Data visualization_Creating basics charts

Data analytics

1. What is Tableau

2. WhatisTableau ?
•Tableau is an excellent data visualization and
business intelligence tool used for reporting and
analyzing vast volumes of data.
•Founded: January 2003
•Parent organization: Salesforce Inc
•Founders: Pat Hanrahan, Christian Chabot, Chris
Stolte
•Subsidiaries: Narrative Science, HyPer
•President: Mark Nelson
•Headquarters: Seattle, Washington, United States
•CEO: Mark Nelson

3. Tableau Architecture

4. Tableau Products

5. Tableau Desktop Workspace

6. Tableau workspace after importing data

7. Tableau Desktop Workspace Menu

8. Tableau Navigation

9. Data Window in Tableau

10. Data Window in Tableau

11. Data Window in Tableau

12. Dimensions and measures
•Dimensionscontain qualitative values (such as
names, dates, or geographical data). You can use
dimensions to categorize, segment, and reveal the
details in your data. Dimensions affect the level of
detail in the view.
•Measures contain numeric, quantitative values that
you can measure. Measures can be aggregated.
When you drag a measure into the view, Tableau
applies an aggregation to that measure (by default)

13. Blue and Green Color in tableau
•Blue = Discrete Field
•Green = Continuous fileld

14. File Extensions

15. Pills
A piece of data that you want to show up on
your visualization.

16. Shelves
A shelf is an area on the visualization screen
where you can drop pieces of data so that
Tableau can act on them.

17. Data Types in Tableau

18. Advantages of Tableau

19. Advantages of Tableau

20. Disadvantages of tableau

22. WhatisaBarchart?
•Abarchartisawayofsummarizingasetofcategorical
data(continuousdatacanbemadecategoricalbyauto-
binning).
•Thebarchartdisplaysdatausinganumberofbars,each
representingaparticularcategory.
•Theheightofeachbarisproportionaltoaspecific
aggregation(forexamplethesumofthevaluesinthe
categoryitrepresents).
•Thecategoriescouldbesomethinglikeanagegroupora
geographicallocation.
•Itisalsopossibletocolororspliteachbarintoanother
categoricalcolumninthedata,whichenablesyoutosee
thecontributionfromdifferentcategoriestoeachbaror
groupofbarsinthebarchart.

24. StackedBarChart
•Stackedcolumnchartsworkwellwhenthefocus
ofthechartistocomparethetotalsandonepart
ofthetotals.
•Stacked column chart can work well for dates,
but should only be considered if the total of your
partsis crucial.
•If you want to use stacked column charts for time
data, make sure your dates have the same
intervals.
•Stacked column charts work well for only a few
totals.

25. GroupedBarChart
•A grouped bar chart, also known as clustered bar
graph, multi-set bar chart, or grouped column
chart, is a type of bar graph that is used to
represent and compare different categories of
twoor more groups.
•Becausethecategoriesaregroupedand
arranged side-by-side, the bar clusters make easy
to interpret the differences inside a group, and
evenbetweenthesamecategoryacrossgroups.

26. Bestusesforthegroupedbarchart
•Showing the changes in set categories over time,
locationor sector
•Comparing financial data–for example sales,
costsandrevenue–acrossdifferentgroups
•Evaluating the performance of a series of
marketingprojectsincontrasttooneanother

28. Advantages
•showeachdatacategoryina frequencydistribution
•displayrelativenumbersorproportionsofmultiple
categories
•summarizealargedatasetinvisualform
•clarifytrendsbetterthandotables
•estimatekeyvaluesataglance
•permit a visual check of the accuracy and
reasonableness of calculations
•be easily understood due to widespread use in
businessandthemedia

29. Disadvantages
•requireadditionalexplanation
•beeasilymanipulatedtoyieldfalseimpressions
•fail to reveal key assumptions, causes, effects, or
patterns

30. PieChart
•Apiechart(oracirclechart)maybeacircular
statisticalgraphic,whichissplitintoslices,for
instance,numericalproportion.
•In a pie chart, the arc length of every slice (and
consequently its central angle and area), is
proportionaltothenumberitrepresents.
•To draw a pie chart you have to select minimum
two attributes( one in row and one in column) by
draganddropthenselectthechartoptionaspie.

