More and more data is being collected and used by various companies and institutions, and the proper management of this data allows them to improve and even make more profit. This is why companies need data analysts and data scientists so badly. And while both professions deal with large amounts of data, an analyst is like a translator, while a data scientist is more like a programmer, using codes to manage data.
The rise of Big Data has been accompanied by the creation of many new jobs in technology and business. More and more data is being collected and used by various companies and institutions, and the proper management of this data allows them to improve their business and even help increase profit. This is why companies need data analysts and data scientists so badly. In this post, we will look at the differences between these two professions.
A data analyst processes all the useful information a company has gathered and helps to identify trends, find solutions, and plan future actions. Similar professions existed before the advent of big data, which makes it easier to define the profession. An analyst does not need to have programming skills or technical knowledge, but they do need to know how to use data analysis software and data management applications. It is also very important that the analyst has good commination skills, as he or she must be able to communicate the processed and purified information in a way that is particularly understandable to their colleagues.
Unlike a data analyst, A data scientist must have programming experience. This is the biggest difference between a data analyst. Data Scientists analyse and interpret data in a similar way to analysts, but they use code to create algorithms that allow them to analyse the company’s data more broadly and deeply. To give you an idea, consider the way Netflix or Facebook ads work. An algorithm ‘tracks’ what films or series you watch and suggests similar ones to you, in the same way, that it ‘tracks’ what you are interested in on Google and promotes them to you on social networks. Data scientists develop these and similar algorithms, which is why they need to be able to analyse statistics, know the software, data visualization and be able to code in languages like Python. There are not many of these versatile professionals, which makes data scientists a highly sought-after and well-paid profession.
We hope this post has given you a better idea of what you can do as a big data scientist and why these specialties are so important and in-demand these days.
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