A relatively new realm in IT, the term Data Scientists and Data Analysts are often used interchangeably, but do they really mean the same? Big Data is the next big thing in IT, throwing up millions of jobs thus making it necessary for us to understand its terms.

Data Analyst vs Data Scientist, Let’s understand these terms from the kind of responsibilities they hold.

Data Scientist

  • Builds statistical models enabling decision-making through data which is tough for example block a page from rendering, or soft, allot a score for the malevolence of a page that is used by downward systems or humans
  • Guides causality experiments to identify the root issues of an observed phenomenon. The same can be achieved by designing A/B experiments or if A/B experiment is difficult apply epidemiological approach to the problem
  • Finds newer products and features that are derived from unlocking the value of data; being a thought leader on the value of data

Data Analyst

  • Writes convention queries to answer compound business questions
  • Conceives and implements fresh metrics for netting formerly not so understood parts of the business / product
  • Identifies data quality concerns, such as data gaps or partialities in data acquisition
  • Cooperates with the rest of engineering team to gather incremental new data

The above role and responsibilities reflect a clear overlap between the two roles where a Data Scientist is required to write custom queries and a Data Analyst to build a decision-making module either by simple rules or applying machine learning principles. However, the qualifications or pre-requisite for both these are somewhat different. Let’s take a look:

Data Scientist Data Analyst
· Requires knowledge of Database Systems such as MySQL, Hive and machine learning such as Mahout, Bayesian, Clustering, etc. · Requires knowledge of Data Warehousing and Business Intelligence concepts.
· Good to have know-how of Java, Python, MapReduce job developments · Good to have know-how of ETL Tools
· Understanding of analytical functions such as median, rank, etc. · Understanding of SQL and analytics, Hadoop based analytics (HBase, Hive, MapReduce jobs, Impada, Casscading, etc.)
· Expertise in mathematics, statistics, correlation, data-mining, etc. · Expertise in data storing and retrieving tools, tools and components of data architecture
· Good knowledge of ‘R’

A look at the salary and the tools used also reflect significant difference between the two.

Salary

A look at the salary of Data Scientist and Data Analyst reflect a clear bias towards Data Scientists who as per a survey conducted in 2015 earn $117,000 while a Data Analyst earns $62,000.

Tools Used 

While the term may be used interchangeably, these is of course clear difference between a Data Analyst and a Data Scientist in terms of role and responsibilities, tools used and the qualifications required to become one.

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