Competence in Mathematics, Statistics, Programming, and Data-Visualisation, will be increasingly demanded.
It is like a language unto itself. As people who speak it rapidly increases, and eventually rise to senior decision-making positions, they will only want to hire those who speak the same language.
Companies whose senior leaders cannot speak Data Science, will quickly lose to companies whose leaders are well-versed in sound data-driven decision-making.
Blue-collar and white-collar workers are roughly differentiated by their reading and writing ability. Blue-collar workers often produce far higher value than white-collar glorified pen pushers, but because high-level decision-making and communication is done via English (or whatever lingua franca), those who are less-skilled in English will tend to be lower on the organisational hierarchy.
Similarly, we are witnessing the emergence of a new class of workers who are differentiated by their ability to read (interpret the results of) and write (utilise) mathematical, statistical, and computational methods. Those who speak Data Science will tend to rise to the upper echelons.
Furthermore, expertise in maths, stats, and programming, are easy to assess, and difficult to fake. This means that managers who have gotten by (or even risen to the top) via smooth talk, will find it increasingly impossible to continue their charade. Case study: When Elon Musk realised China’s richest man is an idiot ( Jack Ma )
Data Science cannot possibly be a bubble. It is a superior decision-making language / medium whose importance will only skyrocket as more people learn to use it effectively.
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Actually, a sort of bubble does exist: the multitude who foolhardily rote-learns “data science in 3 months!!!” without developing true understanding of the underlying statistics, maths, and coding skills, will find themselves utterly ineffective even if they can produce a data science portfolio via “monkey see, monkey do.” Recruiters will simply toss them an aptitude test assessing general maths / stats / coding ability, and such people will quickly be filtered out. (Even those with full degrees will not be spared, if they thought they could get away with skimping on the maths / stats.)
Cf. https://towardsdatascience.com/sorry-projects-dont-get-you-jobs-3e5d8e74bfdc
)
Cf. https://priceonomics.com/survey-which-data-skills-do-you-need-to-get-ahead/
“ It’s potentially challenging to get 80%+ of executives to agree on anything, but the survey reveals that business leaders think data skills are now a requirement for becoming a senior leader and those skills will only become more valuable over time.”
“ On each question regarding the value of data skills, Chinese business leaders scored highest on emphasizing its importance. Over 90% of respondents in China noted that they need data skills to get promoted and that data literacy was a requirement for entering senior management. Even for general office work, 88% of respondents in China noted that data skills were a requirement. If data skills are table stakes for competing in the global economy in the future, Chinese businesses are foremost in recognizing this education need.”
“ While data science jobs get a lot of publicity as a “hot career,” the reality is data science is not the only data-related career with high growth and demand. Dataquest, a data learning company, notes that jobs as diverse as operations analyst to digital marketing manager all require some level of advanced data skills, are highly demanded by employers, and command high salaries.”
A comment on Hacker News (https://news.ycombinator.com/item?id=20016181)
“In many companies that I get to interact with (fortune 100s) run-of-the mill analysts exist for ass-covering the non-data-based decisions of higher ups. Post-hoc decision justifying. But DS teams (currently) actually get a seat at the table and get listened to. If for nothing else that makes DS way more effective then just BI or whatever. And as long as we get things right that should turn into a virtuous cycle of being heard, being seen to contribute, and being asked to contribute more.”
Hiring managers looking to do real data science, will surely test potential hires for maths / stats / computing ability.
Meanwhile, people who are only good at churning out “projects” via mimicry (with little understanding of the underlying theory), are destined to do fake “data science” related to post-hoc decision justifying (i.e. “decision-driven data-making”). Those (non-technical) hiring managers will _not_ want people with real analytical / stats skills. They just want you to tell cool stories with pretty pictures. This is the fate of “data scientist” aspirants who skimp on the maths / stats / computing fundamentals.