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The Four Things Each Data Scientist Needs

What are the four key elements that you need to succeed?

19Jan

The fact that there is a gap in the number of data scientists and the number of people who have job requiring them, is almost undisputed. There has been such a clamor for them in the past two years, that they can almost ask for any amount of money they want.

Therefore, it is a career that many are looking at admirably, but it is not for everybody.

So what does a data scientist need to know before they start?

We believe that there are four skills that are an absolute necessity for people to have in order to become a data scientist.

Maths Skills

Much of the data systems that are used today, apply automatic algorithms to datasets. This means that manual analysis is not always necessary, they often even slow down the process and increase that chance of human error.

Maths skills are vital in the role because they guide the Data Scientist in the correct algorithm to use and how to correctly interpret results. It allows a thorough knowledge of the workings behind the technology, which is the most important aspect of any data analysis.

Business Knowledge

Having the ability to create the analysis is one thing, but Data Scientists need to have a certain level of autonomy in order to work effectively. This requires the identification of areas of the business that could be made more effective.

This is done not only through discussions with others within the company, but also through their existing knowledge of the business.

It is also useful for unexpected findings and trends.

If a data scientist finds an unexpected link or trend, it is important that they can contextualize it in the wider company. This means that despite working within data and having the technical ability to work through algorithms, they can step back and look at the bigger picture across the entire company.

Experience

This is undoubtedly the most important aspect of being a successful Data Scientist.

Many have the practical maths experience from a maths or statistics degree, but if they are not comfortable with the software and hardware that they need in order to complete their role effectively, then the likelihood is that they will fail.

This ability to understand the systems and technologies does not come through being sat in lecture halls, but instead through using these technologies consistently. It will not coming through knowing how to use them, but through becoming comfortable in their use.

Knowing how to use multiple software systems is in many ways, more important than have a relevant degree. If you look at some of the most important players in Data Science, many do not come from a degree directly associated with Big Data. In fact many have attributed their success, not to their knowledge of maths, but to their knowledge of how to effectively use a system.

Ability To Learn

Big Data is a relatively new concept. It is also one of the fastest moving landscapes in the world, with new developments and technologies appearing almost weekly.

The fact is that one of the main data analysis systems is Hadoop, which is open source and constantly evolving. That many companies have their Big Data initiatives with a constantly shifting foundation means that all Data Scientist need to do one thing: Learn.

Not only this, but they must have the capability to learn quickly. The job of data is to give insight and therefore a slight advantage over competitors, if another Data Science team can get better results because they have learnt a new technology, whilst you have taken time to learn, the other team/company will take the advantage. Learning to deal with change and learning from it is key to success.

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