“There were 5 Exabytes of Information created between the Dawn of Civilization through 2003, but that much Information is now created every Two Days” - Eric Schmidt
The idea of Specialization of Labor became a raging principle as organizations expanded and became more and more diversified. The idea referred to the utility in breaking down larger tasks into smaller chunks and assigning the responsibility of completing each of the activity to specific individuals on the basis of their expertise.
Even though each individual happens to be single-mindedly committed to the task of accomplishing the goals of the organization, are they the same? The answer is definitely ‘NO’.
The same logic applies to the topic of Data Analyst vs. Data Scientist. The prefix of ‘Data’ in both the designations conveys a sense of similarity in terms of the fact that both individuals happen to deal with Data.
However, sharing similarity does not imply interchangeability. Thus, the question of Data Analyst vs. Data Scientist is very much a real one.
In this blog, we shall look at the topic of Data Analyst vs. Data Scientist. The issue will be sought to be understood through different lenses such as Data Scientist vs. Analyst: a Comparison of Skills, Roles and Responsibilities, Educational Background as well as Data Analyst vs. Data Scientist Salary. Consequently, we shall take up a tripartite comparison on the issue of Data Analyst vs. Data Scientist vs. Data Engineer.
Who are Data Analysts?
A Data Analyst is a qualified professional responsible for acquiring data from different sources, cleaning and organizing it as well as conducting data analysis on it. They happen to sift through data in order to identify patterns and trends (data mining), thus converting valuable business information into actionable insights.
They make use of different Data Manipulation techniques in order to interpret complex datasets. In this capacity, they should be proficient in deriving meaning from numeric data, have a strong grip over programming languages, as well as be adept in the fundamentals of data handling. They generally operate on structured data for resolving quantifiable business problems.
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Who are Data Scientists?
Data Scientists generally hold senior positions and are possessors of advanced degrees. They utilize more advanced data techniques and strategies like neural networks, clustering, decision trees and so on; for not only interpreting and analyzing data, but also for making predictions about the future through building predictive models.
They are highly proficient in Coding, Statistics, Mathematical Modeling as well as Machine Learning. Skilled with advanced programming, they are capable of creating new processes for Data Modeling within the discipline of Data Science.
Who are Data Engineers?
If a Data Analyst and a Data Scientist occupies two ends of a straight line, a Data Engineer happens to be positioned somewhere in between those two end points. They are primarily responsible for preparing data for analytical and operational purpose.
In the capacity of a Data Engineer, you should be well versed with the maintenance and development of Data Architecture. They are proficient in creating and integrating APIs and have a strong understanding of Data Pipelining.
Data Analyst vs. Data Scientist: Similarities
The topic of Data Scientist vs. Data Analyst is not necessarily an elaboration of the differences between the two positions alone. A proper analysis of the two positions will be incomplete without looking at the issue of Data Analyst vs. Data Scientist in terms of the similarities between the two professionals.
Let us look at some points of overlap.
- Both are responsible for dealing with Data in the accomplishment of business goals. Thus, they are often found to be working in the same business units.
- They both leverage predictive analytics, statistical analysis and reporting techniques to understand trends and for analyzing data.
- Both roles require an understanding of programming, databases, machine learning algorithms, and other tools used for data manipulation and data analysis.
- A Data Analyst as well as a Data Scientist is required to possess excellent written and verbal communication skills as they are responsible for conveying their findings to business leaders.
- Both require expertise in traditional statistics.
- Data Analysts as well as Data Scientists play a critical role in helping organizations gain insights into their operations and performance through meaningful data-driven decisions which can have significant impacts on the bottom line of their respective companies or clients.
- Both the professions have garnered exceptional popularity in the public eye especially with the intensification of the wave of Data Boom.