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Data Visualization: How to make data attractive

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Presentation by Alycia Murugesson & Nokuthula Mabhena on how to make data attractive for the 5th Biennial SAMEA Conference. Covers data visualization and infographics.

Data Visualization: How to make data attractive

  1. 1. HOW TO MAKE DATA ATTRACTIVE Alycia Murugesson & Nokuthula Mabhena SAMEA Conference October 2015
  2. 2. Data is all around us & has been for a very long time
  3. 3. DATA VISUALISATION a process by which data is presented in a visual or graphical format
  4. 4. Your data is only as good as your ability to understand and communicate it, which is why choosing the right visualization is essential. –Data Visualization 101
  5. 5. WHY USE VISUALS & GRAPHICS
  6. 6. of communication is nonverbal Psychologist Albert Mehrabian, 1972
  7. 7. Visual stimulants are processed up to times faster than text
  8. 8. Studies show that people remember: 10% of what they hear 20% of what they read 80% of what they see
  9. 9. The use of creative visuals to produce attractive reports, grabs the attention of readers & ensures that FINDINGS ARE NOT IGNORED
  10. 10. Evaluators can showcase trends, correlations & outliers & draw attention to the differences in comparative data trends correlations outliers
  11. 11. Findings can be reported using numbers, but to highlight results, VISUALS DRIVE THE MESSAGE
  12. 12. The advantage to using visuals is that they are more easily consumed and understood by a wider range of people.
  13. 13. KNOW YOUR DATA
  14. 14. Creating a data visualisation is a lot like cooking. You decide what data you need, you collect it, you prepare and clean it for use, and then you make the visualisation and present your finished result. –Data & Design
  15. 15. Quantitative (nominal, ordinal, interval and ratio) Qualitative Categorical DATA TYPES
  16. 16. Interval Categorical Nominal Ordinal Qualitative Term Grade No. of Learners No. of classes per grade Favourite Activity Educators comment 1 Grade 4 280 4 Soccer “Classes are too crowded and it is difficult to attend to all learners” 2 Grade 6 190 4 Dancing “We need more textbooks, learners have to share”
  17. 17. Collection & process Identify patterns in your data Isolate key messages DATA CLEANING & AGGREGATION
  18. 18. Knowing your data helps with deciding WHICH & HOW MUCH data to illustrate, consider these three questions:
  19. 19. What Do I Know About My Data?
  20. 20. What Do I Know About My Data? What do the findings mean?
  21. 21. What Do I Know About My Data? What do the findings mean? Is it important to communicate?
  22. 22. The only thing worse than not visualising data is visualising data incorrectly. –Randy Olsen
  23. 23. THE HOW TO: METHODS AND EXAMPLES
  24. 24. TYPES OF GRAPHS Bar/Column charts Pie/ Donut charts Line charts Scatter plots/ Bubble charts
  25. 25. 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 Gr4 Gr3 Gr5 Gr7 Gr6 BAR/COLUMN
  26. 26. 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 Gr4 Gr3 Gr5 Gr7 Gr6 BAR/COLUMN Notitle Inconsistent colours Axisintervalsare notsuitable 3Dvisualisations canbemisleading VerticalLabelscan behardtoread Dataisnotordered appropriately They-axisis truncated
  27. 27. BAR/COLUMN 0 50 100 150 200 250 300 350 Gr 3 Gr 4 Gr 5 Gr 6 Gr 7 NumberofLearners Number of Learners per Grade
  28. 28. 0 50 100 150 200 250 300 350 Gr 3 Gr 4 Gr 5 Gr 6 Gr 7 NumberofLearners Number of Learners per Grade BAR/COLUMN Useconsistent colours UsesuitableAxis intervals Descriptivetitle Spacebars appropriately UseHorizontal Labels Orderdata appropriately Startthe y-axisat0
  29. 29. BAR/COLUMN 280 123 232 322 243 Gr 3 Gr 4 Gr 5 Gr 6 Gr 7 Overcrowding in Grade 6 With only 4 classes in Grade 6, the average number of learners per class is too high
  30. 30. PIE/DONUT 82 56 21 59 12 9 3 5 33 No of Gr learners favourite activity Soccer Tennis Swimming Netball Hockey Cricket Rugby Running Dance
  31. 31. 82 56 21 59 12 9 35 33 No of Gr learners favourite activity Soccer Tennis Swimming Netball Hockey Cricket Rugby Running Dance PIE/DONUT Toomany categories Unitsinsteadof percentages 3Dvisualisations canbemisleading Toomany colours
