PrefersMacOSUses ChatGPT(or Gemini/Claude) 3+times per weekThought aproject wouldtake aweek…it tookthree monthsBelievesfeatureengineering ismore importantthan modelselectionHas beenasked “isthis AI?”Has Googledtheir ownerrormessageword-for-wordSpeaks>4 naturallanguagesHas usedreinforcementlearningHasneverused gitSomeoneasked you to“just add AI”to a projectHasmanuallycleaned aCSV in ExcelHasworkedremotelyfull-timeHas beenthe onlydata personon a teamHad toexplain p-values to anon-technicalaudienceTried toexplain whycorrelation≠ causationHastrained amodel ona GPUStudied/studiesdatascienceKnowsBayestheorem offthe top oftheir headHas vibecodedsomethingHasbrokenproductionHas copiedcode fromStackOverflowPrefers shippingsomethingsimple overbuildingsomethingcleverThinks mostAI-generatedimages lookslightly offBuilt adashboardthat no oneever usedStillusespython 2UsesRSpends moretime wranglingdata thanbuildingmodelsHas aChatGPTProsubscriptionTuned hyper-parametersfor hours withnoimprovementPrefersTableau toPowerBIUsesPythonFound outthat yourdataset hasduplicate rowsafter analysisHas hard-codedsomething“just for thedemo”Wrote an entirefunction, only todiscover there’sa library thatdoes it betterPrefersGemini toChatGPTHas beento a datascienceconferenceUsesMATLABUses >4programminglanguagesThinksXGBoostis still kingTried toexplainoverfitting toyourmanager/clientPreferspytorch totensorflowUsesJupyterPresented amodel/dash-board, only tobe asked for anExcel exportUsesVisualStudioCodeHad a modelworkperfectly…only intrainingHastransitionedinto datafrom anotherfieldPrefersMacOSUses ChatGPT(or Gemini/Claude) 3+times per weekThought aproject wouldtake aweek…it tookthree monthsBelievesfeatureengineering ismore importantthan modelselectionHas beenasked “isthis AI?”Has Googledtheir ownerrormessageword-for-wordSpeaks>4 naturallanguagesHas usedreinforcementlearningHasneverused gitSomeoneasked you to“just add AI”to a projectHasmanuallycleaned aCSV in ExcelHasworkedremotelyfull-timeHas beenthe onlydata personon a teamHad toexplain p-values to anon-technicalaudienceTried toexplain whycorrelation≠ causationHastrained amodel ona GPUStudied/studiesdatascienceKnowsBayestheorem offthe top oftheir headHas vibecodedsomethingHasbrokenproductionHas copiedcode fromStackOverflowPrefers shippingsomethingsimple overbuildingsomethingcleverThinks mostAI-generatedimages lookslightly offBuilt adashboardthat no oneever usedStillusespython 2UsesRSpends moretime wranglingdata thanbuildingmodelsHas aChatGPTProsubscriptionTuned hyper-parametersfor hours withnoimprovementPrefersTableau toPowerBIUsesPythonFound outthat yourdataset hasduplicate rowsafter analysisHas hard-codedsomething“just for thedemo”Wrote an entirefunction, only todiscover there’sa library thatdoes it betterPrefersGemini toChatGPTHas beento a datascienceconferenceUsesMATLABUses >4programminglanguagesThinksXGBoostis still kingTried toexplainoverfitting toyourmanager/clientPreferspytorch totensorflowUsesJupyterPresented amodel/dash-board, only tobe asked for anExcel exportUsesVisualStudioCodeHad a modelworkperfectly…only intrainingHastransitionedinto datafrom anotherfield

Data Science Bingo! - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Prefers MacOS
  2. Uses ChatGPT (or Gemini/ Claude) 3+ times per week
  3. Thought a project would take a week…it took three months
  4. Believes feature engineering is more important than model selection
  5. Has been asked “is this AI?”
  6. Has Googled their own error message word-for-word
  7. Speaks >4 natural languages
  8. Has used reinforcement learning
  9. Has never used git
  10. Someone asked you to “just add AI” to a project
  11. Has manually cleaned a CSV in Excel
  12. Has worked remotely full-time
  13. Has been the only data person on a team
  14. Had to explain p-values to a non-technical audience
  15. Tried to explain why correlation ≠ causation
  16. Has trained a model on a GPU
  17. Studied/ studies data science
  18. Knows Bayes theorem off the top of their head
  19. Has vibe coded something
  20. Has broken production
  21. Has copied code from Stack Overflow
  22. Prefers shipping something simple over building something clever
  23. Thinks most AI-generated images look slightly off
  24. Built a dashboard that no one ever used
  25. Still uses python 2
  26. Uses R
  27. Spends more time wrangling data than building models
  28. Has a ChatGPT Pro subscription
  29. Tuned hyper-parameters for hours with no improvement
  30. Prefers Tableau to PowerBI
  31. Uses Python
  32. Found out that your dataset has duplicate rows after analysis
  33. Has hard-coded something “just for the demo”
  34. Wrote an entire function, only to discover there’s a library that does it better
  35. Prefers Gemini to ChatGPT
  36. Has been to a data science conference
  37. Uses MATLAB
  38. Uses >4 programming languages
  39. Thinks XGBoost is still king
  40. Tried to explain overfitting to your manager/client
  41. Prefers pytorch to tensorflow
  42. Uses Jupyter
  43. Presented a model/dash-board, only to be asked for an Excel export
  44. Uses Visual Studio Code
  45. Had a model work perfectly…only in training
  46. Has transitioned into data from another field