.kdnuggets.com/2019/08/top-handy-sql-feature-data-scientist.html

https://data-flair.training/blogs/data-science-big-data-quotes/amp/

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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