  32. 32. PIE/DONUT 29% 21%20% 18% 12% Gr 3's Favourite Activity Soccer Netball Tennis Dance Other
  33. 33. 29% 21%20% 18% 12% Gr 3's Favourite Activity Soccer Netball Tennis Dance Other PIE/DONUT Orderslices correctly Visualisenomore than5categories Usepercentages- makesuretheyadd upto100%
  34. 34. PIE/DONUT 29% 21%20% 18% 12% Grade 3 learners love playing soccer When asked to indicate what after school activity is their favourite, 29% chose soccer. Following was Netball, tennis and dance at 21%, 20% and 18% respectively Soccer Netball Tennis Dance Other
  35. 35. LINE 36 45 60 69 25 54 72 89 32 40 51 66 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1 2 3 4 LEARNERS MATHS SCORES GRADE 3 GRADE 4 GRADE 5
  36. 36. 36 45 60 69 25 54 72 89 32 40 51 66 20 30 40 50 60 70 80 90 100 110 120 130 140 150 1 2 3 4 LEARNERS MATHS SCORES GRADE 3 GRADE 4 GRADE 5 LINE LABELSHARDTO READ INCORRECTHEIGHT DISTRACTING LINES INCORRECTLYREPRESENTED BASELINE
  37. 37. LINE GRADE 3 GRADE 4 GRADE 5 0 10 20 30 40 50 60 70 80 90 100 Term 1 Term 2 Term 3 Term 4 AveragePercentage Learners Maths Scores in 2014
  38. 38. GRADE 3 GRADE 4 GRADE 5 0 10 20 30 40 50 60 70 80 90 100 Term 1 Term 2 Term 3 Term 4 AveragePercentage Learners Maths Scores in 2014 LINE Labelthelines directly USETHERIGHT HEIGHT UsesolidLINES only Includeazero baseline
  39. 39. LINE GRADE 3 GRADE 4 GRADE 5 0% 20% 40% 60% 80% 100% Term 1 Term 2 Term 3 Term 4 Grade 4 achieves highest gains in Maths scores The Grade 4s improved their maths scores by over 60% from an average score of 25% in Term 1 to 89% in Term 4
  40. 40. SCATTER/BUBBLE 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 80 90 100 Literacy Score
  41. 41. 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 80 90 100 Literacy Score SCATTER/BUBBLE Densegridlines aredistracting Toomany trendlines NOAXISTITLES Pointshard tosee
  42. 42. SCATTER/BUBBLE 0 10 20 30 40 50 0 20 40 60 80 100 ExtraClassesAttended Learners’ Literacy Score Literacy Scores, by Extra Classes Attended
  43. 43. 0 10 20 30 40 50 0 20 40 60 80 100 ExtraClassesAttended Learners’ Literacy Score Literacy Scores, by Extra Classes Attended SCATTER/BUBBLE Nogridlines Oneortwo TRENDLINES ClearAXIS TITLES Morevisible points
  44. 44. SCATTER/BUBBLE 0 10 20 30 40 50 0 20 40 60 80 100 ExtraClassesAttended Learners’ Literacy Percentage Extra classes contribute to higher literacy scores Learners who attended over 25 extra literacy classes achieved a literacy score of 50% or more.
  45. 45. INFOGRAPHIC graphic visual representations of information, data or knowledge intended to present information quickly and clearly.
  46. 46. INFOGRAPHICS are essential for making data attractive & can include a number of different DATA VISUALISATIONS
  47. 47. Both data visualization and infographics turn data into images that nearly anyone can easily understand – making them invaluable tools for explaining the significance of digits to people who are more visually oriented. – Jonsen Carmack
  48. 48. OTHER VISUALISATION MISTAKES
  49. 49. COLOUR
  50. 50. this makes colour blind people cry
  51. 51. analogous triadcomplementary COLOUR RULE
  52. 52. analogous triadcomplementary Design seeds –http://design-seeds.com/
  53. 53. FONT
  54. 54. When good people pick bad fonts
  55. 55. Bradley Hand ITC Brush Script Courier New Comic Sans MS Juice ITC Kristen ITC Lucida Console Times New Roman Trebuchet MS Tempus Sans Papyrus Verdana
  56. 56. Don’t forget to provide CONTEXT for your visuals & a SUPPORTING NARRATIVE
  57. 57. RESOURCES
  58. 58. CANVA https://www.canva.com/ EVERGREEN DATA http://stephanieevergreen.com/ TABLEAU http://www.tableausoftware.com/ VISAGE https://create.visage.co/ PIKTOCHART http://www.piktochart.com/
  59. 59. ADOBE COLOR CC https://color.adobe.com/ DESIGN-SEEDS http://design-seeds.com/ FONT SQUIRREL http://www.fontsquirrel.com/ FLATICON http://www.flaticon.com/ FREEPIK http://www.freepik.com/
  60. 60. THANK YOUAlycia Murugesson & Nokuthula Mabhena Khulisa Management Services info@Khulisa.com

