Data science is the superset that contains machine learning, data warehousing, data analysis, data mining, and many other related technologies. A data scientist is expected to make predictions using past data. A data scientist generally gives consultancy to the owner on where to focus more on the business and what could bring more revenue or customer satisfaction. To do all this data scientist needs knowledge in Business administration, Data analysis, machine learning, and many more field to correctly predict and consult the business managers. That is the reason a data scientist is more like a jack of all cards than a master of one.
The process of work of a data scientist is that they create new questions whereas the data analyst answers the already existing questions. A data scientist takes the data then they may visualize it then they analyze the data by implementing the machine learning algorithms on the data that helps to find patterns in that data. The patterns a machine learning algorithm spits out are not always obvious that is why the data scientist should have business operational knowledge to make sense of those patterns.
A data analyst is a person who analyzes the data and translates the data into plain English. They are also responsible for giving answers to the questions like, which department performed best this quarter and so on visualizing the data helps them backing the answers that they give and it looks cool too. A data analyst does not use machine learning algorithms to predict anything. They work and give results based on the past data exactly like the data scientist but they just give a report about what happened in the past not what will happen in the future or which department is going to perform better in the coming quarter?
So, the work of a data scientist is to look for different non-related data to find insight and convert that insight into a model with the help of visualization. Those models will help the business understand those insights, whereas the data analysts scrape the regular data of the company like sales numbers etc. They are supposed to find answers to the previously standardized questions given by the business, unlike the scientists that are supposed to formulate the questions whose answer’s will profit the business. Analysts are not required to be experts in machine learning algorithms as they do not need to find any new patterns in the data.
It is simple to guess that the data scientists earn more than the data analyst because of the broader area of knowledge required for the work and also the expertise required in the machine learning domain. Data Scientist was named the sexiest job in 2017 due to the median pay of $120K in the US and 6.7L in India while a data analyst earns a median salary of $60K in the US and 3.6L in India. Although most organizations do not yet fully use the data scientist potential and sometimes there is confusion in the job description of data scientist and analyst. Hope this article helped you in understanding the roles of data scientist and data analyst.
