AI is changing the skills needed of tomorrow's data scientists

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There’s a global shortage of data scientists and it’s going to get worse before it’s going to get better.

As the demand for data scientists increases, the supply of talent in the US cannot keep up. According to the McKinsey Global Institute, the U.S. economy could short as many as 250,000 data scientists by 2024. Countries like Malaysia are building national programs to fill the gap as they seek to become a global hub of data science talent. The United States needs to apply more resources in order to grow the number of US-trained data scientists and maintain a global leadership position in data science.

As data becomes more ubiquitous in every job role and in every organization, data scientists will only grow in importance. Today’s students will be the first data-native employees of the future, and it’s critical that they understand data science, how data science is changing and how data science solves real world problems.

The potential for the next wave a data scientists is huge but for them to be successful, education must evolve.

Learning to work with machines

Automation and artificial intelligence (AI) are already changing workforce requirements of data scientists. Traditionally, working with data has been complex and highly manual work, requiring specialized technical skills. Today, we’re in the beginning stages of applying machine learning and artificial intelligence to the operation of complex IT systems. We’re seeing how algorithms can improve and streamline security, network management and now data platforms.

AI is changing the nuts and bolts of data management, alleviating data teams from a lot of tedious, manual dirty work so that they can focus their time on creating business outcomes and allowing data scientist to work at a speed and scale that is impossible today. The data scientist of tomorrow must be prepared to work with the AI revolution, optimizing processes without losing the human ability to think creatively and apply data-driven insights to real-world problems.

Learning to apply data to business

The next generation of data scientists will be even more necessary for helping to apply models and algorithms to problems and processes across the enterprise. For data science students, it’s not only crucial to understand the data and the technology but it’s equally as valuable to learn how to function in teams, collaborate and teach. That requires a broader understanding of business.

Data scientists will need to be able to think like MBAs and MBAs will need to think like data scientists. That requires an interdisciplinary approach to education.

Communication is key

Future data scientists will not only be doing development work but collaborating broadly across many groups. Along with understanding the different functions of the business, they will also need to be effective communicators. The data scientists of tomorrow will need to be able to speak the language of engineering, marketing, sales and leadership in order to ensure that the data is improving every part of the business. Insights that fall on deaf ears won’t move the business forward.

The data science learning never stops

To be successful, data scientists need a mix of soft social skills and hard technical skills. Soft skills include curiosity, creativity, problem solving, communication and collaboration. Great data scientists iterate quickly, looking at problems from a variety of angles to find the best approach to creating insights and answering questions. That takes curiosity and an understanding of the innovations that are shaping the world.

And that means that the learning can’t stop.

Enterprises will be need to invest in their data scientist, ensuring that the learning continues and the well of creativity never runs dry. Whether that means dedicating internal resources to education or empowering data scientists to attend conferences and continued education courses, the data scientists of tomorrow must be constantly learning and evolving.

Data for all

As enterprises become data-driven, every employee will need to understand how and where data analytics can be applied to their work. The future of data science education is rapidly evolving. Long gone are the days of data science being a specialized track for only a small portion of students. In fact, everyone entering the workforce should have some type of data analytics skills. Universities and enterprises need to make data analytics skills mandatory.

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