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The Wait is Over: Nexis Data Lab for Academic Launch Takes Universities' Data Science Enablement to New Heights

March 18, 2021

The world is being inundated with data. No wonder one of the fastest growing area of study at today’s universities is data science. In fact, says DiscoverDataScience.org, IBM’s Analytics Department projects that “In 2021, job openings for data scientists and similar advanced analytical roles will reach 61,799. This is a significant number, but it represents just 2% of the projected demand across all job roles requiring data and analytics skill.”

As universities across the country look to begin or expand Data Science programs, there are challenges to overcome—from adequate preparation for students entering the programs to the content these programs should cover. According to a recent article published in the Journal of Statistics, technology and data access can be serious hurdles. “Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years” found from a survey of faculty involved  in Data Science education that technology concerns ranged from “…local IT issues to teaching coding.” In addition, the article notes, “Instructors were interested in online resources with exploration exercises, notebooks for coding, classroom activities, and engaging and relevant databases for use in the classroom.” Now, Nexis® Data Lab for Academic is here to help universities get over those particular hurdles.

Hands-on solution for data discovery & experimentation

Nexis Data Lab features our extensive database of current and archival content—enhanced through our industry-leading data fabrication and normalization process—to enable fast search and results refinement across thousands of full-text documents for data mining, algorithm testing, and advanced analytics.

When combined with the best-in-class Jupyter Notebook environment, this combination of data and technologies gives students and faculty a powerful tool that enables exactly what DiscoverDataScence.org says is needed. The article notes, “Finding opportunities to contribute to real-world data projects early will help build an in-depth understanding, as well as develop a better sense of the technologies, techniques, and analysis tools required for a career in data science.”

Watch the full interview with Todd Larsen to learn about these benefits and more: