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Evolution of the Data Scientist Through the Decade: What’s Changed


 
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Evolution is the truth of mankind and it’s inevitable. We all are evolutionizing everyday biologically as well as technologically and so do our roles and responsibilities. Here is the summary of evolution of Data Scientist role and it’s hiring trends in industry throughout the decade.



Data Scientist Hiring Trends 2011

R emerged as a major challenger to SAS at this time. Since it is an open source tool, it was a very attractive option for everyone. It was around this time that the term ‘big data’ started being used for massive data sets and non-relational databases. Hadoop and related technologies started to emerge but the focus was still very much on analytics skills.

This is also the time when Data Scientists started to differentiate themselves from Analysts. However, it was all still very confusing. Everyone in analytics wanted to be called data scientist now and we were still some time away from a clear demarcation of roles.

And Now in 2016

It has taken a long time but finally we are seeing a clearer demarcation of roles within analytics. Generalists are giving way to specialists. In my earlier article, I have spoken about the new roles and designations emerging in analytics.

Today’s data scientists have a choice. They can specialize in data science or machine learning or data visualization or big data. Of course, most companies still prefer to hire people who have a mix of these skills. For example, data scientists who know machine learning or are experts in data visualization are preferred to data scientists who just have statistical modeling skills.

People with strong domain knowledge and basic analytics skills are being classified as business analysts. People with an expertise in dealing with unstructured data are being called big data specialists. And so on.

All these roles are fairly new and still evolving. As the industry matures, so will acceptance of these classifications. On the way, we may see some changes as well – some paths may merge, while others emerge and so on. This is a constant battle for those of us in the analytics learning space– to adapt and map a constantly evolving industry scenario to specific roles and skills. So the need of the hour is specialized learning paths adapted for ever-evolving roles in data science, Big Data, machine learning and data visualization.

Data Scientist Hiring Trends 2016

What Does the Future Look Like?

As a data scientist, I love to predict. Well, here are my predictions for 2021.

  • Machine learning and deep learning will become much more popular. More data and better processing power will enable a lot more analysis of different data. Those who develop these skills will be in demand.
  • There will be a lot more data generated through internet of things. This data will be bigger and messier. Data scientists who develop skills to work with IoT data will have an advantage.
  • Specialized roles will continue to evolve. Specializations will become more logical and some of the confusion around them today will disappear in the next 5 years.
  • Analytics will play an important role in hiring for analytics (and all other roles). We are already seeing evidence of this and I think data driven hiring is coming very soon.

What do you think of the predictions? What’s in store for data scientists? What will the future data scientist role look like? I would love to hear your opinions on this.

Original post. Reposted with permission.

Bio: Gaurav Vohra is CEO & Co-founder of Jigsaw Academy which aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training and education to develop business-ready professionals.

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