Knows howto create abar chart inExcelKnows howto importdata into aspreadsheetor databaseKnowswhat ahistogramis used forHas donedatavisualisationKnowswhat abox plotrepresents Has usedExcel fordataanalysisPursuing adegree indata scienceor analyticsCandescribe theprocess ofhypothesistestingHasexperiencewithpredictivemodelingCan identifytrends in atime seriesdatasetHasexperiencewithclusteringalgorithmsKnows howto calculatea percentagechangeCancalculate asimplecorrelationcoefficientCanidentifyoutliers ina datasetFreeKnows howto create apie chart inExcelCaninterpretA/B testresultsHasattended adata sciencebootcampKnowswhat adict is inPythonCan explainmachinelearningconceptsCan interpreta basicregressionanalysisUnderstandsthe conceptof data typesHas built adashboardusingTableau orPower BIHascodedin RHasexperiencewith cloudcomputingplatformsHas donedataanalysisHascoded inPythonCan explainthe conceptof datanormalizationHasdeployedmachinelearningmodelsHascontributed toopen-sourcedata scienceprojectsCan explainthe conceptof overfittingin machinelearningHascreated abasic lineplotHasconductedbasic datacleaningtasksHasattended adata sciencemeetup orconferenceCan explainthe differencebetweenmean andmedianHas used pivottables for datasummarizationKnows howto create abar chart inExcelKnows howto importdata into aspreadsheetor databaseKnowswhat ahistogramis used forHas donedatavisualisationKnowswhat abox plotrepresents Has usedExcel fordataanalysisPursuing adegree indata scienceor analyticsCandescribe theprocess ofhypothesistestingHasexperiencewithpredictivemodelingCan identifytrends in atime seriesdatasetHasexperiencewithclusteringalgorithmsKnows howto calculatea percentagechangeCancalculate asimplecorrelationcoefficientCanidentifyoutliers ina datasetFreeKnows howto create apie chart inExcelCaninterpretA/B testresultsHasattended adata sciencebootcampKnowswhat adict is inPythonCan explainmachinelearningconceptsCan interpreta basicregressionanalysisUnderstandsthe conceptof data typesHas built adashboardusingTableau orPower BIHascodedin RHasexperiencewith cloudcomputingplatformsHas donedataanalysisHascoded inPythonCan explainthe conceptof datanormalizationHasdeployedmachinelearningmodelsHascontributed toopen-sourcedata scienceprojectsCan explainthe conceptof overfittingin machinelearningHascreated abasic lineplotHasconductedbasic datacleaningtasksHasattended adata sciencemeetup orconferenceCan explainthe differencebetweenmean andmedianHas used pivottables for datasummarization

Data Analytics 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. Knows how to create a bar chart in Excel
  2. Knows how to import data into a spreadsheet or database
  3. Knows what a histogram is used for
  4. Has done data visualisation
  5. Knows what a box plot represents
  6. Has used Excel for data analysis
  7. Pursuing a degree in data science or analytics
  8. Can describe the process of hypothesis testing
  9. Has experience with predictive modeling
  10. Can identify trends in a time series dataset
  11. Has experience with clustering algorithms
  12. Knows how to calculate a percentage change
  13. Can calculate a simple correlation coefficient
  14. Can identify outliers in a dataset
  15. Free
  16. Knows how to create a pie chart in Excel
  17. Can interpret A/B test results
  18. Has attended a data science bootcamp
  19. Knows what a dict is in Python
  20. Can explain machine learning concepts
  21. Can interpret a basic regression analysis
  22. Understands the concept of data types
  23. Has built a dashboard using Tableau or Power BI
  24. Has coded in R
  25. Has experience with cloud computing platforms
  26. Has done data analysis
  27. Has coded in Python
  28. Can explain the concept of data normalization
  29. Has deployed machine learning models
  30. Has contributed to open-source data science projects
  31. Can explain the concept of overfitting in machine learning
  32. Has created a basic line plot
  33. Has conducted basic data cleaning tasks
  34. Has attended a data science meetup or conference
  35. Can explain the difference between mean and median
  36. Has used pivot tables for data summarization