There is Big Paycheck Waiting for Data Scientists having Expertise in Open-Source!

There is Big Paycheck Waiting for Data Scientists having Expertise in Open-Source!

Data scientists seem squeezing a lot of compensation leveraging big data. But wait, wait for a minute! If you’re dreaming of getting big paychecks then remember one thing how much money you are going to garner is largely dependent on the kind of data scientist you are!

 

According to a new O’Reilly survey, data scientists whose area of expertise is open source technologies spin more money than those sticking with proprietary technologies. The more open source solutions you deal with, the more money you are going to make in big data.

 

Big data means Big Money: Depending upon the interest in Big Data, it’s surprising to see companies spending a whopping amount of money on paychecks to get best employees hired. However, it’s not an easy way for enterprises to get best minds work for them. Hence, this scarcity of data scientists around is really going to up the pricing as far as their salary is concerned.  

 

Glassdoor Survey of Data Scientists’ Salary

According to Glassdoor, the median paycheck for data scientist in the United States is about 117,500 dollars. Contrary, the salary of a business analyst is 61,000 dollars and a data analyst about 55,000 dollars.   

In other countries data scientists fulfill all their desires in an age of 35 which in every case is a major achievement. Isn’t it a big price jump for getting your job title polished?

 

Tools of the data science trade: to put it simple, there is more to being a data scientist than just upgrading your job title. As per O’Reilly’s 2013 Data Science Salary survey, the big data domain has boomed in the arrival of recent complex tools that just a few people can decode or have even an idea of. Knowing these tools is what going to do wonder for you!

 

But which tools one masters turn out to have a large, material effect on its earning capability.

 

Well, the top data tool till date is SQL of course! Yep, it’s not surprising at all that data analysis has been around before we turned it into dazzling name ‘data science’. Accessing data through SQL queries has long been the standard form for data analysis.

But, once you go beyond this, you get to know how much of the most widely used Big Data tools are open-source, for example, Hadoop, Python, R and many more. More interestingly, there lies a split between the Hadoop group (orange) and the SQL/Excel group (blue).

 

Data scientists using one tool do not jump on another tool, the whole industry is divided into two groups, one with red group and basically making a border around Hadoop. According to O’Reilly’s report, the two clusters do not have tools in common and are distant in terms of correlation. There exist only four positive correlations between the two sets while there are as many as 51 negative correlations. 

 

The money reside in open-source: While it’s interesting to see the split between Hadoop and SQL, open vs. closed, it becomes even more interesting to see just how much this split affects salaries!

The more data tools one uses, the big paycheck he/she gets. Once a data scientist makes use of at least ten tools, his/her paycheck grows to a greater extent!

 

Interestingly, those coming into the open-source or Hadoop cluster are inclined to use more tools and, therefore, they stand a chance to get more money. As the report points out, median paycheck rise is proportional to the number of tools used from Hadoop clustrer, from 85K dollars for those who don’t use such tools to 125K dollars for those who at least are familiar with six tools.

 

Those who are in proprietary techniques like SQL use five or more tools from the proprietary cluster may be the reason of drop in salary.

 

If you want more highly paid positions, update yourself with tools like Python, R, D3, Hadoop frameworks and scalable machine learning tools.

 

The tools residing in Hadoop clusters have a common feature and that is allowing large data sets and/or support large data sets analysis. If you know how to work with large data sets, then there is a demand for you in the market. In particular the demand is for those who can perform more advanced machine learning, chart and real-time tasks on big data.

 

To sum it up, the open source tools may be better options for handing larger data sets, while the proprietary tools have a narrower, query based usability.

At last, if you really want a handsome salary, do dive deep into data science with open source like NoSQL, Python, Hadoop and other technologies and tools! 

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