I recently began taking courses to obtain a Digital Transformation certificate from Stanford University's School of Engineering. I highly recommend it since it flexes your critical thinking and strategic acumen; a few weeks ago, I completed the Building an AI-Enabled Organization course, which showcases how data science and artificial intelligence (AI) can be applied within the context of the business and corporate world. I thought I completed my training when I obtained a master's in data analytics and business administration.
A couple of items that stuck out to me from the course:
- Data Drifts & Model Drifts - Data drifts are when the statistical distribution of the data changes over time. For instance, electric cars didn't exist before the 2000s. The percentage of gasoline-powered cars reduced after the 2000s. Model drifts, aka concept drifts, are learning models that are outdated since they were developed using the pre-data drift model.
- Unicorns—Steven Geringer's Data Science Venn Diagram v2.0 showcases the rare breed of knowledge worker called unicorns.
Steven Geringer's Data Science Venn Diagram v2.0 (Mokrian, n.d.)
These unicorns are rare since these data scientists have insights into the subject matter expertise, computer science and a background in math and science. These unicorns reminded me of professionals who work with bioinformatics, nursing informatics, aviation informatics, and other informatics. According to Wood (2023), the subject of informatics is to design, develop and maintain systems that capture, store, and manage domain-specific data. Wood (2023) also states that data science is to get insights into the data and develop predictive analytics to enable key staff to make decisions. Even though informatics and data sciences are distinct disciplines, they depend on each other to help any key organization or program. For instance, data scientists can only gain insights if the data quality is good. Data quality is an outcome of how systems capture and store the data. If organizations want their data scientists to leverage multiple data sources to gain insights, then data management is key. In other words, data scientists are dependent on informatics specialists since their work is important.
Suppose you or your loved ones want to be the next Unicorn or be part of the Unicorn team. In that case, I recommend that you pursue an informatics degree in a specific domain and take a handful of data analytics and data science courses. The informatics degree will train you in a specific subject matter and expose you to computer programming. Here is a list of the colleges and universities and the types of informatics degrees they offer:
- Applied Informatics - University of Technology, Russia;
- Business Informatics - Modul University, Austria;
- Cybersecurity Informatics - San Jose State University;
- Health Informatics - George Mason University;
- Informatics for Logistics - College of Logistics, Czech Republic;
- Integrative Informatics - Allegheny College;
- Maritime Logistics Informatics - University of Tasmania, Australia;
- Social Informatics - HSE University, Russia;
- Urban Informatics - Northeastern University; and
- Others.
For domains, like sports, that already use data science, colleges offer specialized degrees like:
- Crime Analytics - Toronto Metropolitan University, Canada;
- Nutrition and Health Analytics - Munster Technological University, Ireland;
- Sports Analytics - American University; and
- Others
I predict colleges and universities will offer more domain-specific analytics degrees in a matter of years. With more domain-specific data being generated by various industries, data scientists will tighten up the algorithms to be specific to each domain for better insights and more focused decision-makers. I don't think there will be a slowdown in folks getting informatics degrees since industries still need human smarts and brains to design how data should be captured, stored, and processed. With the demand for platform technologies like Salesforce, Appian, and others, as well as platform businesses like Fiverr, Etsy, LinkedIn, and others, folks with data science and computer science degrees will have jobs. Suppose you have these skills and expertise in niche domains like maritime, aviation, urban, nutrition, and others. In that case, you have a very bright future that can be translated into more money for the next several years. I also envision platforms that support specific domains will be created and may thrive. I say "may thrive" because the folks who run these platform businesses need savvy executives who have a feel for the markets and adopt agile business strategies. The future is exciting in this space for unicorns, entrepreneurs, and disrupters. Generative AI technologies will complement this space, but I don't see them ever replacing the unicorns since Generative AI is nothing more than a chatbot that overlays the data science engines and the unicorns who work with them.
In summary, if you or your loved ones want to make a living as unicorns, there are avenues to pursue, and I see an exciting future for the next five to ten years or even longer.
As always, please comment if you agree or disagree with my entry and subscribe to this blog.
References
Morkian, P. (n.d.). Building an AI-Enabled Organization. Stanford Online University.
Wood, T. (2023, October 10). Healthcare Data Science vs. Healthcare Informatics (and why the difference matters). Fast Data Science. https://bit.ly/4hbH0u3
