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Data Literacy for Librarians


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Presentation on creating a data-driven project for library decision making - day 1

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Data Literacy for Librarians

  1. 1. Elaine Lasda, MLS, CAS University at Albany Libraries March 12, 2018
  2. 2. Introduction Collecting Data Interpreting Data Visualizing Data Class Missions Agenda
  3. 3. Philosophy
  4. 4. What is Data?
  5. 5. The Data Cake Rubric
  6. 6. Where do I find data?
  7. 7. Types of Data
  8. 8. Nominal Ordinal Interval/Discrete Ratio “NOIR” Data Type Rubric
  9. 9. Numerical Datatypes: Ordinal Continuous Discrete I find these more useful
  10. 10. Text/Character Datatypes: Character/String Categorical Interval I find these more useful
  11. 11.  Total count of print monograph volumes in the Texas State Library  Top 20 Library Systems in the Southwest ranked by annual budget  Total overdue fines paid at the Albany Public Library in FY 2016-2017  Responses collected from a suggestion box at the circulation desk of the Heermance Public Library  Children’s reading levels by age range  The zip codes encompassing your library’s service area Quick check: ID the Datatype
  12. 12. Collection Methods
  13. 13. Do do you have to collect your own data? First party data Collected by entity doing the analysis Unique; often a direct relationship to data source Trustworthy (?) Smaller datasets (mostly)
  14. 14. Second party data Access from external platform, but you can obtain it Repositories Creator of platform has direct relationship to data source  Trustworthy (?)
  15. 15. Third party data Access from another platform Collected anonymously; without user consent “data exhaust” Large, aggregated datasets  Trustworthy (?)
  16. 16. Quantitative Qualitative Mixed Methods Another Perspective
  17. 17. Empirical Anecdotal Logical A Third Perspective
  18. 18. How does the type of data you wish to collect affect the way in which you collect it? QUESTION FOR YOU
  19. 19. DATA CLEANING Cleaning
  20. 20. QUESTION FOR YOU What can happen to your data when it is being collected that requires you to “clean” it prior to analysis?
  21. 21. Formatting error Misspellings Decimal points off Numerical/text transpositions Incomplete data N/A vs. 0 Common Data “Dirt”
  22. 22. Data Transformation
  23. 23.  Apply a mathematical formula to correct for skew  Log  Square Root/Cube Root/Square  Inversion  For non-numeric data:  Create frequency tables  Assign a scale to a category  Category dis/aggregation What are transformations?
  24. 24. Data Interpretation
  25. 25. QUESTION FOR YOU What’s are some differences between Data and Statistics?
  26. 26. Descriptive Statistics
  27. 27. Inferential Statistics
  28. 28. Mean Median Mode
  29. 29. Range Quartile Variance Standard Deviation
  30. 30. Frequencies Frequency Distributions Correlation/Causation Measures of Error
  31. 31. Vizualization/Presentation
  32. 32. Table
  33. 33. Bar graph
  34. 34. Line Graph
  35. 35. Area Graph
  36. 36. Scatter Plot
  37. 37. Pie Graph
  38. 38. Name some key considerations when designing a chart, graph, infographic or other visual display of your data. QUESTION FOR YOU
  39. 39. Clear labels ID Units of easure Standard intervals Avoid 3-D effects LESS IS MORE Best Practices
  40. 40. Don’t let this happen to you
  41. 41.  Information is Beautiful  Many Eyes  The Grammar of Graphics  Tufte- Visual Explanations Develop Your Eye
  42. 42. Briefly: ETHICS
  43. 43. Ethics How is the data collected? How is the data used? How is the data stored and preserved and what are the implications? How and when is the data disposed of?
  44. 44. Questions?
  45. 45. Message Board: Case Study Assignment: Explore A Dataset Homework