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.

Starting to explore time series data

Statements about time series analysis

Exploring AirBnB time series

Generating a new date variable

Analyzing trends by day of week

Calculating year-over-year change

Exploring Time Series Data

Time series run chart

Cyclical vs. Seasonal

Analyzing Glassdoor reviews over time

Building your first time series chart

Calculating rolling averages

Finding anomalies in time series data

Analyzing Time Series in Power BI

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. 

Introductie tot decomposition trees

Uitspraken over decomposition trees

Decomposition trees gebruiken in Power BI

Aan de slag met decomposition trees

Dieper duiken in decomposition trees

Attritionpercentages ontleden

Decomposition Trees

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.

Introduction to key influencers

Decomposition trees vs. key influencers

Using key influencers in Power BI

Key influencers for average listing price

Segments from key influencers

Key influencers for super hosts

Congratulations!

Key Influencers

Exercises and Datasets

DataCamp vs. Local Experience

Course Glossary

In deze cursus leer je tijdreeksen analyseren, je gegevens visualiseren en trends spotten. Je bouwt nieuwe datumvariabelen, ontdekt run charts en gaat aan de slag met het berekenen van voortschrijdende gemiddelden. Tot slot leer je met de decomposition trees en key influencers van Power BI vast te stellen welke variabelen de grootste invloed hebben op de doelvariabele.

De begrippenlijst van de cursus vind je rechts in de sectie met bronnen.

Om CPE-credits te behalen, moet je de cursus afronden en minimaal 70% scoren op de gekwalificeerde toets. Je gaat naar de toets via de CPE-creditsvermelding aan de rechterkant.

<h2>Geen tijdreeksgegevens</h2>

  

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Exploratory Data Analysis in Power BI

Verbeter je rapporten met trendanalyses zoals tijdreeksen, decompositiebomen en belangrijke invloeden.

Trendanalyse in Power BI

Verbeter uw rapporten met trendanalysetechnieken zoals tijdreeksen, decompositiebomen en belangrijkste beïnvloeders.

Data-analist in Power BI

Uitspraken over decomposition trees

Trendanalyse in Power BI

Interactieve oefening met praktijkervaring