In view of the continuous skills shortage, industry and universities are working on programs to produce a consistent pipeline of data science and AI/ML workers. As part of their efforts, the students immerse themselves in data science staples like Python and R languages to create data models and build scripts. Students will get the opportunity to embrace the data science role and apply theoretical principles to real-world business problems during their internship.

It’s no surprise that data science jobs are in high demand. Organizations are trying to leverage data and advanced technologies like artificial intelligence and machine learning for the betterment of customers, to reduce cost, and optimize the operation. 

Based on its monitoring of numerous job posting sites, the report predicts a shortage of 250,000 data scientists by 2020. According to the State of the CIO study for 2021, data science and analytics capabilities were at the top of the list for 31% of CIOs, while 20% predicted problems sourcing AI and machine learning specialists and 19% expected difficulty locating and hiring analytics and data science experts.

Freshly minted graduates and undergraduates aren’t a universal cure for filling up the senior level, they play a perfect role for entry-level posts and for cultivating and growing employees into more advanced and analytical roles industry and universities are priming this pipeline for numerous reasons.

One of that, corporate enterprise ensures it emphasizes what’s important to business as opposed to pure theoretical book learning. Universities get exposure to real-world business problems that provide practical context. Hundreds of businesses are experimenting with industry-university partnerships to fill critical skill gaps, particularly in data science and cybersecurity. Microsoft, for example, is collaborating with universities such as Purdue University Global, the University of London, and Bellevue College to provide blended and flex-learning options based on Microsoft technical skills courses in areas like AI, data science, and big data. Intel’s Digital Readiness Program is providing community colleges with over 225 hours of AI-related curriculum in order to assist them in developing AI certifications or launching full AI associate programs.

In a slightly different twist, Infosys, a global consulting leader, has partnered with North Carolina State University for a three-year collaboration aimed at improving the education and skills development of its internal employees in foundational data science areas such as statistics, data visualization, machine learning, and Python programming. At least 150 new Infosys employees are expected to complete the six-week full-time program.

In a relatively short period of time, university-level data science programs have created a steady stream of job candidates. Organizations must be willing to spend the money necessary to hire the right employees on the ground and hire top executives. A wide-scale private-public relationship is required to build a talent pipeline large enough to satisfy future data science needs, in addition to any specific firm activities.

As a result, “university-industry cooperation is a solid start,” but we also need collaboration from government officials, organization’s, and other businesses to fill the talent gap.