With the number of people claiming to be a data scientist growing, the “true” data scientists are becoming hard to find. Here your guide identify the clues to catch a bad data scientists.
The importance of data in modern day business cannot be questioned with many companies realising the importance of data and analytics to relate to their customers and get the most out of their business.
With added emphasis on the use of data by many companies, from supermarkets to multinational corporations, new roles are developing for data scientists to analyse and investigate what lies behind the figures and what trends can be shown through the collection of data.
While the role may seem relatively straightforward, it can be exceptionally complex and there are key data science skills that are required to excel in the role. While every company will have a different value for particular skills and tools, we take a look at what makes a bad data scientist and what skills are essential to the role.
Data scientists must analyse information and look at what lies beneath the surface. Often, this can involve looking at large quantities of information and working as a unit. If a data scientist cannot function in a team or wants all of the glory, then they are not going to work well with others and produce the best results.
Mathematics is one of the key tools in analysing data. Therefore, it is important that a data scientist has a strong mathematical knowledge and can learn algorithms and other key tools quickly. Having a passion for maths will lead to a higher quality of work.
To succeed as a data scientist, it is important to have strong computer skills to calculate and present information. Not everything is analysed or presented on paper; thus, a strong digital background is key. If a data scientist doesn’t have knowledge of some of the key platforms, such as Spark, then chances are, they’re a bad one.
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