Why You Must Learn SQL to Become Data Analyst or Data Scientist? Find out Top 8 Reasons below

Do you want to become Data Analyst or Data Scientist? But still not started learning SQL? Buck up guys start learning SQL now. Why you must learn SQL these are the top 8 reasons explained below.

What is SQL?

SQL stands for Structured Query Language used as a dialect to interact with databases such as Microsoft SQL Server, Oracle, Teradata, DB2, Sybase, Mysql etc. SQL can be used to perform Data Extraction, Data Preparation, Data Manipulation, Data Mining etc. SQL was developed at IBM by Donald D. Chamberlin and Raymond F. Boyce in Early 1970’s. SQL is referred as High Level Language or an English alike language.

Different Components of SQL Language:

Data Definition Language (DDL)

Data Manipulation Language (DML)

Data Control Language (DCL)

Transactions Control Language (TCL)

Top 8 reasons you must learn for making career as Data Analyst, Data Scientist:

1. Easy to Learn and Understand:

SQL is Easy to learn, understand and apply. Although it comes with a bit of learning curve for Non techies but still if you learn SQL from scratch, practice and apply the concepts you become better than techies while writing SQL codes.

2. Data Retrieval/Extraction:

Most of the time Data will be present in database, specially transactional data generated in Banks, Insurance, Healthcare, Retail, Ecommerce etc is most suitable to be stored in Database. Hence to retrieve the data from database SQL is the commonly used language to interact with database. Also data retrieval is faster using SQL.

3. Data Preparation and Wrangling:

Once the Data is extracted from the external source, it may not be in an appropriate form. Hence you need to enrich the data. For that you may have to clean the data, restructure your data, transform the data into the desired format, merge the data from different sources and put it in a new dataset etc. This entire process is referred as Data Wrangling. Now the data is prepared for further manipulation and analysis.

4. Data Manipulation:

Many times the data which you are looking forward is not present in a single table, hence you have to join 2 or more table to get the data in a single tabular form, or you have to perform certain calculations, aggregations on a numeric column for further manipulations or you need to find out what is the 2nd highest revenue generating customer, or you need to know what is the last ordered product by the customer, what is the date on which the patient has to come for next follow up after 2 months, customer id, patient id generated etc. These tasks are the part of Data Manipulation which can be easily done by SQL Joins, SELECT Statement using various Functions such as aggregate, date, string, ranking, analytic etc.

5. Data Mining:

Many times just by seeing the data we do not get the real picture or insights which are hidden out of it. Hence for Data Mining purpose even SQL is also now a day used by many organizations. Using Data Mining meaningful patterns and insights can be inferred such as segment potential leads/ customers who can bring more revenue, understanding your customer needs and requirements, forsee customer attrition or churn, sales and inventory forecasting etc.

6. Preparation for Test Environment by Table Creation:

Many times you are required to create your own tables to test certain analysis done before they are deployed in the model in the live environment. Hence SQL can be used for this purpose.

7. High Paying Jobs:

Based on the current trend in market candidates who know SQL well with Python, R are absorbed faster and get higher salaries compare to candidates without the knowledge of SQL. SQL can be easily integrated with the scripting language for fetching the data for building machine learning models.

8. Wider Industry Acceptance:

SQL is used by almost all mid-sized and big organizations such as Accenture, Amazon, Altisource, AirBnb, Analytics Quotient, CityBank, ICICI Bank, HDFC Bank,Standard Charted Bank,JP Morgan, Wells Fargo, IQVIA, Cerner, Google, Facebook, Twitter, Flipkart, Ninjakart, WNS etc Hence you have wider industry acceptance of candidates with SQL hands on.

End Notes:

Now the question arises how you can learn SQL?

If you are looking forward to learn SQL you can learn Online. There are many online resources from which you can learn SQL easily such as Edx, Coursera, Datacamp etc but the only problem is most of the courses available are recorded ones and you really need to be focused, maintain the same energy throughout the session, maintain the passion and motivation to complete the course. If you can do these then you can learn SQL for free. But in case if you are looking forward for Live Instructor Led online paid sessions, I can be your instructor and mentor. I can provide you Instructor Led Online Training in SQL for Data Analysts and Data Scientists with 250+ Assignment Questions, 5 Tests, 3 Projects, Learning resources such as SQL Manuals, E-books. You can contact me via LinkedIN.
I hope this article helps and would give you a clear path of what next can be done after completing your training in Analytics and Data Science. If you feel this article is helpful for you, please like and share this article with your friends. You can connect with me on LinkedIN and discuss your concerns.

Thank you,

Best Regards

Dyuti Lal

About the Author

Dyuti is an Analytics Enthusiast. She is an MBA in Finance and B.E in Computer Science. She has years of experience in the field of Analytics and is also the Co-founder, CEO, Nikhil Analytics. She has prior worked with companies like HCL Technologies, Deutsche Bank ,WNS, Reliance Capital etc.

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