Difference between Data Scientist Vs Data Analyst

Data Scientist Vs Data Analyst

An android programmer is an Android programmer? The role is the skill set. But what skill sets are useful for a data scientist or data analyst? Does it make it more difficult to staff and execute projects in the analytical space? Is data analysis a science, or is it the science of exploring data to prepare it for further analysis? Unfortunately, there is no standard industry using the terms Data Scientist Vs Data Analyst that clearly distinguishes the two roles. However, the devil is in the details; these roles tend to complement each another, but often involve a wide variety of different sets of skill’s and functional roles. A “Data Analyst” focuses on the movement and interpretation of data, typically focusing on the past and present. It is also possible that a “data scientist” is primarily responsible for summarizing the data in order to provide forecasts or an outlook for the future on the basis of the models identified from past and current data.

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Data analysts and data scientists are still differentiated by the type of roles they perform. Data analysts generally perform migration and data visualization roles that focus on the description of the past; While data scientists generally perform data manipulation roles and create models to improve the future.

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A list of common roles that data scientists and data analysts typically perform:

                             DATA SCIENTISTS                                DATA ANALYSTS
  1. Data Mining Experts
                              1.BI Developers
           2.Statistics  SMEs                              2. SQL Developers
           3.Trusted Advisors                              3.Visual Analytics Users
           4.Experiment Designers                              4.Data Mining Tool Users
           5.Advanced Analytics

             Software Experts

                              5.Report Owners

When trying to use a data analyst and a data scientist for a project in the analytical space, it is important to understand the project objectives and the client’s needs. Should the data be organized and analyzed to identify patterns of the past? Perhaps a data analyst is your best bet. Or does the company need the value of using past models to make their business decisions more robust? If so, a data scientist is the way to go. The objective of the project is to generate the visualization of the data, in which case either role would suffice.

data science vs data analytics

Overall, the objective of the project generally defines the scope of the role of data analyst and scientist.
While both roles can be interchangeable and it is important to understand the knowledge base both to ensure project success and proper staffing.

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2 thoughts on “Difference between Data Scientist Vs Data Analyst

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