Top 5 Difference between Data Scientist and Data Analyst

Data Scientist vs Data Analyst

Data Analysts and Data Scientist are some of the hottest buzzwords now-a-days. Sometimes, it’s getting confusing to differentiate between these two terms. Might be you also use these two terms interchangeably while there is a very subtle but considerable difference between them including different job to do.

Firstly, let’s define each term, which is itself its first difference:

Data Scientist: A professional with a better and deep understanding of statistics than any software engineer and also a better software engineer than a statistician.

Data Analyst: A data analyst is a person who works on a specific business problem that customer/employer has requested. Their task is directly related to the tangible business needs.

Unfortunately, no industry clearly distinguishes between these two roles. However, the mystery is in its details as both spans around a wide variety of different skill sets and functional roles. See the below image to understand the process. It could be the next difference:

Here, we have included the questions based on the job roles of a data scientist or a data analyst that could be their third difference.

By seeing the above image, you may have some idea of their roles. Let us clarify these in details. Typically, data scientists are supposed to create and manipulate the data to improve the future (what scientists really do). On the other hand, data analysts perform data migration and visualization roles that describe the past.

Moving forward, both of these contradict each other in terms of software tools that they use:

Data Scientist and Data Analyst use a series of the software tools – R, Spark, Tableu, Julia, Qilk View, mongoDB, Cloudera, Apache HBASE, SAP, Pentaho etc. However, tools are not foremost specified. These are based on the preference of their job role. Look at the below image to get an idea of their skill set.

Now move towards our next difference, we are defining the positions that are holded by the Data Analysts and Data Scientists:

If we discuss the salary of both positions then there is also some consistent difference. Just read below lines to get their difference:

Data Scientists can earn more money than Data Analysts Counterparts. Generally, salary depends o their role like if you are data analyst then what role you are performing matters like financial analysts, market research analyst, and operations analyst. According to a salary survey report by Bureau of Labor Statistics(BLS) in 2012, average salary of market research analysts is $60,570 , operations research analyst on average earn $70,960 and average salary of a financial analyst is $74,350. It is predicted that job market will grown significantly by 1/3rd till 2022, approx 131500 jobs. Currently an average salary of data analyst anges from $50,000 to $75,000 and for experienced data analysts it is between $65,000 to %110,000. On the other hand, the average salary for data scientists is $113,436. In Us and Canada, this figure is $122K. Data science managers earn an average of $176K.

This could be the next main difference between both.

Hopefully, you have an idea what all are the basic differences between Data Scientist and Data Analysts. Where data scientists plan about future, the data analysts think about the past doings and analyze that result. Both are the important position for any company. You can choose the one based on your interests. Still, you have any doubt on any of these; you can call us at +91 90691 39140 or email us at info@hub4tech.com.

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Source : Hub4Tech

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