In the past couple of years, the field of data science seems to have rapidly shot to fame. The main reason for this is ‘data’. In our information-driven world, all of us play quite a crucial role. Since the moment we wake up and glance at our phones, to the last moment of the day, we are all digital labourers, trying to generate the very data that acts as a fodder material for the companies over at Silicon Valley.
All of these high tech companies today are all for the increasing demands of data scientists and data analysts. Jobs in this sphere have been steadily increasing and have taken up permanent residence at the top job search engines all over the web. The various titles that beckon data aspirants are the likes of Data Scientists, Data Analysts, Data Engineers and many others. While the prefix of all of these job titles may lead you to believe that all of these professionals carry out the same functions, it is not really so. As data science happens to be a vast field with so many diverse verticals and untapped areas, there is always something new to do with someone new.
Coming back to how these similar sounding positions are actually quite different. Let us start with Data Scientists, these professionals are popularly known as the rock stars of the Information Technology industry. They are usually in charge of making accurate predictions, which help the businesses take the most lucrative decisions. These individuals have a treasure trove of educational qualifications and experience.
They usually belong to a background of computer science applications, modelling, statistics or math. They have an ‘IT’ factor in the form of a combination of brilliant business skills and excellent communication skills that set them apart from the general public involved in the industry. Further division of roles for a Data Scientist could be becoming a Data Researcher, Data Developer, Data Creatives and even Data Businessmen.
Apart from Data Scientists, there is another career option which is called as Data Analyst. These professionals perform a wider spectrum of functions like collecting, organizing and analysing of data in order to derive important information from the same. They are also known as Data Visualizers as they are supposed to present this collected and processed data in the form of charts, graphs and tables and go on to build other related databases for their firms. They could diversify their careers by going into roles like Data Architect, Database Administrators, Analytics Engineers, and Operations and so on.
The major differences between these two positions are that a Data Scientist usually is required to be familiar with database systems like MySQL as well as Java, Python and so on. Whereas a data analyst must be familiar with other data warehousing and business intelligence concepts and must have an in-depth knowledge of SQL and analytics.
If we put the differences between the two aside, then we would infer that both the positions require a professional to do a thorough course in programming tools like Python, Big Data Hadoop, SAS Programming, and R Programming and so on. While these tools could be learnt through self-study, but most prefer institutes like Imarticus Learning to help them along their journey.