Online Education is Paving a Smoother Path to Earning Data Science Skills

Flexible and affordable offerings help to bridge skills gap

Many organizations now have more data available to them than they know what to do with. Turning this avalanche of data into meaningful business insights creates challenges that require data science skills. The need for professionals skilled in data science, analytics, and machine learning is skyrocketing. We’re seeing this across industries and sectors. A recent report from IBM states that there will be over 300,000 new job openings for data driven professionals by 2020. As it stands now, there is a shortage of the necessary technical skills for these roles.

Specifically, professionals should focus on how to turn data into decisions. We’ve learned to build the infrastructure that can store and process massive amounts of data, but we still lack the critical ability to convert that data into meaningful information. This is one of the defining challenges of our time: to use data to make accurate predictions that can lead to high-impact decisions. Tackling this challenge means developing the ability to perform data processing and computation at a massive scale. This requires a time investment in ongoing education, preferably through multidisciplinary programs that include elements from engineering, mathematical sciences, and the social sciences. MOOCS (Massive Open Online Courses) and online programs are able to mend this gap by creating a path to a traditional academic degree.

Some universities are adapting to this data revolution by providing more on-campus data science courses, however,  these are relatively new offerings that were not available to most while they were in school. Many professionals need rigorous training in these skills now, and want them without having to leave their day job. They need the practicality and flexibility that online learning provides, so the operative question becomes: can MOOCS deliver these skills without compromising quality?

The key to developing the right data skill set, however, isn’t in knowing any one technology, model, or practice. Professionals should be well-versed in a variety of tools, perspectives, and approaches so they can identify which methods and models are most appropriate for their own organization’s unique use cases. For example, having a course in machine learning is particularly important as machine-learning approaches allow automatic data extraction. Machine learning is becoming increasingly critical in delivering insights to the right decision makers at the right time.

As the future of this industry unfolds, two things are clear: many more data scientists will be needed to generate meaning from this abundance of information, and online education will be critical to training a workforce capable of turning data into decisions. As the field evolves and real-time data allows more intricate predictions and computations at larger scale, new paradigms will undoubtedly emerge. Data will continue to drive key business decisions, and a new insight market will likely take shape.

About the Author

Devavrat Shah is an MIT Professor of Computer Science and Director of the MIT Statistics and Data Science Center with Scott Murray the Communications Officer for the MIT Institute for Data, Systems, and Society.
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