Is Data Scientist & Data Analyst are same? Learn the Differences Now!


If we talk about the hottest jobs in tech right now, then only two names will pop up in your mind those are a data analyst or data scientist. And, that’s not only we are saying even the Harvard Business Review declared “data scientist” as the“sexiest job of the 21st century.” But there is a lot of confusion between both the names even people who have some basic knowledge of data science consider both the job profiles as one.

  Well, the reason behind this confusion is both of the professions deal with big data, rather defined vaguely and sometimes used interchangeably with one another.

difference-between-Data-science-and-data-analyst

So, here we are to explain the differences between the two of them and that too in the easiest way. Let’s begin the brief introduction of both the terms, followed by skillsets each require to understand well. 

Definition

a. Data Analysts


Data Analysts play a vital role in the Data Science domain. As a role, they play a variety of tasks associated with collecting, organizing data and getting statistical information out of it. Besides that, they also showcase the data in the form of charts, tables, and graphs and use the same to build relational databases for organizations. There are various other fields and titles which are associated with Data Analyst job roles such as Data Architects, Analytics Engineer, Database Administrators, Operation Analyst, etc.

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b. Data Scientists


On the other hand, a data scientist, make predictions which further assist businesses to make critical but correct decisions. However, as Data Scientists are proficient in statistical, mathematical, and computer application skills too they can perform almost all the tasks a data analyst used to do. Moreover, they are the ones who are effective in selecting and solving the relevant problems that can help a business excel.

Skills

a. Data Analyst 


Top skills a data analyst hold is basic math know-how, understanding of algorithms, good communication skills, knowledge of software engineering, programming languages like Python, SQL, R, JavaScript & HTML, Spreadsheet Tools (Excel) and Data Visualization Tools like Tableau.

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b. Data Scientist


Like a Data Analyst, a Data Scientist also has basic math know-how, good communication skills, understanding of algorithms, and knowledge of software engineering. But being expertise in the job the skillset of a Data Scientist also requires a knowledge of programming languages like Python, SAS, R, Matlab, Pig, SQL, Scala, and Hive, Business Acumen, Story-telling, and Data Visualization, Distributed Computing frameworks like Hadoop and Machine Learning Skills.

  We can say the strong foundation of acumen along with the ability to communicate the findings in the form of a story to both business stakeholders as well as IT leaders is something that differentiates a data scientist from a data analyst. 

On a concluding note, we have seen enough that differentiates the two hottest IT professions in the big data world. Regardless of the differences and similarities between a data analyst and a data scientist job roles, one is incomplete without the other. And, this is the best time to master any of them and get started in Data Science domain. 




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