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A person working as a data analyst or as a data scientist work with data but the difference lies at the heart of what they do with the data. A data analyst typically examines large datasets and then identifies trends, further develops them into charts and then creates visual presentations for businesses to be able to make strategic decisions. A data scientist on the other hand helps to construct and design new types of processes for data modelling and production using algorithms, prototypes, custom analysis and predictive models. There are some other differences that significantly differ for both which we will explore in this article.

We have strived to list down the differences between the two so that it helps you understand it better and see which side of the spectrum you fall in.

• Typical Background – for a data analyst or a business analyst, a background in the field of statistics and mathematics is important. In case a background in quantitative is not there then they need to know the tools that are needed to make decisions with numbers. For a data science expert, it is important to have hacking skills and substantive expertise along with the basic mathematics and statistical knowledge that must be present.

• Skills and tools – for a person who is going to analyse data, some of the important skills and tools that are needed are data warehouse or data mining, data modelling, SAS or R, statistical analysis, SQL, data analysis and database management and reporting. For a data scientist, it is important that they must know software development, machine learning, java, Hadoop, data warehouse or data mining, python, data analysis and object-oriented programming.

• Roles and responsibilities – for someone who is going to be a data analyst, then the roles and responsibilities that come along with it are being able to maintain and design various databases and data systems, use various statistical tools to interpret various data sets, and prepare reports that effectively and efficiently communicate trends, predictions and patterns that are based on relevant findings. For someone who is a data scientist, some of the roles and responsibilities include designing data modelling processes and as well as creative predictive models and algorithms to help extract information that is needed by the organization to solve complex business problems

• Educational background – for a person interested in becoming a data analyst, an under-graduation degree in engineering, science, technology or math is recommended. An advanced degree in either is also recommended. Apart from that, experience in science, math, programming, predictive analysis and modelling is recommended. For a data scientist on the other hand, along with machine learning and data mining, a master’s or a PhD in similar fields is recommended.

Apart from the above which spell out the basic differences between the two, it is important to make a list of what are your interest areas and how well they align with either of the career options. After that make a list of the companies that you want to work for and the kind of work, they are doing in both the field. Once that has been done look up people who worked as either a data scientist or a data analysist and see what is their career growth along with the kind of salary that is offered for each role. Then try to align them with the plans that you have laid out for the way you want your career to grow and advance and then make an informed decision. It is best to never rush into anything without doing proper research.