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SQL- A Leading Language for Data Science Experts

For Data Science it has been a popular and well known fact that Python, R and Java are the best programming languages, but according to a new research it has been revealed that SQL is as much necessary for data science programming as these languages. The research is published at r4stats.com, the site analyzes data science latest technological trends and the focus is clearly R programming language. . This time the site has shown its research study of indeed.com, which is a job site and has extensive capabilities of data mining.

In this research Muenchen focused on data scientist posting protocol of indeed.com and in that research, of his R passes SAS, but both of them were left behind by Python and the actual data SQL was at the topmost positions among all of the data science software. There near about 18,000 jobs were posted for SQL, while only 13,000 jobs were for Python and Java technologies.

Despite the SQL job posted on the site, maximum companies are still focusing on Python and It is leading against R, Scala and Java. Latest trend shows that in the world of data science, R and Python are still leading languages and they are followed by Java and Python.

Read: 100+ Data Science Interview Questions and Answers {Interview Guide 2023}

However, according to a few other researches, SQL is also prominent language for data science, especially in the case of Big Data analytics. Though, SQL is a decade old language, but it is a strong language for relational database management systems. Not only this the most important Big Data software, Apache Spark is also driven by SQL and is used in Big data analytics, machine learning and streaming. So, if you want to learn Big data analytics or data science, then SQL will surely help you a lot. There are a number of researches conducted, which have proven that SQL has left behind the most popular languages Python and R and especially for the latest technological trend, learning SQL can provide you a great help. A few researchers also have shown that SQL is just slightly behind R. So SQL is a leading language for database related projects.

The popularity of any language can straightly be measured by the number of jobs posted on the job sites in that language and here is where SQL is leading on indeed.com. To be an expert data scientist you must know the SQL language perfectly and familiar with it. For big data professionals SQL is incredibly important. Well, SQL is one of the top ten in demand programming languages for data science. You can refer a number of researches and know why SQL is important to learn Big data analytics, like Apache Hive, a software for Hadoop also uses HiveQL, which is a language use database queries, which are similar to SQL queries.

Nowadays online SQL training can help you, if you have decided to make the career in Big data or as a data scientist, then learning SQL will help you a lot. For any analytics based task, the SQL programming language is the best one. For open source R again the SQL libraries can help you a lot. For Oracle like Db software, no programming language can be better then SQL.

Read: List of Top 20 Data Science Tools For Visualization, Analysis

If you are still working somewhere, and want to improve your technical skills as data scientist then online sql certification can help you in getting familiar with SQL, without leaving your job. Just be an expert for the SQL analytics and become the best analytics.

Number of data analytics tasks like data aggregation, bucketing and ranking of the data, other statistical operations and windowing functions can also be performed with the help of SQL. The main SQL features which can help any data scientist include date handling, text mining, query generations, data loading from database, sequence generation and many more.

JanBask Training is a Real Time IT training company that specializes in various online courses in Online SQL Training , QA Training, Data Science Training and many more. Log on to janbasktraining.com for more courses online.

Read: A Practical guide to implementing Random Forest in R with example


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