Editor's Notes

  • Early forms of data visualisation can be found in the form of maps and graphs dating back to the 1600’s
  • Data Visualisation is defined as a process by which data is presented in a visual or graphical format. The goal is to effectively communicate information to a diverse audience, in the form of visual data which is easily consumed and digested.
    Your data is only as good as your ability to understand and communicate it, which is why choosing the right visualization is essential
    –Data Visualization 101
     
    In order to succeed in visualising data, there are many factors to consider. This includes; knowing your data, understanding the power of visuals as well as learning how to create appropriate graphics. It is also important to be aware of common data visualisation mistakes to avoid.
  • If your data is misrepresented or presented ineffectively, key insights and understanding
    are lost, which hurts both your message and your reputation. The good news is that you
    don’t need a PhD in statistics to crack the data visualization code. This guide will walk you
    through the most common charts and visualizations, help you choose the right
    presentation for your data, and give you practical design tips and tricks to make sure
    you avoid rookie mistakes. It’s everything you need to help your data make a big impact.
  • With the increase in the use of graphics to disseminate information, reporting is in the process of a transformation.
    This involves the use of creative visuals to produce attractive reports,
    grabbing the attention of readers and ensuring that findings are not ignored.
  • Communicating data using visuals allows evaluators to showcase trends, correlation and outliers in interesting ways.
    It also draws attention to the differences which exist in comparative data.
    Findings can be conveyed using numbers, but to highlight certain results, visuals drive the message.
  • To accurately create visualisations, it is important to understand your data. This involves identifying the types of data that can be visualised, ensuring that your data is consistent and accurate as well as aggregating data to discover trends, correlations and outliers.
  • It is necessary to know the type of data you are working with as it determines how it can be visually communicated.
    Basic data groups include; quantitative (nominal, ordinal, interval and ratio), qualitative and categorical.
  • Data cleaning and aggregation

    Data should be collected and processed thoroughly to ensure consistency which will produce accurate results for analyses.

    Aggregation of data is necessary to identify patterns in your data which translate into key findings. These processes are necessary to isolate key messages to highlight using appropriate data visualisation.

  • Knowing your data helps with deciding which and how much data to illustrate when creating a visualisation. Consider these three questions
  • The first aspect to be considered is the type of graphic most appropriate to illustrate your data. Many different graphs and other visual methods can be utilised. The most commonly used graphs include:


    Each of these graphs can be used in several ways to display different types of data. Quick and easy changes to the standard format of these graphs can improve the appearance, making it more visually attractive and appealing to readers.
  • In Grade 3 30% belonged to the low achievers, 33% belonged to the medium achievers and 33% belonged to the high achievers.
  • Data Visualisation is defined as a process by which data is presented in a visual or graphical format. The goal is to effectively communicate information to a diverse audience, in the form of visual data which is easily consumed and digested.
    Your data is only as good as your ability to understand and communicate it, which is why choosing the right visualization is essential
    –Data Visualization 101
     
    In order to succeed in visualising data, there are many factors to consider. This includes; knowing your data, understanding the power of visuals as well as learning how to create appropriate graphics. It is also important to be aware of common data visualisation mistakes to avoid.
  • including graphs, charts, quotes, icons and pictures
    Infographics use these elements combined with appropriate use of colour and fonts to present results and findings in a visually appealing graphic.
  • Use abobe color cc to find suitable colour combinations
  • Note grayscale printing
  • Don’t forget to provide context for your visuals and a supporting narrative.
  • ×