The 2017 LinkedIn Emerging Jobs Report listed data science as one of the top industries for emerging roles in the United States. In fact, data science roles have seen a 6.5x growth over the last five years, and demand for data scientists is only expected to increase in 2018 and the future.

Here are 10 data science predictions for 2018:

  1. Increased Demand

As mentioned, the demand for data scientists continues to grow. IBM has predicted a 28% increase in demand for data scientists by 2020 and reports that Data Science and Analytics roles stay open for 45 days on average- which is five days longer than the market average.

  1. More Clearly Defined Skills

As the field continues to grow, there seems to be a level of ambiguity around what a data scientist actually is. In 2018, we’ll see “data science” become less of a buzzword. Instead, recruiters and hiring managers will be drilling deeper into the specific skills that data scientists must have.

These include the ability to build and test hypotheses, model-building skills, and machine learning knowledge.

  1. A Focus on Data Privacy

In 2018, the EU’s General Data Protection Regulation (GDPR) will go into effect. This will mean that companies around the world will have to prove that their data security is state-of-the-art.

If companies (including those outside of Europe) are collecting data belonging to EU citizens, they need to comply with new rules around data control and privacy rights. The GDPR imposes limits on consumer profiling and data processing. It also creates a “right to an explanation” for organisations using automation for decision making. Finally, it holds firms accountable for any discrimination or bias found in those automated decisions.

  1. Understanding of Machine Learning

While data scientist was number 2 on the LinkedIn list of emerging roles, Machine learning engineer was number 1. The best candidates will combine their knowledge of data science with software engineering. This means that successful software engineers wanting to expand their skillsets will be uniquely positioned to be hired for those high-paying, high-demand positions.

  1. Increased Specialisation

Due to the current demand, even the most general data scientist is likely to easily find a new role. But those who want to find the most impactful and fulfilling roles will specialise in specific areas within data science.

This could mean diving deep into a particular technology tool stack or methodology or focusing solely on a particular industry. Either way, data scientists who specialise will be in even greater demand and can pick and choose from a variety of roles.

  1. Automation

While the demand for data scientists is greater than ever before, 2018 will see the automation of data science in many different industries. While businesses once derived management insights from analytics reports and dashboards, we can now expect to see intelligent automation.

Both data prep and model prediction are likely to become increasingly automated. This is due to the shortage of data scientists and allows fewer data scientists to accomplish the work of many.

  1. Connected Networks

Businesses will need to be prepared for an increase in big data which will be generated by connected consumer devices. In 2017, IoT-connected devices outnumbered the world’s population. Companies that are prepared to identify new lines of business and better compete in this market or define their data architecture will be a step ahead of those that are not.

  1. Chief Data Officers

The most successful companies in the world will soon have CDOs reporting directly to the CEO. This will be essential for building a company-wide strategy to manage, leverage, and secure the massive amount of data produced each day.

In fact, Gartner has estimated that within a year, 90% of companies will have hired a CDO. And most of them will be upskilling and learning on the job.

  1. Better Use of Data Science

Forrester has predicted that by 2020, the companies using data effectively will be worth $1.2 trillion. This is an increase from 2015 when this was just $333 billion. However, getting to this place will require better governance and management, along with a focus on hiring the right people to make better use of the data they already have.

  1. Rethinking of Infrastructure

Companies will need to re-evaluate how their infrastructure is designed so they can successfully navigate the new changes. While the focus has so far been on converging and consolidating data, businesses will now be focused on onboarding and connecting.

This means capturing data and then building out the architecture and tools to appropriately connect this data.

What do you think of these data science predictions? Leave a comment below or get in touch with your thoughts.