ROLL NO 62 DIV D

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Across
  1. 2. : Evolution of Analytics
  2. 4. : Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
  3. 7. : Web Scraping basics in R
  4. 8. : Data Structures in R (vectors, lists, data frames, matrices, arrays, factors)
  5. 10. : Exploratory Data Analysis (EDA)
  6. 12. : Skills of a Business Analyst
  7. 15. : Business Analytics Applications (Marketing Analytics, HR Analytics, Supply Chain Analytics, Web & Social Media Analytics, Healthcare Analytics)
  8. 19. : Spatial Analysis (introduction)
  9. 20. : Business Analytics vs Business Analysis
  10. 21. : Business Intelligence vs Data Science
  11. 23. : Line, Bar, Pie Charts, Histograms (visualization types)
  12. 26. : Analytical Decision-Making Process
  13. 27. : Need for Analytics
  14. 30. : Data Visualization (concept)
Down
  1. 1. : Characteristics of Good Analytical Questions
  2. 3. : Data maturity stages of organizations
  3. 5. : Tools for Analytics
  4. 6. : Apply / sapply / tapply functions in R
  5. 9. : Data Analyst vs Business Analyst
  6. 11. : Data Types in R
  7. 13. : Popular Data Visualization Tools
  8. 14. : Flow Control in R (loops, conditionals)
  9. 16. : Data Quality Issues
  10. 17. : R Programming Environment
  11. 18. : File operations in R (reading/writing, SQL in R)
  12. 22. : Data Exploration / Data Cleaning
  13. 24. : Hypothesis Testing (conceptual)
  14. 25. : Concept of Insights
  15. 28. : Definition of Analytics
  16. 29. : Data, Information, Knowledge (differences)