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Data Visualization: How to make data attractive
The document discusses the importance of data visualization and effective communication of data insights through visually appealing formats. It emphasizes that visuals are processed faster than text and can significantly enhance understanding and retention of information. The document also outlines various types of data visualizations, best practices for creating them, and resources for further learning.
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Data Visualization: How to make data attractive
- 1. HOW TO MAKE DATA ATTRACTIVE AlyciaMurugesson & Nokuthula Mabhena SAMEA Conference October 2015
- 2. Data is allaround us & has been for a very long time
- 3. DATA VISUALISATION a process bywhich data is presented in a visual or graphical format
- 4. Your data isonly as good as your ability to understand and communicate it, which is why choosing the right visualization is essential. –Data Visualization 101
- 5. WHY USE VISUALS& GRAPHICS
- 6. of communication is nonverbal PsychologistAlbert Mehrabian, 1972
- 7. Visual stimulants are processedup to times faster than text
- 8. Studies show thatpeople remember: 10% of what they hear 20% of what they read 80% of what they see
- 9. The use ofcreative visuals to produce attractive reports, grabs the attention of readers & ensures that FINDINGS ARE NOT IGNORED
- 10. Evaluators can showcasetrends, correlations & outliers & draw attention to the differences in comparative data trends correlations outliers
- 11. Findings can bereported using numbers, but to highlight results, VISUALS DRIVE THE MESSAGE
- 12. The advantage tousing visuals is that they are more easily consumed and understood by a wider range of people.
- 13.
- 14. Creating a datavisualisation 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. Quantitative (nominal, ordinal,interval and ratio) Qualitative Categorical DATA TYPES
- 16. Interval Categorical NominalOrdinal 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. Collection & process Identifypatterns in your data Isolate key messages DATA CLEANING & AGGREGATION
- 18. Knowing your datahelps with deciding WHICH & HOW MUCH data to illustrate, consider these three questions:
- 19. What Do IKnow About My Data?
- 20. What Do IKnow About My Data? What do the findings mean?
- 21. What Do IKnow About My Data? What do the findings mean? Is it important to communicate?
- 22. The only thingworse than not visualising data is visualising data incorrectly. –Randy Olsen
- 23. THE HOW TO: METHODSAND EXAMPLES
- 24. TYPES OF GRAPHS Bar/Column charts Pie/Donut charts Line charts Scatter plots/ Bubble charts
- 25.
- 26.
- 27. BAR/COLUMN 0 50 100 150 200 250 300 350 Gr 3 Gr4 Gr 5 Gr 6 Gr 7 NumberofLearners Number of Learners per Grade
- 28. 0 50 100 150 200 250 300 350 Gr 3 Gr4 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. BAR/COLUMN 280 123 232 322 243 Gr 3 Gr 4 Gr5 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. PIE/DONUT 82 56 21 59 12 9 3 5 33 No of Grlearners favourite activity Soccer Tennis Swimming Netball Hockey Cricket Rugby Running Dance
- 31. 82 56 21 59 12 9 35 33 No ofGr learners favourite activity Soccer Tennis Swimming Netball Hockey Cricket Rugby Running Dance PIE/DONUT Toomany categories Unitsinsteadof percentages 3Dvisualisations canbemisleading Toomany colours
- 32. PIE/DONUT 29% 21%20% 18% 12% Gr 3's FavouriteActivity Soccer Netball Tennis Dance Other
- 33. 29% 21%20% 18% 12% Gr 3's FavouriteActivity Soccer Netball Tennis Dance Other PIE/DONUT Orderslices correctly Visualisenomore than5categories Usepercentages- makesuretheyadd upto100%
- 34. PIE/DONUT 29% 21%20% 18% 12% Grade 3 learnerslove 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. 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 34 LEARNERS MATHS SCORES GRADE 3 GRADE 4 GRADE 5
- 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 34 LEARNERS MATHS SCORES GRADE 3 GRADE 4 GRADE 5 LINE LABELSHARDTO READ INCORRECTHEIGHT DISTRACTING LINES INCORRECTLYREPRESENTED BASELINE
- 37. LINE GRADE 3 GRADE 4 GRADE5 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. GRADE 3 GRADE 4 GRADE5 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. LINE GRADE 3 GRADE 4 GRADE5 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. SCATTER/BUBBLE 0 10 20 30 40 50 60 70 0 10 2030 40 50 60 70 80 90 100 Literacy Score
- 41. 0 10 20 30 40 50 60 70 0 10 2030 40 50 60 70 80 90 100 Literacy Score SCATTER/BUBBLE Densegridlines aredistracting Toomany trendlines NOAXISTITLES Pointshard tosee
- 42. SCATTER/BUBBLE 0 10 20 30 40 50 0 20 4060 80 100 ExtraClassesAttended Learners’ Literacy Score Literacy Scores, by Extra Classes Attended
- 43. 0 10 20 30 40 50 0 20 4060 80 100 ExtraClassesAttended Learners’ Literacy Score Literacy Scores, by Extra Classes Attended SCATTER/BUBBLE Nogridlines Oneortwo TRENDLINES ClearAXIS TITLES Morevisible points
- 44. SCATTER/BUBBLE 0 10 20 30 40 50 0 20 4060 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. INFOGRAPHIC graphic visual representationsof information, data or knowledge intended to present information quickly and clearly.
- 46. INFOGRAPHICS are essentialfor making data attractive & can include a number of different DATA VISUALISATIONS
- 47. Both data visualizationand 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
- 49.
- 50.
- 51. this makes colour blindpeople cry
- 52.
- 53. analogous triadcomplementary Design seeds–http://design-seeds.com/
- 54.
- 55. When good people pickbad fonts
- 56. Bradley Hand ITC BrushScript Courier New Comic Sans MS Juice ITC Kristen ITC Lucida Console Times New Roman Trebuchet MS Tempus Sans Papyrus Verdana
- 57. Don’t forget to provideCONTEXT for your visuals & a SUPPORTING NARRATIVE
- 58.
- 59. CANVA https://www.canva.com/ EVERGREEN DATAhttp://stephanieevergreen.com/ TABLEAU http://www.tableausoftware.com/ VISAGE https://create.visage.co/ PIKTOCHART http://www.piktochart.com/
- 60. ADOBE COLOR CChttps://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/
- 61. THANK YOUAlycia Murugesson& Nokuthula Mabhena Khulisa Management Services info@Khulisa.com
Editor's Notes
- #3 Early forms of data visualisation can be found in the form of maps and graphs dating back to the 1600’s
- #4 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.
- #5 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.
- #10 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.
- #11 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.
- #14 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.
- #16 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.
- #18 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.
- #19 Knowing your data helps with deciding which and how much data to illustrate when creating a visualisation. Consider these three questions
- #25 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.
- #40 In Grade 3 30% belonged to the low achievers, 33% belonged to the medium achievers and 33% belonged to the high achievers.
- #46 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.
- #47 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.
- #52 Use abobe color cc to find suitable colour combinations
- #53 Note grayscale printing
- #58 Don’t forget to provide context for your visuals and a supporting narrative.