10 Signs Of A Bad Data Scientist
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.
By Seamus Breslin, Solas Consulting.
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.
Not a team player
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.
Poor mathematical background
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.
Poor computing knowledge
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.
Poor communication skills
A data scientist has to bring clarity and insight to data and regardless of whether they can memorise algorithms or key formulas if they cannot communicate their findings or their ideas, then they will not succeed as a data scientist. A data scientist must be approachable and aid the performance of an organisation with good communication.
No business knowledge
A data scientist must have a knowledge of the world of business and know what problems your business has and the problems your company is trying to solve. If they fail to understand business issues then how can they solve the problem?
Lack of knowledge about tools
When it comes to data science, there is an arsenal of tools that can be used to collate, analyse and present information. From Scala and Python to SAS and Matlab. A data scientist must have knowledge of most of these tools. If not, they are not a great fit for your business.
SAS only knowledge
Similar to the point above, some “data scientists” have a knowledge of coding and thus have rebranded themselves to be a data scientist. However, if they only know about code, this does not mean that they know how to read or analyse data.
Don’t want to get their hands dirty
If a data scientist is unwilling to take risks, analyse data and dig into the code, then they will simply not fit into any organisation. Being a data scientist takes risk and a hard-working ethos.
A know it all
Nothing is ever the answer when analysing data until the data proves or matches the relevant theory. If a data scientist is convinced that they have the right answers all the time, then they will never be able to see out of their own prism, thus, they will never be able to adequately review figures.
Lacking a natural sense of curiosity
Most data scientists need to find the answers and wish to find out the trends and data behind the figures if a data scientist is not curious or is unmotivated to find out what makes things tick, this is exceptionally bad practice.
Bio: Seamus Breslin is the Founder and Managing Director of Solas Consulting, and has over 11 years experience in the IT sector. Solas specialises in placing Data, BI , SQL , Oracle , Java and .Net professionals.
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