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Analyzing Data in Tableau

Combiner les calculs de table rapides et les caractéristiques

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Cet exercice fait partie du cours

Analyzing Data in Tableau

DébutantNiveau de compétence

4.9+

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Learn best practices for organizing fields into dimensions and measures and how to configure date and time fields for trend analysis. All the while, you’ll use Calculated Fields, quick table calculations, and highlight actions to elevate your visualizations and reveal the hidden insights.

Exercise 1: Data preparation Exercise 2: Non-aggregating numerical dimensions Exercise 3: Preparing the data Exercise 4: Measures and calculated fields Exercise 5: Highlighter Exercise 6: Calculated Fields to extend data Exercise 7: DATEDIFF function Exercise 8: Weekday or weekend?Exercise 9: Highlighter for multiple rows Exercise 10: Visualizations for exploratory analysis of trends Exercise 11: Discrete or continuous time analysis?Exercise 12: Discrete time analysis and Quick Table Calculations Exercise 13: Low usage trends Exercise 14: Weekly pattern by user type Exercise 15: Slicing and dicing Exercise 16: Weekday trends by gender and time of day Exercise 17: Bubble chart Exercise 18: Scheduling a promotional discount

In this chapter, you’ll get to know more about Divvy users. Learning about them provides valuable information toward successful and continued engagement. To do this, you’ll build bar charts, KPI charts, and histograms with variable bin width. You’ll create additional insights by adding filters to your visualizations.

Exercise 1: Who are your customers?Exercise 2: Missing information Exercise 3: Exploring user data Exercise 4: Getting to know the Divvy users Exercise 5: Adding filters Exercise 6: Building a KPI dashboard Exercise 7: Formatting user KPIs Exercise 8: Creating a user dashboard Exercise 9: Enhancing the dashboard Exercise 10: The distribution of users Exercise 11: Choosing the right chart type Exercise 12: Visualizing distributions Exercise 13: Using a histogram Exercise 14: Using a line chart Exercise 15: Working with bins Exercise 16: Customizing the bin size Exercise 17: Exploring day pass user age

Dive deeper into maps and elevate geographic data with layers and density. You'll customize Tooltips and learn how to embed them with more information. You'll also learn more about Quick Table Calculations and apply them to your visualizations.

Exercise 1: Cartographie interactive de l’activité client dans le temps Exercise 2: Activité client en mouvement Exercise 3: Visualiser la concentration d’activité Exercise 4: Le nouveau type de repère de densité de Tableau Exercise 5: Superposition et utilisation totale des quais Exercise 6: Découper la carte Exercise 7: Améliorer la carte Exercise 8: Superposition des cartes Exercise 9: Calculs rapides de table pour le classement Exercise 10: Qu’est-ce qui va où ?Exercise 11: Combiner les calculs de table rapides et les caractéristiques

Exercice en cours

Exercise 12: Ajouter des détails sans bruit Exercise 13: Décomposer par type de client Exercise 14: KPI et segments temporels Exercise 15: Visualiser les KPI des stations Exercise 16: Des bulles aux donuts Exercise 17: Analyser nos résultats

Expand your Tableau toolbox with groups, sets, and parameters. You'll create groups using Lasso selection and Calculated Fields. You'll also use Parameters to enable users to dynamically input changes to your visualizations. Finally, you'll create Sets and compare your findings to an external weather data source.

Exercise 1: Groups Exercise 2: Groups for regions Exercise 3: Lasso selection Exercise 4: Editing groups Exercise 5: Creating seasonal groups Exercise 6: Seasons Exercise 7: Ranking dashboard Exercise 8: Embedding in the tooltip Exercise 9: Parameters and sets Exercise 10: Parameter characteristics Exercise 11: Parameters Exercise 12: Replacing rank with a parameter Exercise 13: Color by parameter Exercise 14: Sets Exercise 15: Set for windiness Exercise 16: Divvy trips and windiness Exercise 17: Divvy trips and temperature