31. Advantages
•The pictureissimpleand easy-to-understand
•Data canbe representedvisuallyas a fractionalpartofa
whole
•It helps in providing an effective communication tool for
theevenuninformedaudience
•Provides a data comparison for the audience at a glance
to give an immediate analysis or to quickly understand
information
•No need for readers to examine or measure underlying
numbers themselves, which can be removed by using this
chart
•To emphasize a few points you want to make, you can
manipulatepieces ofdatainthepiechart

32. Disadvantages
•It becomes less effective, if there are too many
piecesof datatouse
•If there are too many pieces of data. Even if you add
data labels and numbers may not help here, they
themselvesmaybecomecrowdedandhardtoread
•As this chart only represents one data set, you need
a seriesto comparemultiple sets
•This may make it more difficult for readers when it
comestoanalyzeandassimilateinformationquickly

33. BubbleChart
•ABubbleChartisamulti-variablegraphthatisacross
betweenaScatterplotandaProportionalAreaChart.
•LikeaScatterplot,BubbleChartsuseaCartesian
coordinatesystemtoplotpointsalongagridwheretheX
andYaxisareseparatevariables.
•However.unlikeaScatterplot,eachpointisassigneda
labelorcategory(eitherdisplayedalongsideorona
legend).
•Eachplottedpointthenrepresentsathirdvariablebythe
areaofitscircle.
•Colourscanalsobeusedtodistinguishbetween
categoriesorusedtorepresentanadditionaldata
variable.
•Timecanbeshowneitherbyhavingitasavariableon
oneoftheaxisorbyanimatingthedatavariables
changingovertime.

35. •Bubble Charts are typically used to compare and
showtherelationshipsbetweencategorisedcircles,
by the use of positioning and proportions. The
overall picture of Bubble Charts can be used to
analyse for patterns/correlations.
•Likewith ProportionalArea Charts,thesizesofthe
circles needtobedrawn basedonthe circle’sarea,
not its radius or diameter. Not only will the size of
thecircleschange exponentially,but thiswilllead to
misinterpretations by thehuman visualsystem.

36. Advantages
•displaythreevariableswithoutusing3Dgraphs
•visual size makes it very easy to make relative
comparisons
•Conveyssameinformationasalinegraph

37. Disadvantages
•due to circle sizes, can be difficult to ascertain
actualvalues
•difficulttoreadandunderstand
•cannotbeusedtodisplayalotofdata.

38. Uses
•Bubble charts are often used in business to
visualizetherelationshipsbetweenalternatives
investment in dimensions such as cost, value,
andrisk.

39. Highlightedtable
•The highlighttable allows usto use conditional formatting
toaviewofanormaltable.
•Tableauwill automatically applya color schemein either
endless or stepped array of colors from highestto lowest.
•It’s greatforcomparinga field’svalueswithin a row or
column.
•Use highlighttablestomatchcategorical data usingcolor.
•Highlighttablesare oftenoutstandingas theyhelp users
quicklyspot the foremost interestingvaluesduringatable
ofnumbers.
•Incaseswheremultiplevariablesaredisplayed,Measure
Valuesareoftenusedtoapplyonecolorschemetoallor
anyofthevaluesinatable.
•Unlessthevariablesareverysimilar,highlightingmultiple
variablesistypicallynothelpfulandcanmislead.

41. Scatter Plot
The scatter plot is used to visualize the
relationship between the two measures.
It is designed by adding measures in both x-axis
and y-axis.
This can show the trend or relationship between
the measures selected.
To create a scatter plot, you should have at least
one measure in the rows shelf and one measure in
the columns shelf.
However, you can add the dimensions field to the
scatter plot that plays a role of different color
making for already existing points in the scatter
graph.

43. Advantages of Scatter Plot
•Show a relationship and a trend in the data
relationship.
•Show all data points, including minimum and
maximum and outliers.
•Can highlight correlations.
•Retains the exact data values and sample size.
•Shows both positive and negative type of
graphical correlation.

44. Disadvantages of scatter plot
•Flat best-fit line gives inconclusive results.
•Interpretation can be subjective.
•Correlation does not mean and not show
causation.
•Data on both axes have to be continuous data
(see our postdiscrete vs continuous data).
•You cannot use Scatter diagrams to show the
relation of more than two variables.