A recent documentary on TV amused me. It asked teens what they want to do professionally. The answers included YouTube star, DJ and – over and over again – “influencer.” Quite apart from the fact that all these are relatively new jobs, they also have something else in common: There is no training available. It is not even clear if they need any particular skills, though I suspect not.
Is that sustainable? I doubt it. Professions like baker have been around for centuries, but these new “professions” could soon be done by artificial intelligence. It might even be much more efficient. Well, it is the zeitgeist, I suppose.
While this is entertaining, it also highlights a more important question: How can you prepare for the job market today? What do you write in your curriculum vitae to stand out from the crowd and be invited to a job interview? In my day, we had to know how to use Word and PowerPoint, but what do today’s jobseekers need?
The answer is data science
Anyone who follows my blogs will be able to guess what is coming next. The answer is data science. Why? The keyword is digitisation. In business, self-service is becoming more prevalent, enabled by new applications and services that force (sorry, empower) employees to do everything themselves, without outside help. We have long since moved on from the days when support was available to update databases and prepare reports, never mind book business trips, arrange meetings, generate invoices, etc. Now employees do everything themselves, and that includes being able to analyse data to support their work and transform it into information rapidly and effectively.
Data science training is not moving fast enough
The recent Higher Education Report 2020, published by the Stifterverband in Germany in collaboration with McKinsey, recommends eight education policy goals. It also contains a number of familiar themes: problems with STEM subjects; a shortage of practical knowledge; and the fact that women who are successful in their academic careers remain disadvantaged in the world of work. There is, however, also recognition of a new issue: Data science training is not moving fast enough.
Measured against pioneers such as the US, German universities have moved slowly in the field of big data. At Columbia University in New York City, for example, the interdisciplinary Data Science Institute was established back in 2012, providing all students at the university with basic data skills. By contrast, in Germany at the beginning of 2017, there were only 23 study programs with any explicit specialisation in big data and advanced analytics. I started to get a feeling of déjà vu as I read recommendations like:
- Establish data science education programs for bachelor's degree programs at universities that provide basic data analysis skills for all subjects and that all students should attend.
- Targeted cooperation between universities and companies in the provision of data analysis competences, for example, through so-called hackathons, or collaborative software and hardware development events.
I could almost have written it myself!
Things we can do to help ourselves
The issue, however, is more than just creating new job descriptions and defining data scientists. Data has become central to our everyday lives, and we need to recognise this in developing competencies for the future. Everything is becoming more and more digital (or “smart”), and we all need to know how to manage devices and data.
Governments and educational institutions need to work together on this. Just as PowerPoint became standard in education, so should data analysis. I don’t mean training in highly complex deep-learning artificial neuronal network ANOVA regression-mining special algorithms, or anything like that, but simply application-oriented everyday mathematics. That would be a start.
But even if this recommendation is not adopted, there are things that we can all do to help ourselves. Statistics, analytics and maths have to get away from their dry, dusty image and show that they are not only for nerds, but for everyone. Something has to change. Watch this space for more.
How about starting immediately? Getting into data science is quite easy, believe me! A good occasion is the SAS #UEChallenge: Just work on a Netflix data set, submit your answers and win great prizes! Curious? Enter here.
P.S. By the way, successful influencers check their social media influence score all the time. They analyse their statistics to see what works, how they can drive the score up, which times and topics are most successful, and so on. Data science is looking much more exciting now, isn’t it?
