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Data Analyst vs. Data Scientist vs. Big Data Expert

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Data Analyst vs. Data Scientist vs. Big Data Expert

The field of data manipulation is vast and varied, and there is room for everyone. With that in mind, let's see what separates data analysts, data scientists, and big data experts.

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With the advent of data science and big data as a mainstream career option, there has been a lot of confusion about the different options out there. Several claims suggest data analysts will be obsolete after big data, while some claim that big data and data science are the same, or one is a subset of another. When it comes to one profile eliminating the other, only time can tell. As for the differences, a simple factual study of either can reveal the truth about them.

Data science has been here for a long time, while big data, on the other hand, is fairly new, originating from the former — with significant changes. Data analysis leverages the techniques and software systems used in either (and vice versa with respect to techniques) but is a whole different story.

So, here is a little comparison between data analysts, data scientists, and big data experts!


Data Analysts

Data Scientists

Big Data Experts

Definition

Using automated tools, they fetch segregated data and insights. They define datasets and do an extensive demographical analysis to determine business and product related strategies.

As evident from 'scientist,' they fetch data, construct and maintain databases, clean and segregate data for various needs and also work on data visualization and analysis.

They deal with a continuous and large amount of data, define parameters and datasets for analysis, and program analytical systems to provide strategic insights for businesses.

Skills Required

Programming, Statistics and Mathematics, Machine Learning, Data Visualization and Communication, Data Wrangling and Dataset Definition

SAS/R/similar tools, Python, Hadoop, SQL, Restructuring Data, Database Construction and Management

Mathematics and Statistics, Programming and Computer Science, Analytical Skills, Business Strategy

Application

Healthcare, Insurance, Travel, Administration, Gaming, Distribution Systems

Search Engines, Advertisements, Adaptive Algorithms, AI Systems

Retail, E-commerce, Financial Services, Communication


This makes it pretty clear as to the roles of any of these three profiles. The most important difference is the application, which further shortlists the industries that hire them. And the difference is substantial and diverse. There certainly is an overlap in the skills and requirements and also, the work. The overlap, however, is because of the common foundation all these profiles stand upon. And because of that, the profiles have a hierarchical progression among them.

  • As you can see, data analytics is the most basic among the options. The job of a data analyst has wider application and is thus the more diverse adoption in different industries. Even the educational and academic requirements are lower for a data analyst.
  • Next in line are big data jobs, which are fairly complicated and require advanced skills. Sometimes, a big data certification is a mandatory requirement to get a big data analyst job. The scope for big data jobs is increasing by the day due to the penetration of digital technologies in various industries.
  • At the top lies data science jobs. These have a diverse set of profiles under them and require certain expertise. Data science certification is mandatory to get a job. The scope for data scientists is lower compared to big data due to the different (individual) profiles that lie under the umbrella of data science.

As of the threat to data analysts from big data experts, it is important to understand while big data analysts can take over data analysis with some modifications to their profiles, it is very unlikely. The reason is that the type of data and its processing needs (and end goals) are very different for either of the profiles — that's also why they cater to different industries.

With the above information, the differences among data analysts, data scientists, and big data experts should be clear. This info can be used to make better career plans in the field of data analytics and business strategy. Informed decisions are always the best decisions one can make. Research well and analyze and success will be a few steps ahead!

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Topics:
data analysis ,big data analytics ,data science ,big data

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