Data Scientists have the sexiest job in the world according to Harvard Business Review who made the claim in 2012.
We have seen a significant shortage of well qualified data scientists, which has meant that those within the industry are demanding salaries that many could only dream of.
With this in mind, there is a clamour to become a data scientist from those who want to have these big salaries and work for the best companies.
So how do you become a data scientist? What does it take and how do you start?
The road to being a data scientist, is relatively simple in terms of qualifications. Several universities are offering courses with data science skills the core component. These vary in what is required to enter the courses, but as a rule of thumb, a good maths qualification, some coding skills and an interest in statistics are the minimum requirements to get on these courses.
From there, many companies are looking for data scientists at the moment and moving in to a junior position on a team would be relatively easy.
But at this point, when you are a data scientist, how do you become the best? We believe there are some key traits that the best data scientists need to have:
The first thing that is needed is an inquisitive mindset. This will allow people to not only find hidden trends and patterns, but also mean that you are more likely to experiment outside of the confines of a degree or course.
The next aspect is agility and flexibility. Although this is not a vital aspect to become a data scientist initially, in order to become the best or just keep up, you need to be able to adapt to new scenarios.
New kinds of data is being mined all the time and new technologies are being created to speed up and improve data processing. This means that if you are not staying abreast of all the latest developments within the data science space, you will be left behind. One of the ways that people are staying updated on these developments is through helping in the development of these new technologies. Looking at open source software like Hadoop, becoming involved in the development of this has significant benefits to not only developing a knowledge of new products, but also in communicating changes to others in the company.
As well as being agile and flexible, it is important for data scientists to be patient and have the ability to wait before trying to identify trends and correlations. Often the best results come from larger datasets, which take time to create. This means that there needs to be patience and conclusions should not be jumped to on incomplete datasets.
This is especially important if you are starting a new data programme at a company. It will take time to gain buy-in from certain elements of any company, it is important that data scientists are patient with them and not reactive. It requires a certain degree of democracy, but will ultimately results will bring those who may be against data programmes around.
This seems obvious, but goes well beyond the analytical aspects that are required as the base needs for a data scientist.
There needs to be constant analysis of how things could be done better, how data could be better stored and how the company could perform better with it. It is beyond looking through customer or sensor data to identify trends and instead look at aspects of the company that may be affected by data and how these could be improved.
Having a perspective wider than just the dataset you have in front of you is vital to become successful as a data scientist. The ability to take what you know and apply it to business problems, can only be done if there is a perspective of what the business wants and needs.
This could be anything from noticing that more people from Canada are looking at a certain product and therefore focussing advertising there, or that more people are clicking on a certain coloured button. These kind of findings are not useful in isolation and may well be missed by other departments, therefore perspective of how the company as a whole could utilize your findings is vital to success.