PrefersGemini toChatGPTUses ChatGPT(or Gemini/Claude) 3+times per weekUsesMATLABUsesVisualStudioCodeKnowsBayestheorem offthe top oftheir headHas vibecodedsomethingPrefersTableau toPowerBIHas usedreinforcementlearningSpeaks>4 naturallanguagesPrefers shippingsomethingsimple overbuildingsomethingcleverHas beenthe onlydata personon a teamPreferspytorch totensorflowHasneverused gitPresented amodel/dash-board, only tobe asked for anExcel exportTried toexplain whycorrelation≠ causationWrote an entirefunction, only todiscover there’sa library thatdoes it betterHas beento a datascienceconferenceHad toexplain p-values to anon-technicalaudienceHasworkedremotelyfull-timeHasbrokenproductionBelievesfeatureengineering ismore importantthan modelselectionUsesJupyterTried toexplainoverfitting toyourmanager/clientStillusespython 2PrefersMacOSThinks mostAI-generatedimages lookslightly offHas hard-codedsomething“just for thedemo”Hasmanuallycleaned aCSV in ExcelHastransitionedinto datafrom anotherfieldThinksXGBoostis still kingFound outthat yourdataset hasduplicate rowsafter analysisHas Googledtheir ownerrormessageword-for-wordHastrained amodel ona GPUTuned hyper-parametersfor hours withnoimprovementThought aproject wouldtake aweek…it tookthree monthsStudied/studiesdatascienceHad a modelworkperfectly…only intrainingSpends moretime wranglingdata thanbuildingmodelsUsesRHas copiedcode fromStackOverflowBuilt adashboardthat no oneever usedSomeoneasked you to“just add AI”to a projectHas aChatGPTProsubscriptionUsesPythonHas beenasked “isthis AI?”Uses >4programminglanguagesPrefersGemini toChatGPTUses ChatGPT(or Gemini/Claude) 3+times per weekUsesMATLABUsesVisualStudioCodeKnowsBayestheorem offthe top oftheir headHas vibecodedsomethingPrefersTableau toPowerBIHas usedreinforcementlearningSpeaks>4 naturallanguagesPrefers shippingsomethingsimple overbuildingsomethingcleverHas beenthe onlydata personon a teamPreferspytorch totensorflowHasneverused gitPresented amodel/dash-board, only tobe asked for anExcel exportTried toexplain whycorrelation≠ causationWrote an entirefunction, only todiscover there’sa library thatdoes it betterHas beento a datascienceconferenceHad toexplain p-values to anon-technicalaudienceHasworkedremotelyfull-timeHasbrokenproductionBelievesfeatureengineering ismore importantthan modelselectionUsesJupyterTried toexplainoverfitting toyourmanager/clientStillusespython 2PrefersMacOSThinks mostAI-generatedimages lookslightly offHas hard-codedsomething“just for thedemo”Hasmanuallycleaned aCSV in ExcelHastransitionedinto datafrom anotherfieldThinksXGBoostis still kingFound outthat yourdataset hasduplicate rowsafter analysisHas Googledtheir ownerrormessageword-for-wordHastrained amodel ona GPUTuned hyper-parametersfor hours withnoimprovementThought aproject wouldtake aweek…it tookthree monthsStudied/studiesdatascienceHad a modelworkperfectly…only intrainingSpends moretime wranglingdata thanbuildingmodelsUsesRHas copiedcode fromStackOverflowBuilt adashboardthat no oneever usedSomeoneasked you to“just add AI”to a projectHas aChatGPTProsubscriptionUsesPythonHas beenasked “isthis AI?”Uses >4programminglanguages

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