How to fix friends and inference people
Medicinal articles to treat a bingo board of bad bosses and misinformed coworkers
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Let’s face it. If you work in data, you’ve met that coworker who makes you want to facepalm (or even facedesk?) on a daily basis. Whether it’s the pointy-haired boss who is surprised to learn that you can’t do data science without data, the statistical inference person whose pedantic nitpicking gets in everyone’s way, the become-a-data-scientist-in-3-months “hotshots” who have no idea that they have no idea, the science-obsessed leader who thinks numbers can’t lie, or the salesperson whose pitch overshoots science fiction to sound more like I’ve-never-even-heard-of-science fiction… you’ve likely been tempted to scream into a pillow.
Frustrated souls, my first newsletter is for you. Here’s my cunning plan: I offer you a set of 30 blood-pressure-raising symptoms (in no particular order) that I’ve encountered during my career - never all in one person, since the list runs the gamut from ignoramuses to pedants to their mutant offspring - and invite you to play bingo to see how your experience compares with others.
Where’s the cunning part? Perhaps one of these is causing your team a special headache. Let’s fix that! Each symptom comes with a link to an article that you can send the offending entity to help cure them. We’ll try to be sneaky - the official cover story is that you’re just sharing these articles to “entertain” your friends... that’s why I tried to make them as entertaining as possible.
Be sure to use the original links I’ve provided, since they sneak you past the Medium.com paywall.
Ready? Let’s play bingo!
Count how many of these you’ve encountered during your career and let me know your score here (link repeated at the bottom).
Symptom 1: They have no idea what AI is, but they talk about it anyway
Treatment: bit.ly/quaesita_sbucks and bit.ly/quaesita_simplest
Symptom 2: They believe machines + data + math = objectivity
Treatment: bit.ly/quaesita_aibias and bit.ly/quaesita_tiger
Symptom 3: They make you cringe whenever they say “because”
Treatment: bit.ly/quaesita_experiment and bit.ly/quaesita_correlation
Symptom 4: Their ignorance of the data science job market is impressive
Treatment: bit.ly/quaesita_bubble
Symptom 5: They expect machine learning to solve all their problems
Treatment: bit.ly/quaesita_fad
Symptom 6: They wouldn’t know a data-driven decision if it punched them
Treatment: bit.ly/quaesita_inspired
Symptom 7: They want to apply statistics *everywhere*
Treatment: bit.ly/quaesita_pointofstats and bit.ly/quaesita_saddest
Symptom 8: They think analysts are “lesser” data professionals
Treatment: bit.ly/quaesita_hero
Symptom 9: They never split their data… and it shows
Treatment: bit.ly/quaesita_sydd
Symptom 10: They get excited about obviously-doomed ML/AI use cases
Treatment: bit.ly/quaesita_island and bit.ly/quaesita_parrot
Symptom 11: They’ve missed the point of hypothesis testing
Treatment: bit.ly/quaesita_damnedlies
Symptom 12: They’re constantly second-guessing the person in charge
Treatment: bit.ly/quaesita_incomp
Symptom 13: They think algorithms are the most important part of ML/AI
Treatment: bit.ly/quaesita_fail
Symptom 14: They say “data science is the sexiest job of the 21st century”
Treatment: bit.ly/quaesita_22
Symptom 15: They trust ML/AI solutions that other people have built
Treatment: bit.ly/quaesita_donttrust
Symptom 16: They don’t see what’s so hard about getting useful data
Treatment: bit.ly/quaesita_phillla and bit.ly/quaesita_provenance
Symptom 17: They insist that “numbers can’t lie” and “facts are facts”
Treatment: bit.ly/quaesita_confirmation and bit.ly/quaesita_scientists
Symptom 18: They’re obsessed with hiring PhD researchers
Treatment: bit.ly/quaesita_roles
Symptom 19: They’re only willing to trust AI if “it can explain itself”
Treatment: bit.ly/quaesita_xai
Symptom 20: They think leading a team of data scientists is easy
Treatment: bit.ly/quaesita_dsleaders
Symptom 21: They take an analyst’s “insights” seriously
Treatment: bit.ly/quaesita_versus and bit.ly/quaesita_datasci
Symptom 22: Their presentations are gooey with jargon and equations
Treatment: bit.ly/quaesita_noeqns and bit.ly/quaesita_speaking
Symptom 23: They’re scared of AI for the wrong reasons
Treatment: bit.ly/quaesita_ethics and bit.ly/quaesita_genie
Symptom 24: They expect a data pro to know “the everything” of data
Treatment: bit.ly/quaesita_universe
Symptom 25: They nitpick constantly and can’t get enough of “rigor”
Treatment: bit.ly/quaesita_battle
Symptom 26: They don’t see the point of doing validation (properly)
Treatment: bit.ly/quaesita_idiot
Symptom 27: They think unsupervised learning is unsupervised
Treatment: bit.ly/quaesita_unsupervised and bit.ly/quaesita_drugs
Symptom 28: They start ML/AI projects by calling in the nerds
Treatment: bit.ly/quaesita_first
Symptom 29: They won’t shut up about how terrible p-values are
Treatment: bit.ly/quaesita_needles
Symptom 30: They tell lies with data and call it “data storytelling”
Treatment: bit.ly/quaesita_inkblot
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So, what’s your score? How many of these are painfully familiar? Let me know here!