The Various Facets of The Digital University for Data Science

The demand for data scientists is booming and will only increase especially as data-driven decision-making becomes organizational mainstays, and new-age technologies and solutions reach maturity.

Data is indeed the new soil…a fertile ground that is non-rivalrous, non-depleting, regenerative, and almost unlimited and holds the promise of great outcomes.

As the importance of data grows in the organizational narrative, so does the role of the data scientists. Enough has been said about the job of the data scientist being the sexiest job of the 21st century. The requirement for data scientists and analysts is expected to grow from 364,000 openings to 2,720,000.

Demand is outrunning supply in the data scientist universe.

It is hardly a wonder that a career in data science is becoming increasingly lucrative.

To be employable as a data scientist, apart from the theoretical knowledge, one needs to build a strong portfolio of projects, showcase some experience in solving real-world problems, and demonstrate working knowledge.

With the COVID-19 pandemic, online learning has taken over classroom training. Digital universities are the future of learning. For emerging fields like data science, the training programs need to be innovatively designed.

Educational institutes aiming to offer data science universities need to consider the following aspects in their data science digital universities –

Course Content and Tools Infrastructure

The course content has to be of primary focus and should bridge the gap between academics and industry by having a solution-based approach.

The end goal of the data science course should be to increase the technical vocabulary and also to help the data scientists develop their skills as solution-providers. Apart from the academic nuts and bolts, the data science course should be based on a design thinking model to help the future data scientists develop solutions ideas with a strong foundation of design.

It is, therefore, extremely critical to provide access to the right data science tools and platforms. The course should provide the students access to an end-to-end Data Science Platform with Artificial Intelligence (AI) and Machine Learning (ML), Data Visualizations, and Data Apps.

The tools ecosystems should be exhaustive and help future data scientists become better creative thinkers, improve storytelling, and enable faster innovation and value creation.

Learning and certification paths should also cover different themes that range from machine learning, predictive analytics, robotics, cybersecurity, blockchain, IoT, Cloud, and SaaS and should allow the learners to work on real-world cases to drive better learning.

Expert Assistance and Application-Driven Learning Approach

Data science courses also have to be designed for working professionals and allow self-paced learning. Such courses should have a scalable design to accommodate the shifting needs of the students, organizations, and industries.

It is important to provide access to a robust mentor network that is a coalition of business leaders across industries and government bodies and other institutions. These mentors can share their expertise with the students for their authentic development.

Along with this, it is essential to offer practical experience of working on real-world data science cases under the guidance of industry experts and data scientists themselves. Providing access to experts helps learners understand how to take academic information and translate it into practical knowledge for solving real-world challenges.

Doing this empowers the students by making them more application-driven in their learning approach and helps them extend their learning to make it more impactful and deliver measurable value.

Multidimensional Program Structure

The digital university should also offer Data Science-AI-ML Accelerator and should facilitate interactions between the students, professionals, and mentors for cross-pollination of ideas and information exchange. The course structure and platform should adopt a more democratic approach and should allow the users to employ their findings and ideas for research projects, prototyping projects as well as building MVPs.

A digital university offering a marketplace to sell such solutions is an added bonus.

Along with academic knowledge, data science courses should also focus on driving innovation challenges to enable beyond-the-classroom opportunities. These could span across a range of subjects and topics to allow students, academicians, and others to express their entrepreneurial spirit. Having an incubation system in place to enable the same becomes essential, especially when we want students to pursue entrepreneurship ideas.

RubiVersity, the educational arm of Rubiscape, offers various facets of a digital university under one umbrella. By leveraging it, corporates and academic institutions drive innovation and create more entrepreneurs and data scientists.

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