CompTIA Data+ DA0-002 Lessons

Every lesson is free to read, with three practice questions each. Work through them in order, or jump to the objective you need.

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  1. Databases: relational, NoSQL and the data warehouseObjective 1.1Audio
  2. Data types, data structures and file formatsObjective 1.1Audio
  3. Data sources: databases, APIs and website dataObjective 1.2Audio
  4. Data sources: files, logs and repositoriesObjective 1.2Audio
  5. Cloud, on-premises and where the data livesObjective 1.3Audio
  6. Storage and containerization for data workObjective 1.3Audio
  7. Coding environments: SQL, Python, R and notebooksObjective 1.4Audio
  8. BI software, spreadsheets and analysis platformsObjective 1.4Audio
  9. AI models and machine learning, for analystsObjective 1.5Audio
  10. Natural language processing and robotic process automationObjective 1.5Audio
  11. Querying with SQL: SELECT, WHERE and ORDER BYObjective 2.1Audio
  12. Joins and combining data setsObjective 2.1Audio
  13. Aggregation, grouping and subqueriesObjective 2.1Audio
  14. Data integration: ETL, ELT and pipelinesObjective 2.1Audio
  15. Profiling a new data set before trusting itObjective 2.2Audio
  16. Missing values, duplicates and redundancyObjective 2.2Audio
  17. Finding and handling outliersObjective 2.2Audio
  18. Cleansing and standardising dataObjective 2.3Audio
  19. Merging, appending and reshaping dataObjective 2.3Audio
  20. Parsing dates, strings and structured fieldsObjective 2.3Audio
  21. Formatting, derived fields and preparing featuresObjective 2.3Audio
  22. Descriptive statistics: centre and spreadObjective 3.2Audio
  23. Distributions and the shape of dataObjective 3.2Audio
  24. Sampling, bias and confidence intervalsObjective 3.2Audio
  25. Hypothesis testing and statistical significanceObjective 3.2Audio
  26. Correlation and regressionObjective 3.2Audio
  27. Trends, time series and the types of analysisObjective 3.2Audio
  28. Knowing the audience for an analysisObjective 3.1Audio
  29. Presenting findings with their limitsObjective 3.1Audio
  30. Choosing the method for the question and the audienceObjective 3.1Audio
  31. When the numbers do not agree: reconciling resultsObjective 3.3Audio
  32. Common analysis errors, from double counting to Simpson's paradoxObjective 3.3Audio
  33. Tools and resources for fixing an analysisObjective 3.3Audio
  34. Choosing the right chart for the questionObjective 4.1Audio
  35. Maps and geographic dataObjective 4.1Audio
  36. Design elements: colour, labels and accessibilityObjective 4.1Audio
  37. Misleading visuals and how to spot themObjective 4.1Audio
  38. Dashboards against reportsObjective 4.2Audio
  39. Delivering reports: scheduling, distribution and self-serviceObjective 4.2Audio
  40. Filters, drill-down and interactivityObjective 4.2Audio
  41. Validating a report against its sourceObjective 4.3Audio
  42. Refresh failures and stale dataObjective 4.3Audio
  43. Review and quality control before publishingObjective 4.3Audio
  44. Documentation: data dictionaries and metadataObjective 5.1Audio
  45. Versioning and data lineageObjective 5.1Audio
  46. Regulations and data classificationObjective 5.2Audio
  47. Retention and auditsObjective 5.2Audio
  48. Access control and encryption for dataObjective 5.3Audio
  49. Masking, anonymisation and pseudonymisationObjective 5.3Audio
  50. Data quality: profiling, monitoring and testingObjective 5.4Audio