What’s in a name? Correctly said by Shakespeare and still stands valid for our topic of discussion also.

 

                                   Whether it is data scientist or data analyst, either one of them will be the hottest job of 21st century. As awarded by the Harvard Business review as the “Sexiest Job of 21st Century” to Data scientist profile. But there is still a lack of clarity in the minds of students and even the working professionals working in this field of analytics that what actually is the difference between data science and data analysis.

Let’s first discuss about Data Analysis, this field is as old as the field of statistics. Normally data analysis does not require even computers it’s all about analyzing the data with the statistical procedures and creating insights into the data and to help making better decisions. In the current time the data analysis comprises of using software tools for generating reports, analyzing data, visualization of data, making predictions and many more. So, if we want to categorize a role as a data analyst role then for this role you will be needing good knowledge of statistical algorithms, ability to handle spreadsheet tools to organize data as we don’t want to store data in paper-based storage nowadays, knowledge of visualization tools as data representation is also an important aspect of data analysis, and a little bit programming skills to assist the statistical analysis.

  Now the most discussed job profile of 21st Century, the Data Scientist. So, a data scientist has to master every skill that a data analyst has, apart from that there are some additional skills that make the role of a data scientist different from a data analyst. Basically, a data scientist is someone who can not only provide solutions to business problems by answering their questions but can also add new business questions to add value. A data scientist should be good to find patterns in the data  and find correlation among several entities which can give a organization even deeper insights to expand their horizon. The role also requires a good knowledge of machine learning models and skills like artificial intelligence and deep learning, knowledge of programming languages like R/Python/SAS.

But still there is no concrete boundary between a profile of data scientist and data analyst, companies do use these terms interchangeably.

Sometimes data science is considered as a sub domain of data analysis and some time as a different entity.

So, that’s why we stated what’s in a name just stick with the roles and responsibilities.