Trends analyseren per weekdag

Tijdreeksanalyse gebeurt op verschillende datum-niveaus: jaar, maand, week, dag. Het omvat ook het beoordelen van trends in context afgeleid van datum, zoals de dag van de week.

In deze oefening gebruik je de functie WEEKDAY() om een nieuwe variabele voor de dag van de week te maken, en analyseer je vervolgens trends per stad met small multiples.

Als je voortgang kwijt bent, sluit dan alle open rapporten en laad 1_2_weekday.pbix uit de map Workbooks op het bureaublad.

Deze oefening maakt deel uit van de cursus

Trendanalyse in Power BI

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Deze oefening maakt deel uit van de cursus

Trendanalyse in Power BI

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In this chapter, you’ll get more familiar with time-based variables and the multiple ways to extract further variables using EDA for analysis—like day of week and time difference. You’ll get hands-on with Power BI as you build line charts to calculate new metrics and uncover trends hiding in your data—including period-over-period change and rolling averages.

Exercise 1: Beginnen met tijdreeksgegevens verkennen Exercise 2: Uitspraken over tijdreeksanalyse Exercise 3: Airbnb-tijdreeksen verkennen Exercise 4: Een nieuwe datumvariabele genereren Exercise 5: Trends analyseren per weekdag

Huidige oefening

Exercise 6: Jaar-op-jaarverandering berekenen

Exercise 1: Time series run chart Exercise 2: Cyclical vs. Seasonal Exercise 3: Analyzing Glassdoor reviews over time Exercise 4: Building your first time series chart Exercise 5: Calculating rolling averages Exercise 6: Finding anomalies in time series data

One of the most powerful functions of EDA in Power BI is being able to identify which variables have the most influence on your target outcome. A native Power BI visualization tool enabling that is Decomposition Trees. You'll learn about Decomposition Trees, how to construct, then interpret in order to explain a target outcome by other variables.

Exercise 1: Introduction to decomposition trees Exercise 2: Statements about decomposition trees Exercise 3: Using decomposition trees in Power BI Exercise 4: Getting started with decomposition trees Exercise 5: Diving deeper into decomposition trees Exercise 6: Decomposing attrition rates

In this chapter you'll build another native Power BI tool, Key Influencers visual. It helps you to understand how much a target outcome changes based on specific variables and segments of observations.

Exercise 1: Introduction to key influencers Exercise 2: Decomposition trees vs. key influencers Exercise 3: Using key influencers in Power BI Exercise 4: Key influencers for average listing price Exercise 5: Segments from key influencers Exercise 6: Key influencers for super hosts Exercise 7: Congratulations!